\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6129",leadTitle:null,fullTitle:"Kalman Filters - Theory for Advanced Applications",title:"Kalman Filters",subtitle:"Theory for Advanced Applications",reviewType:"peer-reviewed",abstract:"This book presents recent issues on theory and practice of Kalman filters, with a comprehensive treatment of a selected number of concepts, techniques, and advanced applications. From an interdisciplinary point of view, the contents from each chapter bring together an international scientific community to discuss the state of the art on Kalman filter-based methodologies for adaptive/distributed filtering, optimal estimation, dynamic prediction, nonstationarity, robot navigation, global navigation satellite systems, moving object tracking, optical communication systems, and active power filters, among others. The theoretical and methodological foundations combined with extensive experimental explanation make this book a reference suitable for students, practicing engineers, and researchers in sciences and engineering.",isbn:"978-953-51-3828-0",printIsbn:"978-953-51-3827-3",pdfIsbn:"978-953-51-4038-2",doi:"10.5772/intechopen.68249",price:139,priceEur:155,priceUsd:179,slug:"kalman-filters-theory-for-advanced-applications",numberOfPages:314,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"7341ee991eeb22a10a8b14bcd53a24b6",bookSignature:"Ginalber Luiz de Oliveira Serra",publishedDate:"February 21st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6129.jpg",numberOfDownloads:21632,numberOfWosCitations:30,numberOfCrossrefCitations:39,numberOfCrossrefCitationsByBook:7,numberOfDimensionsCitations:54,numberOfDimensionsCitationsByBook:8,hasAltmetrics:0,numberOfTotalCitations:123,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 18th 2017",dateEndSecondStepPublish:"May 9th 2017",dateEndThirdStepPublish:"August 5th 2017",dateEndFourthStepPublish:"November 3rd 2017",dateEndFifthStepPublish:"January 2nd 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"11032",title:"Dr.",name:"Ginalber Luiz",middleName:"De Oliveira",surname:"Serra",slug:"ginalber-luiz-serra",fullName:"Ginalber Luiz Serra",profilePictureURL:"https://mts.intechopen.com/storage/users/11032/images/3436_n.jpg",biography:"Dr. Ginalber L. O. Serra was born in São Luis, Maranhão, Brazil, in 1976. He received the B.Sc. and M. Sc. degrees in electrical engineering from Federal University of Maranhão, Maranhão, Brazil, in 1999 and 2001, respectively. He received the Ph.D. degree in electrical engineering from State University of Campinas (UNICAMP), Campinas, Brazil, on September 2005. He has finished his Postdoctoral research on multivariable neuro-fuzzy adaptive control of nonlinear systems at the Department of Machines, Components and Intelligent Systems, State University of Campinas, Campinas, Brazil, on September 2006 and he has served as researcher with the Department of Electrical Engineering at the University of Santiago, Santiago, Chile, in 2007. Currently, Dr. Serra is with the Department of ElectroElectronics at Federl Institute of Education, Cience and Technology, São Luis, Maranhão, Brazil. Dr. Serra has served as Reviewer for many prestigious international journals, including the IEEE TRANSACTIONS ON FUZZY SYSTEMS, IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS - PART B: CYBERNETICS, IEE CONTROL THEORY AND APPLICATIONS, and for papers from {\\\\em IEEE Conferences}. His research interests include fuzzy systems, neural networks and genetic algorithms applied to identification and control of nonlinear systems.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Instituto Federal do Maranhão",institutionURL:null,country:{name:"Brazil"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1401",title:"Control Theory",slug:"applied-mathematics-control-theory"}],chapters:[{id:"58317",title:"A Reference Recursive Recipe for Tuning the Statistics of the Kalman Filter",doi:"10.5772/intechopen.71961",slug:"a-reference-recursive-recipe-for-tuning-the-statistics-of-the-kalman-filter",totalDownloads:1478,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The philosophy and the historical development of Kalman filter from ancient times to the present is followed by the connection between randomness, probability, statistics, random process, estimation theory, and the Kalman filter. A brief derivation of the filter is followed by its appreciation, aesthetics, beauty, truth, perspectives, competence, and variants. The menacing and notorious problem of specifying the filter initial state, measurement, and process noise covariances and the unknown parameters remains in the filter even after more than five decades of enormous applications in science and technology. Manual approaches are not general and the adaptive ones are difficult. The proposed reference recursive recipe (RRR) is simple and general. The initial state covariance is the probability matching prior between the Frequentist approach via optimization and the Bayesian filtering. The filter updates the above statistics after every pass through the data to reach statistical equilibrium within a few passes without any optimization. Further many proposed cost functions help to compare the present and earlier approaches. The efficacy of the present RRR is demonstrated by its application to a simulated spring, mass, and damper system and a real airplane flight data having a larger number of unknown parameters and statistics.",signatures:"Mudambi R Ananthasayanam",downloadPdfUrl:"/chapter/pdf-download/58317",previewPdfUrl:"/chapter/pdf-preview/58317",authors:[{id:"209685",title:"Prof.",name:"Mudambi",surname:"Ananthasayanam",slug:"mudambi-ananthasayanam",fullName:"Mudambi Ananthasayanam"}],corrections:null},{id:"58052",title:"The Error Covariance Matrix Inflation in Ensemble Kalman Filter",doi:"10.5772/intechopen.71960",slug:"the-error-covariance-matrix-inflation-in-ensemble-kalman-filter",totalDownloads:1308,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The estimation accuracy of ensemble forecast errors is crucial to the assimilation results for all ensemble-based schemes. The ensemble Kalman filter (EnKF) is a widely used scheme in land surface data assimilation, without using the adjoint of a dynamical model. In EnKF, the forecast error covariance matrix is estimated as the sampling covariance matrix of the ensemble forecast states. However, past researches on EnKF have found that it can generally lead to an underestimate of the forecast error covariance matrix, due to the limited ensemble size, as well as the poor initial perturbations and model error. This can eventually result in filter divergence. Therefore, using inflation to further adjust the forecast error covariance matrix becomes increasingly important. In this chapter, a new structure of the forecast error covariance matrix is proposed to mitigate the problems with limited ensemble size and model error. An adaptive procedure equipped with a second-order least squares method is applied to estimate the inflation factors of forecast and observational error covariance matrices. The proposed method is tested on the well-known atmosphere-like Lorenz-96 model with spatially correlated observational systems. The experiment results show that the new structure of the forecast error covariance matrix and the adaptive estimation procedure lead to improvement of the analysis states.",signatures:"Guocan Wu and Xiaogu Zheng",downloadPdfUrl:"/chapter/pdf-download/58052",previewPdfUrl:"/chapter/pdf-preview/58052",authors:[{id:"210688",title:"Dr.",name:"Guocan",surname:"Wu",slug:"guocan-wu",fullName:"Guocan Wu"}],corrections:null},{id:"57977",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",doi:"10.5772/intechopen.71900",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1980,totalCrossrefCites:11,totalDimensionsCites:12,hasAltmetrics:0,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",downloadPdfUrl:"/chapter/pdf-download/57977",previewPdfUrl:"/chapter/pdf-preview/57977",authors:[{id:"122290",title:"Dr.",name:"Tobias",surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}],corrections:null},{id:"58292",title:"Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems",doi:"10.5772/intechopen.72470",slug:"sensitivity-based-adaptive-srukf-for-state-parameter-and-covariance-estimation-on-mechatronic-system",totalDownloads:1365,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint state and parameter UKF.",signatures:"Mauro Hernán Riva, Mark Wielitzka and Tobias Ortmaier",downloadPdfUrl:"/chapter/pdf-download/58292",previewPdfUrl:"/chapter/pdf-preview/58292",authors:[{id:"122290",title:"Dr.",name:"Tobias",surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"210832",title:"M.Sc.",name:"Mauro",surname:"Riva",slug:"mauro-riva",fullName:"Mauro Riva"}],corrections:null},{id:"57768",title:"Kalman Filters for Parameter Estimation of Nonstationary Signals",doi:"10.5772/intechopen.71874",slug:"kalman-filters-for-parameter-estimation-of-nonstationary-signals",totalDownloads:1556,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.",signatures:"Sarita Nanda",downloadPdfUrl:"/chapter/pdf-download/57768",previewPdfUrl:"/chapter/pdf-preview/57768",authors:[{id:"209587",title:"Dr.",name:"Sarita",surname:"Nanda",slug:"sarita-nanda",fullName:"Sarita Nanda"}],corrections:null},{id:"57455",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",doi:"10.5772/intechopen.71205",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1212,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",downloadPdfUrl:"/chapter/pdf-download/57455",previewPdfUrl:"/chapter/pdf-preview/57455",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}],corrections:null},{id:"57512",title:"Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress",doi:"10.5772/intechopen.71616",slug:"application-of-kalman-filtering-in-dynamic-prediction-for-corporate-financial-distress",totalDownloads:1398,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter aims to dynamically improve the method of predicting financial distress based on Kalman filtering. Financial distress prediction (FDP) is an important study area of corporate finance. The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the state-space method, we establish two models that are used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and further, a general n-step-ahead prediction algorithm based on Kalman filtering is derived for prospective prediction. We also conduct an empirical study for China’s manufacturing industry, and the results have proved the accuracy and advance of predicting financial distress in such case.",signatures:"Qian Zhuang",downloadPdfUrl:"/chapter/pdf-download/57512",previewPdfUrl:"/chapter/pdf-preview/57512",authors:[{id:"209794",title:"Dr.",name:"Qian",surname:"Zhuang",slug:"qian-zhuang",fullName:"Qian Zhuang"}],corrections:null},{id:"57651",title:"Predicting Collisions in Mobile Robot Navigation by Kalman Filter",doi:"10.5772/intechopen.71653",slug:"predicting-collisions-in-mobile-robot-navigation-by-kalman-filter",totalDownloads:1126,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The growing trend of the use of robots in many areas of daily life makes it necessary to search for approaches to improve efficiency in tasks performed by robots. For that reason, we show, in this chapter, the application of the Kalman filter applied to the navigation of mobile robots, specifically the Time-to-contact (TTC) problem. We present a summary of approaches that have been taken to address the TTC problem. We use a monocular vision-based approach to detect potential obstacles and follow them over time through their apparent size change. Our approach collects information about obstacle data and models the behavior while the robot is approaching the obstacle, in order to predict collisions. We highlight some characteristics of the Kalman filter applied to our problem. Finally, we show of our results applied to sequences composed of 210 frames in different real scenarios. The results show a fast convergence of the model to the data and good fit even with noisy measures.",signatures:"Angel Sánchez, Homero Ríos, Gustavo Quintana and Antonio Marín",downloadPdfUrl:"/chapter/pdf-download/57651",previewPdfUrl:"/chapter/pdf-preview/57651",authors:[{id:"210685",title:"MSc.",name:"Angel",surname:"Sanchez Garcia",slug:"angel-sanchez-garcia",fullName:"Angel Sanchez Garcia"},{id:"211071",title:"Dr.",name:"Homero",surname:"Rios Figueroa",slug:"homero-rios-figueroa",fullName:"Homero Rios Figueroa"},{id:"211072",title:"Dr.",name:"Antonio",surname:"Marin Hernandez",slug:"antonio-marin-hernandez",fullName:"Antonio Marin Hernandez"},{id:"219509",title:"Dr.",name:"Gustavo",surname:"Quintana Carapia",slug:"gustavo-quintana-carapia",fullName:"Gustavo Quintana Carapia"}],corrections:null},{id:"59091",title:"Efficient Matrix-Free Ensemble Kalman Filter Implementations: Accounting for Localization",doi:"10.5772/intechopen.72465",slug:"efficient-matrix-free-ensemble-kalman-filter-implementations-accounting-for-localization",totalDownloads:1128,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter discusses efficient and practical matrix-free implementations of the ensemble Kalman filter (EnKF) in order to account for localization during the assimilation of observations. In the EnKF context, an ensemble of model realizations is utilized in order to estimate the moments of its underlying error distribution. Since ensemble members come at high computational costs (owing to current operational model resolutions) ensemble sizes are constrained by the hundreds while, typically, their error distributions range in the order of millions. This induces spurious correlations in estimates of prior error correlations when these are approximated via the ensemble covariance matrix. Localization methods are commonly utilized in order to counteract this effect. EnKF implementations in this context are based on a modified Cholesky decomposition. Different flavours of Cholesky-based filters are discussed in this chapter. Furthermore, the computational effort in all formulations is linear with regard to model resolutions. Experimental tests are performed making use of the Lorenz 96 model. The results reveal that, in terms of root-mean-square-errors, all formulations perform equivalently.",signatures:"Elias David Niño Ruiz, Rolando Beltrán Arrieta and Alfonso Manuel\nMancilla Herrera",downloadPdfUrl:"/chapter/pdf-download/59091",previewPdfUrl:"/chapter/pdf-preview/59091",authors:[{id:"107082",title:"Prof.",name:"Elias",surname:"Nino-Ruiz",slug:"elias-nino-ruiz",fullName:"Elias Nino-Ruiz"}],corrections:null},{id:"58238",title:"Kalman Filters for Reference Current Generation in Shunt Active Power Filter (APF)",doi:"10.5772/intechopen.72467",slug:"kalman-filters-for-reference-current-generation-in-shunt-active-power-filter-apf-",totalDownloads:1669,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Shunt active power filter (APF) method have been used by many researchers as a solution in reducing the harmonics creating by the non-liner loads. Therefore, this research is targeted to design and implement a three-phase shunt APF employing Kalman filter estimator. Conventionally, low-pass filter (LPF) is used to filter out the unwanted DC component of the non-linear load to produce the sinusoidal waveform called the reference current. However, when applying LPF it contributes with the phase shift and high transient at the supply current. Therefore, to reduce these problems, the digital Kalman filter estimator is used to replace the LPF for generating the reference current. Details on the investigation between conventional and proposed methods under simulation based on Matlab Simulink platform and experimental that are made for two types of load, namely, three-phase rectifier with RC-load and three-phase induction motor, are presented. The performance criteria of the shunt APF are determined by the supply current waveform, total harmonic distortion (THD), harmonic spectrum and power quality measurements, which were also obtained by simulation and experimental. In conclusion, by employing Kalman filter estimator for generating the reference current, it reduces the time delay and high transient current at the power supply and, thus, improved the overall THD from 0.1 to 0.42% compared to the LPF.",signatures:"Ahmad Shukri Bin Abu Hasim, Syed Mohd Fairuz Bin Syed Mohd\nDardin and Zulkifilie Bin Ibrahim",downloadPdfUrl:"/chapter/pdf-download/58238",previewPdfUrl:"/chapter/pdf-preview/58238",authors:[{id:"208972",title:"Dr.",name:"Syed Mohd Fairuz",surname:"Syed Mohd Dardin",slug:"syed-mohd-fairuz-syed-mohd-dardin",fullName:"Syed Mohd Fairuz Syed Mohd Dardin"},{id:"209475",title:"Dr.",name:"Ahmad Shukri",surname:"Abu Hasim",slug:"ahmad-shukri-abu-hasim",fullName:"Ahmad Shukri Abu Hasim"},{id:"218276",title:"Prof.",name:"Zulkifilie",surname:"Ibrahim",slug:"zulkifilie-ibrahim",fullName:"Zulkifilie Ibrahim"}],corrections:null},{id:"57692",title:"Applications of Kalman Filters for Coherent Optical Communication Systems",doi:"10.5772/intechopen.71617",slug:"applications-of-kalman-filters-for-coherent-optical-communication-systems",totalDownloads:1496,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we review various applications of Kalman filtering for coherent optical communication systems. First, we briefly discuss the principles of Kalman filter and its variations including extended Kalman filter (EKF) and adaptive Kalman filter (AKF). Later on, we illustrate the applicability of Kalman filters for joint tracking of several optical transmission impairments, simultaneously, by formulating the state space model (SSM) and detailing the principles. A detailed methodology is presented for the joint tracking of linear and nonlinear phase noise along with amplitude noise using EKF. Also, approaches to enhance the performance obtained by EKF by combining with other existing digital signal processing (DSP) techniques are presented. Frequency and phase offset estimation using a two stage linear Kalman filter (LKF)/EKF is also discussed. A cascaded structure of LKF and EKF by splitting the SSM to jointly mitigate the effects of polarization, phase and amplitude noise is also presented. The numerical analysis concludes that the Kalman filter based approaches outperform the conventional methods with better tracking capability and faster convergence besides offering more feasibility for real-time implementations.",signatures:"Lalitha Pakala and Bernhard Schmauss",downloadPdfUrl:"/chapter/pdf-download/57692",previewPdfUrl:"/chapter/pdf-preview/57692",authors:[{id:"210340",title:"M.Sc.",name:"Lalitha",surname:"Pakala",slug:"lalitha-pakala",fullName:"Lalitha Pakala"},{id:"210654",title:"Prof.",name:"Bernhard",surname:"Schmauss",slug:"bernhard-schmauss",fullName:"Bernhard Schmauss"}],corrections:null},{id:"57673",title:"Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design",doi:"10.5772/intechopen.71731",slug:"kalman-filter-for-moving-object-tracking-performance-analysis-and-filter-design",totalDownloads:3160,totalCrossrefCites:14,totalDimensionsCites:22,hasAltmetrics:0,abstract:"This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",signatures:"Kenshi Saho",downloadPdfUrl:"/chapter/pdf-download/57673",previewPdfUrl:"/chapter/pdf-preview/57673",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}],corrections:null},{id:"57985",title:"Distributed Kalman Filter",doi:"10.5772/intechopen.71941",slug:"distributed-kalman-filter",totalDownloads:1350,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The continuing trend toward connected sensors (“internet of things” and” ubiquitous computing”) drives a demand for powerful distributed estimation methodologies. In tracking applications, the distributed Kalman filter (DKF) provides an optimal solution under Kalman filter conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm, which receives all associated measurements. However, the centralized approach requires full communication of all measurements at each time step, whereas the DKF works at arbitrary communication rates since the calculation is fully distributed. A more recent methodology is based on ”accumulated state density” (ASD), which augments the states from multiple time instants to overcome spatial cross-correlations. This chapter explains the challenges in distributed tracking. Then, possible solutions are derived, which include the DKF and ASD approach.",signatures:"Felix Govaers",downloadPdfUrl:"/chapter/pdf-download/57985",previewPdfUrl:"/chapter/pdf-preview/57985",authors:[{id:"209490",title:"Dr.",name:"Felix",surname:"Govaers",slug:"felix-govaers",fullName:"Felix Govaers"}],corrections:null},{id:"57804",title:"Consensus-Based Distributed Filtering for GNSS",doi:"10.5772/intechopen.71138",slug:"consensus-based-distributed-filtering-for-gnss",totalDownloads:1406,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Kalman filtering in its distributed information form is reviewed and applied to a network of receivers tracking Global Navigation Satellite Systems (GNSS). We show, by employing consensus-based data-fusion rules between GNSS receivers, how the consensus-based Kalman filter (CKF) of individual receivers can deliver GNSS parameter solutions that have a comparable precision performance as their network-derived, fusion center dependent counterparts. This is relevant as in the near future the proliferation of low-cost receivers will give rise to a significant increase in the number of GNSS users. With the CKF or other distributed filtering techniques, GNSS users can therefore achieve high-precision solutions without the need of relying on a centralized computing center.",signatures:"Amir Khodabandeh, Peter J.G. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70753",title:"Introductory Chapter: Mass Production and Industry 4.0",doi:"10.5772/intechopen.90874",slug:"introductory-chapter-mass-production-and-industry-4-0",body:'\nWhen it comes to the history of mass production enterprises, the revolutionary developments in mass production come to mind from the past to the present. To be able to better adapt to today’s industrial revolution, of course it is necessary to understand the past industrial revolutions. On the basis of industrial revolutions, each of which is more or less rooted in a technological and cultural basis, there was always an effort to achieve better and faster solutions. Of course, economic concerns have always been taken into consideration. Industry 4.0 is a target of the research policy of the German government. Today, in the focus of integrated mass production, systems benefit from the advantages of this novel industrial revolution.
\nSince it is a great way to meet the large-scale demand of most products, mass production is used in many industries which are big and/or small. For instance, automobiles, computers, and cellular phones are the typical examples of mass production products. A high demand rate for a product is the main specification of mass production. The manufacturing area is typically dedicated to the production of a single type product and/or its variations.
\nTypical or conventional manufacturing methods can be adapted as mass production lines which are machining, casting, joining, and forming or plastic deformation. Each of them has its principles, manufacturing parameters, application areas, methods, and technologies to be considered in detail. It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge and hard experience and the required related tools as well. The use of modern methods and techniques to produce large quantity products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, many works aim to reflect advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, there are many works that focus on mass production processes designed according to Industry 4.0 considering different kinds of advanced quality and improvement research in mass production systems for high productive and sustainable manufacturing [1, 2]. This chapter gives general information about the components of a conventional mass production system and an Industrial 4.0-adapted mass production system with their individual advantages.
\nConventional mass production processes may be also called as continuous production that involves the fabrication of a known part in a specific production way and shape, in a consistent manner. In the mass production area, there are typical manufacturing processes dedicated to the production of a single type product and/or its variations. However, there are lots of benefits of mass production including decreased labor, decreased time in manufacturing, increased output, and lower cost per unit [3]. Besides, there are many components of a conventional production system which need to be considered in detail. The machining lathe, the processing tool, the processed material, the process parameters, and others directly affect the quality of the product. One of the main disadvantages of the conventional manufacturing systems is being not very flexible systems. It is usually difficult to adapt the production line to a different kind of process. Conventional manufacturing systems require close inspection to control the process parameters which are in a close relationship with the quality of the product. With the help of related quality control methods, the required quality works can be reached in conventional mass production systems.
\nToday, some Industry 4.0-adapted factories are called as “smart.” A “smart factory” has a highly flexible production system, which is capable of producing single individual parts with high precision and better quality in an economically efficient way. Additionally, a component-driven logistic system is required to achieve this task besides high flexible production systems and processes. In order to meet the requirements of the hard manufacturing task, digitalization of the systems and sub-systems is also essential. Calling a factory as smart requires at least following the supporting systems of the last industrial revolution [4].
\nA “cyber-physical system” is a physical object or a process that is connected and interacting with a digital representation of that object or process. This is one of the key tools supporting the development of smart factories. The definition of cyber-physical system includes a permanent digital interaction of the object from the physical world and the virtual representation. A permanent flow of data and information between both is the core of the cyber-physical system definition. One of the most important steps toward a functional cyber-physical system and a challenge today is to digitize and network non-digital machines and processes.
\n“The Internet of Things” is a system that supplies an ability to transfer data over a network. Cyber-physical system is enables every device and even every sensor and actor in a production or logistic system to communicate with each other over a common digital network. According to the vision of a smart factory, it is not only internally digitally connected but also with the external supply chain for the product to be produced. In a networked supply chain, smart factories have a network system of hundreds or thousands of cyber-physical systems. They are connected to a common exchanging data and information Ethernet network.
\n“Component-driven production” has been formulated to control the process chain of a product inside of the production. To achieve this, components need to carry their construction plans and other information for manufacturing. In this way the components are taking individual paths toward the production plant without complex planning. Of course, to plan the production future of a component requires knowing the past of that part in detail.
\n“Big Data analytics” is an inevitable tool of an Industry 4.0. It was always hard to analyze the data than to collect it. Additionally we are talking about diverse and larger data than being in the past. A smart factory must have advantages of some analytical techniques against to process that kind of large and diverse data. The data can be supplied from different sources and sizes and be a structured, semi-structured, or unstructured type. With those data-driven solutions, the processed high-quality data can be used at each step of the system even in the complex systems.
\n“Flexible manufacturing systems”: As being discussed at the second section, one of the main disadvantages of the conventional manufacturing systems is being not very flexible systems. This disadvantage of the conventional manufacturing systems could be eliminated by the flexible manufacturing systems of Industry 4.0. One of the most important tasks of Industry 4.0 is to realize a highly flexible production system. The system is usually capable to produce with small lot sizes. The smart factory has to deal with smaller lot sizes and an increasing number of changeover processes during the day-to-day work. Therefore, equipment and labor requirements are prepared in order to cope with the flexibility requirements of the process not only for the present times but also for the possible needs in the future [5].
\nIn competitive market conditions of manufacturing, the enterprises should produce high-quality products within productive manufacturing processes. Mass production requires standardized processes for manufacturing of interchangeable parts in large quantities at comparable prices. In fact, it is a hard work which requires many components to be considered in great detail. The use of modern methods and techniques of mass production provides decreases in the manufacturing costs and improvements in product quality. Manufacturers are trying to survive and/or to take share in hard global market conditions by using such these advantages. With the associated advanced technologies of Industry 4.0 such as cyber-physical systems and Internet of Things, mass production has been revolutionized, but it looks like it will always have issues like quality control of the production process.
\nIn the civil aviation scenario, the pandemic affected the economic circumstances (…) showing the need to reevaluate the demand projections of passengers, aircraft, and load [1]. The efforts of the civil aviation sector are to seek safety guidelines, to intensify the sanitation of surfaces, the use of masks and alcohol gel, frequent hand cleaning with soap/water, the practice respiratory etiquette, and proper ventilation, to keep the environment decontaminated and the ventilation controlled in order to reduce the contagion in airports and planes. Since environmental quality in civil aviation is facing COVID-19 it is necessary to seek comfortable resources in the ventilation itself in an attempt to solve or soften the discomfort during the cruise. The incipient individualization of passengers’ thermal comfort is one of the biggest problems faced by companies in the aviation sector. Exposed, multiple times, to considerable variation of thermal sources and different temperatures of asymmetric airspeed fields, passengers suffer the consequences of neglect with solutions that seek the individual space optimization without compromising the comfort provided in aircraft’s cabins. To evaluate the thermal comfort, one must know the environmental comfort criteria to the relevant thermal environment parameters, along with the methods to its prediction (project phase) or measurement (commissioning and operation phase). From this basic premise, we need to: (a) define which are the main internal climatic parameters of temperature and asymmetric airspeed due to the cabin geometry projected with pitch,1 and what is the restricted average width between the armchairs reducing the space between the passengers; (b) quantify its influence on the passengers, and (c) discern the plane and HVAC system in these parameters.
A large number of researches about thermal comfort was written and published. This extensive research literature was written in international standards [2, 3, 4, 5, 6, 7, 8], with the intent to guide the aeronautical project professionals to project and maintain the internal thermal environment comfortable, being internationally known as indoor climate or indoor air.
In this chapter, we research the thermal comfort and the adaptive comfort standard (ACS) based on variable climatic expectations that shift the locus of thermal regulatory responsibility to the environment of commercial and passengers transport aircraft, and back again to the airplane cabin occupants. The occupants are obligated to become way more active or interactive with the airplane internal cabin to implement the adaptation opportunities offered by the plane to create an acceptable indoor climate for passengers. After elaborating the methodological differences between these two perspectives about the person-environment relationship, the chapter examines the implications of standards and practices of thermal comfort using the computational simulation tool CFD, which allows describing the project guidelines to decision-making during the air distribution planning, considering the aircraft cabin’s geometry. Confident that the obstacles to thermal comfort in the concerned aircraft can be solved with what is observed in simulations made in computational fluid dynamics – capable not only of making predictions about the thermal field and its speed but also of indicating the particle concentration of ventilated environments. The chapter finishes with a discussion about the increase of passengers’ thermal comfort, adapting to variations and adjustments in the thermal environment. Such variations allow the creation and distribution of personalized ventilation that is formed around the passengers, and the best way to control this environment is through insufflation, temperature, and flow rate. Personalized ventilation, through air diffusers, highlights the Indoor Air Quality (IAQ), and the thermal comfort research gives a better understanding of the relationship between the human body and surrounding environments. In this regard, passengers play a central role in the aircraft cabins’ internal environments. It is noted that, although experimental researches using mannequins provided valuable information about airflow, speed, temperature, and pollutants concentration, some other detailed information such as the airflow field around a person and the relation between the amount of heat transfer by radiation and the transfer of convective heat between the human body and its surroundings cannot be obtained in experiments. The innovation happened in the past years with the introduction of CFD technology, which developed and made it possible to analyze the microclimate around a human being. It can simulate the passengers’ transient inhalation and exhalation in the processes, having the geometrical representation of the Computational Thermal Manikin (CTMs) which represents the human body, being a significant factor for the ventilation personal study, representing the turbulence, grid generation, and boundary conditions model selection. The researchers conducted in Denmark (Aalborg University and the Technical University of Denmark), Japan (The University of Tokyo), and Germany (Hamburg University of Technology) use computational thermal manikins (CTMs) with the intention of determining indexes that are either unable or at least very hard to be acquired through experiments.
Because the topic is clearly within the jurisdiction, to present some of the results obtained in our investigation, we first bring a brief literature review about the subject. At this point we highlight the work in which we based the present chapter and, which yet not recognized by scientists due to lack of information about the subject, we also present observations taken sometimes from the visits we made to the airlines and theirs web portals from 2014 to 2020, sometimes from the interviews granted by old employees, whose reports mainly focused on the aircraft’s internal environment development in Brazil. In short, being useful to thermal comfort practitioners.
This will allow the identification of the airport, plane, and passengers with contagion control purposes by airborne contamination, we hope that the professionals who work directly in airplanes and airports’ organization and maintenance consider more actively the elements that involve environmental quality and human thermal comfort.
The first identification of SARS-CoV-2 in human beings happened in Wuhan, China (2019). In that period there was paralyzation of air traffic in China. Right after the COVID-19 virus had spread to South Korea, caused the cancellation of flights, then to Iran and Italy. In that period, there were already cases of COVID-19 around the world. In March 2020, the United Nations (UN) declared a pandemic caused by the new SARS-CoV-2 virus, impacting the health system in biomedical and epidemiological order on a global scale. This fact imposed social isolation, among other measures, to protect health. At that time airports, ports, and land borders were closed except essential flights (Figure 1).
The impact of the COVID-19 pandemic in international airports.
In earlier times the aviation sector had already suffered before, in 1976 there was the Ebola virus contamination, affecting human beings and other mammals. There was a union of forces between the civil aviation secretary and the World Health Organization (WHO) to avoid the transmission of the Ebola virus, especially improving the internal environment quality of passenger airplanes.
In 2009, emerged the Flu A H1N1 pandemic and later on with a new sub-type of influenza A (H1N1). In response to the outbreak, on April 25 the World Health Organization declared a Public Health Emergency of International Concern. Then, on April 27, the WHO announced phase 4 (human-to-human transmission) and phase 5 (sustained transmission) pandemic [9], and phase 6 (global spread) on June 11th, 2009. On this date, there were already 30 thousand cases reported in 74 countries.
The World Health Organization (WHO) declared the pandemic in March 2020, which was a decisive moment to air traffic that came with a change of attitude for the aerospace industry. However, aviation impacts also occurred in 2001, 2008, and 2010.
On September 11th, 2001 many flights were canceled, both in the United States and in other countries, due to terrorist attacks. In this period the discussions about airport security had begun, nowadays every airport security forms are the result of the standards established at that time.
In 2008 because of the US economic recession tourist and business travel decreased. Generally speaking, the business class customers are loyal company customers. In this same period, there was an increase in oil prices, which was reflected in aviation till 2011.
In 2010, the volcanic eruption in Iceland Eyjafjllajökull disrupted European air transport, especially passengers flights between the US and Europe.
According to Faury, Guillaume (CEO of Airbus), 2020 “We are now in the midst of the gravest crisis the aerospace industry has ever known”. The planes used to conduct many cruises were forced to stand still, which requires maintenance before returning to operation. The airplane’s maintenance, in general, follows the “Parking Mode” (1) easy to get back to service; (2) maintenance (more frequent: engine and main systems); and the “short term” (a) preserve engines; (b) remove fluids; (c) cover all entries (sensors, cracks, engines, mechanical ventilation, etc.); (d) disconnect batteries, and (e) lower the shutters of the windows. The airplanes with outdated technology (with old models and large airplanes) or for sale are stored in deserts such as Victorville, California, and Pinal County, Arizona in the United States (Figure 2).
American aircraft boneyard. Commercial aircrafts in Southern California Logistics Airport (former United States Air Force base), Victorville, California, USA. Source:
It is noted that the coronavirus side effect is the use of better technologies, especially when it comes to air quality. The cargo companies besides cargo-specific planes also have passenger planes that transport a portion of cargo. The Belly Cargo (Long haul Flights) is a cargo plane with passengers that carries out 23% of all the world’s cargo. An important aspect of freight transport is hospital equipment products, this happens because, due to the pandemic, it was necessary to protect the whole hospital teams with suitable materials to assists patients with COVID (such as masks, aprons, hospital equipment products, etc.) which no country had in stock. Most of these products are manufactured in Asia, especially in China. In this regard, air transport is being requested to save lives (Figure 3).
Dissemination of COVID-19.
The Brazilian National Civil Aviation Agency (ANAC) acted to soften the pandemic impacts, reducing the contamination risks to the users and employees through the gradual resumption of operations of the internal and external market, with ANAC Ordinance No. 1126 of 23/4/2020 to combat an infectious agent in the standards for fighting COVID-19 published according to the International Health Regulations, in the Collegiate Board Resolutions, (Resolution—RDC No 02, 2003, Resolution—RDC No 21, 2008 and Resolution—RDC No 56, 2008 and in the guidelines of the Ministry of Health. It follows the international protocol to fight COVID and establishes (1) the central systems in operation as long as the air renewal is open at its maximum capacity, and (2) compliance with the Maintenance, Operation, and Control Plan—PMOC of the installed air conditioning systems, especially the filter, in the airport. In airplanes cleaning occurs in the supervision of the cleaning teams with cleaning and disinfection procedure in each scale, before the boarding of new passengers. With the closing of the doors, whenever possible, the airplanes air conditioning system turned on and the mode without air recirculation selected.
The air conditioning and pressurization systems are responsible for ensuring good health and comfort conditions for aircraft’s occupants since they are the components of environment control.
While in buildings we have homogeneous environments, aircraft are considered non-homogeneous once they present different temperature and velocity gradients [10]. The difference between aircraft and building air conditioning system design is due to the aircraft’s weight and the pressure difference between the outside and inside of the aircraft in high altitudes. When it comes to aircraft air conditioning special equipment capable of handling temperature asymmetries or radiant temperature is necessary.
In a good air conditioning system, the airflow must occur at a high speed at the top part of the airplane. In the bottom part, the recirculation is characterized by the mixed air present in the cabin (MV, mixing ventilation). Afterward, the engine must direct the outside air to the inner parts of the cabin, where, under very high temperature and pressure, it will be breathed in. Therefore, besides promoting air conditioning, the pressurization system avoids any discomfort or damage, because of the altitude changes that the cabin undergoes, for the occupants (the fast air change in the cabin eliminates odor and removes any traces of stale air).
Usually, the command cabin controls the pressurization systems that are incorporated in a sealed unit with the luggage compartment. The pressurization system is capable of containing air under higher pressure than the outside atmospheric pressure. Although in high altitudes the aircraft’s external environment does not present viable conditions to the survival of human beings. The air is dry with extremely low temperatures and pressure: according to Lombardo [11, 12], the atmosphere consists of 21% oxygen, 78% nitrogen, and 1% other gases in its volume; however, the increase in altitude implies air rarefaction and a decrease of pressure lowering the amount of oxygen necessary for human functions. That is why aircrafts that do not have air conditioning and pressurization system are usually limited to low altitudes.
The pressurization is directly related to the quality of the partial pressure of oxygen available in the breathing air inside the fuselage compartments of the airplane, occupied by the flight crew. Its purpose is to maintain the indoor pressure equal to or greater than the value of the atmospheric pressure at 8000 feet altitude. Because when the airplane flies at higher altitudes, there will be a reduction in fuel consumption.2
The ventilation, one of the functions for which the air conditioning system is designed, consists of a dynamic intake of pressurized air. This function is done with the aid of an airflow fan, a heating operation on the ground, or a compressor when the aircraft has pressurized air ducts installed in the front, the top, or the bottom. The air goes into the main air entrance of the heater and is heated when passing over the radiator surfaces from where it is then distributed.
The refrigeration system, which is located next to the ventilation methods, are installed to ensure comfortable atmospheric conditions to the aircraft regardless of the altitude where the plane is situated. It also works to maintain the appropriate volume of air circulating at the correct temperature and humidity inside the aircraft. The capacity of the refrigeration system depends on the fuselage cavity proportions so that the circulation of air and vapors occurs. In both cases, the treated air is pumped only in the overhead bins region at a high speed and the outlet is made by side air vents in the bottom. Meanwhile, the cabin air conditioning is made by the central air conditioning and heating system, which provides outflows of up to 700 m3/h (412 cfm) and controls the indoor air temperature from 14 to 35°C.
Before being recycled, the recirculated air is filtered by high-performance filters such as HEPA (High-Efficiency Particulate air filter), which is capable of retaining 99.97% of the cabin’s airborne particles.
The cabin air circulation and ventilation inside an airplane are carefully designed to disperse and redirect contaminants, changing the entire cabin air volume from 20 to 30 times per hour in airplanes with the E-Jet model [13]. Higher is the frequency of air change, lower is the risk of viral dissemination, however, this air change occurs only when the plane is free-flying. The Airbus Chief Engineer Jean-Brice Dumont highlights the importance of air quality design being extremely clean with air renewal every two or three minutes, about 20 to 30 times per hour.
The distance between countries as well as the time to travel these distances, have decreased with the development of the aeronautic engineering industry applied to air transport. Due to factors such as international scientific conferences, work, sport or artistic events, celebrations, etc. culture and habits dissemination happens more frequently.
In that sense, air quality becomes a priority to avoid infectious pathologies and maintain public health by preserving health safety in airplanes and airports.
According to WHO director-general, Tedros Adhanom Ghebreyesus, in a press conference on Wednesday (11/03) “If the countries work to detect and track the disease, isolate the cases and mobilize human resources to respond to COVID-19, it is possible to prevent those places with few cases from becoming centers of virus dissemination and consequently from sustained community transmission.” The director-general also pointed out the WHO guidelines to the countries which follow them: activate and expand the emergency response mechanisms, communicate with the population about the risks and how to protect themselves, find, isolate, test, and treat every case of COVID-19 apart from tracking all the infected.
In this context, keeping strict control on air transport before, during, and after the trip, it is possible to prevent the virus dissemination and its corresponding strains. The precautions before the flight, such as proper face mask usage, packing the luggage with plastic at the airport and/or using alcohol gel before the luggage is placed in the baggage compartment of the plane. As the International Air Transport Association states simple measures, such as the usage of masks by passengers and crewmates, as well as the guidance to use elbows to intercept coughs and sneezes, minimize the risks almost completely.
As the COVID virus could transmit among passengers on touching the infected surfaces and carelessness in using disinfecting substances, the main air companies of the world adopted new cleaning procedures to ensure that the aircraft is scrubbed after each flight, as well as ensuring passengers follow the required health and safety measures.
The air circulation in the cabin of the aircraft is done by a tube that captures external air and heats it during the flight by the engines, or by the auxiliary power unit when the airplane is on the ground. By a process of environmental control, the air is pressurized and cooled down to appropriate temperature for passengers and then it joins the recirculated air.
The air in the airplane’s cabin comes from the ceiling, flows to the ground, and drains below the luggage compartment. As the air flows from top to bottom, the risk of dissemination of infectious agents diminishes regarding the front-to-back direction, the longitudinal orientation of the cabin.
The air conditioning and pressurization systems are responsible for ensuring good health and comfort conditions to the aircraft’s occupants since they are the components of environment control.
While in buildings we have homogeneous environments, the aircraft is considered non-homogeneous once they present different temperature and velocity gradients. The difference between aircraft and building air conditioning system projects is due to the aircraft weight and the pressure difference between the outside and inside of aircraft in high altitudes. When it comes to aircraft air conditioning, special equipment, capable of handling temperature asymmetries or radiant temperatures, is necessary.
In a good conditioning system, the airflow must occurs at a high speed at the top part of the airplane. In the bottom part, the recirculation is characterized by the mixed air present in the cabin (MV, mixing ventilation). Afterward, the engine must direct the outside air to the inner parts of the cabin, where, under very high temperature and pressure it will be breathed in. Therefore, besides promoting air conditioning, the pressurization system avoids any discomfort or damage for the occupants because of the fast air change in the cabin due to the altitude changes that the cabin undergoes, which eliminates odors and removes any traces of stale air.
The pressurization is directly related to the quality of partial pressure of oxygen available in the breathing air inside the fuselage compartments of the airplane, occupied by the flight crew. Its purpose is to maintain the indoor pressure equal to or greater than the value of the atmospheric pressure at 8000 feet altitude. Because, if the airplane flies at higher altitudes, there will be a reduction in fuel consumption.
One of the functions for which the air conditioning system is designed, the ventilation, is performed by a blower to help air circulation and by a heater operation on the ground. From a dynamic compressed air valve or a compressor in aircraft which has ducts of pressurized air installed in its front, bottom, or top surfaces. The air goes into the main entrance of the heater and is heated when passing over the radiator surfaces of the heater from which it is then distributed.
The refrigeration system, which is located next to the ventilation methods, is installed to ensure comfortable atmospheric conditions for the aircraft regardless of the altitude the plane is located. They also work to maintain the appropriate volume of air circulating in the correct temperature and humidity inside the aircraft. The capacity of the refrigeration system depends on the fuselage cavity proportions in which the air cycle3 and the vapor cycle4 occur. In both cases, the treated air is pumped only in the overhead bins at a high speed and the outlet is made by inferior exit ducts. In the meanwhile, the cabin air conditioning is given through the air conditioning and heating central system, which provides outflows of up to 700 m3/h (412 cfm) and controls the indoor air temperature from 14°C to 35°C.
According to Conceição [14], the cabin must also have a humidifying system, responsible for maintaining the relative air humidity between 20 and 70% inside the cabin. It also must control the temperature of the walls once again from 14°C to 35°C, by an additional climatization and temperature control system, as shown in Figure 4.
Cross-section of thermal mock-up. Source: Conceição [
Control valves, sensors, and electrical cables regulate the indoor air temperature when activated by air conditioning system valves, located in the cockpit panel (Figure 5). If there is an automatic control malfunction, there must be manual controls available.
Typical air conditioning and pressurization system. Source: ANAC [
It can be seen that the cabin’s air distributor includes air ducts (with rectangular or circular sections when used in air distribution systems (Figure 6), or with other shapes when allocated in the passengers’ individuals air exits and the window defroster), filters and heater exchangers, silencers, unidirectional valves, humidifiers, sensors of mass flux control and meters. The cabin’s pressure sources cover positive displacement compressors (superchargers), centrifugal compressors, and supercharger controls. Supercharger tools working as airflow meters, pressurization valves, pressurization controls, cabin pressure regulator, and also air pressure safety valve.
Typical air distribution system. Source: ANAC [
According to Lombardo [11], the refrigeration machines which operate with air cycle are the predominant systems in aeronautical applications, especially when it comes to passenger transport aircrafts. The option is justified because of the availability of the working fluid (compressed air from the plane’s propulsion system) and also by the fact that the air cycle (air cycle machine or ACM) do not demand the transport of new working fluid, which would require weight and occupied space restrictions. So, the air is partially treated in high-quality filters, similar to those used in hospitals surgery rooms, and thereafter is mixed with the same proportion of external air. The renovation of conditioned air is necessary for long distance flights and with a large number of crew members in the airplane cabin in a closed environment. The airflow must therefore meet thermal comfort requirements through an air conditioning operating system and be compliant with external environmental atmospheric conditions.
Researches related to air conditioner maintenance of airlines such as TAM and Embraer (2014) show that the air distribution is operated by a container that works at 35% of its capacity, meanwhile, the other 65% of the air volume stands still on the floor, without returning to circulate through the cabin. In other words, the clean air is not used in the internal environment of the aircraft and likewise, the same air used before circulates yet again causing many airborne viral pathologies to be transmitted. Besides, in these situations, health problems caused by engine oil particles that were found in the air filters have become common.
Given the possible damages to passengers’ health, there were established flow standards for air conditioning systems. Maintaining the internal air quality demands that the renewal tax of the external air be high. In the same way, it is fundamental that the supply of external and recirculated air occurs in the appropriate temperature and relative humidity conditions. The temperature control of the cabin’s interior avoids areas with stagnated air, as well as enables the dissipation of contaminants and odors.
Efforts to maintain the good air quality inside the cabins turn out to be especially important as Quinyan Chen and her partners’ researches in the Purdue University College of Engineering (USA) pointed out that ventilation causes the dispersion of contaminants from expiratory activities (for example sneezing, coughing, talking or breathing). Presenting their studies about the main characteristics of particles dissemination in airplane cabins, researchers demonstrated how they can be involved in contamination events. Nevertheless, they suggested the relevance of paying attention to the subject, since there is little research about personalized ventilation systems that can be used along with a mixed ventilation system. It is known that before being supplied to the cabin, the recirculated air is filtered through equipment with high-efficiency particles. Those air filters also known as HEPA filters must be capable of reducing the risks of a cross and longitudinal infection of the airflow supply [15]. Next, Computational Fluid Dynamics (CFD) analysis of the airflow will be used to determine air conditioning coefficients in commercial passenger airplanes.
The aircraft cabin e-170 uses the normal ventilation system with a longitudinal direction. In this chapter, it will be applied and validated, in a testing phase, in mock-ups, with digital thermal manikins controlled by the Autodesk software with a Computational Fluids Dynamic (CFD) tool in order to determinate the temperature and speed coefficients and their corresponding thermal loads.
The tests include three steps: construction of the aircraft prototype; model construction of the empty cabin, with passengers standing and with the digital manikins seated; analyzing the actual air conditioning system to define the cabins’ thermal environment characteristics inside a commercial aircraft for passengers transport.
Prototype construction of the aircraft e-170 with the internal layout of armchair, luggage rack. A digital mock-up will be used for the tests to reproduce the cabin section of a commercial airplane with the dimensions of 3 × 3 × 2.5 m in height Figure 7.
Prototype construction of the aircraft e-170.
The construction of the passengers digital model is made from the use of the digital thermal manikin, built using the software “Solid Works”, it helps in the anthropometric analysis of the armchair to evaluate the equivalent temperatures. The thermal manikin has 1.70 m in height, which allows a more representative temperature modeling of the surface of the body, to verify the passengers’ comfort and discomfort. The thermal manikin, for now described to the passenger as a digital-physical model controlled by three control modes: constant temperature (air temperature and speed), constant power, and Fanger’s comfort equation.
Fanger’s method will be used to verify the thermal exchanges and the thermal balance of the human body, in other words, the “CLO-FANGER-MET” method with the influence of atmospheric pressure. The air temperature and speed are measured in the cabins’ thermal environment Figure 8.
Digital model construction of the passenger.
The air quality and its impact on humans during the cruise are determined by the aeronautical comfort design and have strong influences on the thermal conditions of the passenger. The air recirculation used before the airborne pathologies by various types of viruses. For example, according to researches presented during the Roomvent Congress, 2014, ventilation is related to the circulation of contaminants in airplanes cabins which from expiratory activities, cause cross-contamination events. Belonging to the list of studies about environmental comfort this research therefore, evaluates the actual thermal behavior of the airplane commercial user. In the face of the need to investigate air diffusers, responsible for the crew discomfort and for the transmission of diseases such as the so-called SARS (Severe Acute Respiratory Syndrome), and the challenges that are imposed on the intentions to design a healthy and comfortable environment in the cabins, we analyze the air distribution in the interior project of such cabins. The research focuses around the armchair and duct shapes with the use of the Computational Fluid Dynamic (CFD) tool.
The results of this chapter present a model based on computation that can predict the temperature and airflow as well as the parameters of environmental air distribution in commercial airplanes cabins, in empty cabins, and with passengers seated or standing. The model is known as CFD (Computational Fluid-Dynamic Model). The purpose of this item is to present to the reader some of the main fundamentals that are necessary for the applications of CFD related to internal environmental technology in commercial aircrafts.
This item presents the information used in the data entry for the CFD commercial airplane e-170 cabin simulation: Figure 9.
Mesh refinement.
The results of this chapter present a model based on computation parameters that the air distribution and environment temperature in the commercial aircraft cabin are the passengers breathing, empty cabin, and with the passenger seated or standing. The purpose of this item is to present to the reader some of the main fundamentals that are necessary for the applications of CFD related to the internal environmental technology for commercial aircrafts.
The passangers breathing inside the plane’s cabin is presented in Figure 10.
Shows the air breathed by passengers.
The representation of the empty cabin with the representation of the air supply diffuser going from the top part to the bottom one with the cabin height of 0–1.70 m and 0–1.50 m length is presented in Figure 11. Figure 12 identifies the magnitude velocity from 0 to 0.9 m/min noticing that the maximum airspeed is concentrated close to the gasper outlet, decreasing its speed throughout the path to the air outlet at the bottom of the cabin. The air temperature in degree Celsius is presented in Figure 13, ranging from 22°C to 28°C, notice that this temperature is used for tropical climate inhabitants, different from the countries located in Scandinavia, where temperature above 18°C is the upper limit in summer. The air supply diffuser using a computational mesh through the cabin is shown in Figure 14. Figures 15 and 16 demonstrate, as an example, the points of a constant air temperature value and speed for an empty cabin.
Air supply diffuser representation using a computational mesh to demonstrate a Fluid Domain mesh.
Airspeed in the empty cabin.
Air temperature in the empty cabin.
Air supply diffuser representation using a computational mesh.
Iso surface are surfaces that represent points of a constant number. For example, temperature and airspeed.
Iso surface are surfaces that represent points of a constant number. For example, temperature and airspeed.
The airspeed with the passenger standing is represented in Figure 17 with a variation of magnitude velocity from 0 to 0.11 m/s. Figure 18 presents the air surface and Figure 19 identifies the air temperature between 24.88 and 27.04°C. Demonstrated in Figure 20 is the image from the perspective of the cabin. The cabin refining mesh is presented in Figure 21.
Airspeed with passenger standing.
Iso surface of the cabin with passenger standing.
Cabins temperature exhibition with passenger standing.
Iso surface of the airflow line with passenger standing.
Cabins mesh refinement with passenger standing.
A seated passenger in the cabin has an isometric view with Magnitude Velocity from 0 to 0.007 m/s in Figure 22. In Figure 23 the airspeed with the passenger seated (0.35–0.49 m/s) with the airflow going from the top part to the bottom part of the cabin. The air temperature with front view is shown in Figure 24 with a variation of 23.4–27.4°C and the airspeed with a variation of 0–0.37 m/s in Figure 25 and with airspeed ranging from 0 to 0.89 m/s in Figure 26. Seated passenger mesh refinement is presented in Figure 27.
Isometric view of passenger seated airspeed.
Airspeed with a seated passenger (0.35–0.49 m/s).
Air temperature with a seated passenger (23.4–27.4°C).
seated passenger airspeed (0–0.37 m/s).
Seated passenger airspeed (0–0.89 m/s).
Seated passenger refinement mesh.
The air quality and its impact on humans during the cruise are determined by the aeronautical comfort design and have strong influences on the thermal conditions of the passengers. The air recirculation used before the cause of airborne pathologies by various types of viruses. For example, according to researches presented during the Roomvent Congress, 2014, ventilation is related to the circulation of contaminants in airplanes cabins that from expiratory activities, cause cross-contamination events. Belonging to the list of studies about environmental comfort this research therefore, evaluates the actual thermal behavior of the airplane commercial user. In the face of the need to investigate air diffusers, responsible for the crew discomfort and for the transmission of diseases such as the so-called SARS (Severe Acute Respiratory Syndrome), and the challenges that are imposed on the intentions to design a healthy and comfortable environment in the cabins, we analyze the air distribution in the interior project of such cabins. The research focuses on the armchair and duct shapes with the use of the Computational Fluid Dynamic (CFD) tool. The CFD simulation used the software Autodesk Geometry/Mesh/Solver/Post processing.
Through CFD simulations it was found that the air exits run from the top part to the bottom part of the plane.
Figure 28 shows the effect of the air current circulation and dispersion of particles throughout the aircraft cabin. This demonstrates that it is important to use personalized ventilation during the covid-19 pandemic period. According to Anvisa [16], the airflow of the gasper must be directed straight between passengers to avoid that they inhale the air from one another, avoiding disease dissemination.
Mask usage during flight.
The aeronautical project quality is essential for providing passengers with acceptable environmental conditions in order to achieve their thermal comfort. The research demonstrates the utmost importance of the aeronautical community (industries, research labs, and universities) in the fluids modeling field and the practical applications in the industry’s daily job.
This research results present models based on Computational Fluid Dynamics (CFD) with parameters analysis of temperature and airspeed distributed in a real environment with a cabin sometimes empty, sometimes with passengers standing and/or seated.
It was verified that the temperature and airspeed are influenced by passengers’ behavior, whether they are seated or standing in the plane’s cabin. Breathing influences the airflow, being able to cause contamination in its environment. So, ventilation from the roof of the cabin promotes more particle dispersion throughout its area.
Another significant aspect to highlight is the importance of achieving the plane’s thermal comfort in a cruise. Having an adequate air conditioning system that contributes to the well-being, health, and the aircraft’s users’ health, as well as the other aspects that influence the passengers’ environmental comfort, when based on the cabins’ geometry, the armchairs positioning and ergonomics will also contribute positively in the aircraft’s mechanic system. We suggest the use of the air insufflation systems to avoid contamination through the air in the interior of the cabin.
As this research’s results demonstrate, simulations in CFD showed that there are three variables, and they must be considered to better evaluate the thermal comfort and indoor air quality in an aircraft cabin, which are- the profile of different temperatures, the airspeed, and human breathing. Therefore, the airflow circulation from the upper (ceiling) to the lower air supply duct (floor) promotes a wider dispersion of particles throughout the cabin that is associated with the characteristics of the mixing of this ventilation through passengers and the airplane’s seats. However, it is also important to investigate the airspeed and temperature associated with air humidity.
The use of personalized ventilation is important during the pandemic period of COVID-19, for instance, when the gasper is opened, the airflow must pass between passengers, the air circulation must be oriented directed to the floor’s lower duct. Another precaution that helps to protect the passengers is the use of masks and facial protectors during the flights, which must have non-ventilation and be approved by government agencies. Another security measure adopted to protect the people is to remain at a distance from others and always use alcohol gel.
The concern with passenger air transport relates to public health and safety aspects which must be analyzed in order to have a faster response for such risks. This is fundamental so that measures, such as prevention and vaccine development, in addition to efforts to mitigate COVID-19 transmission and dissemination, can be effective. Technical and scientific researches, as well as public policies to aid and enforce these measures must be constant and applied to the aeronautics industry everywhere in the world, with decisions made according to international health regulations. An additional investigation must be carried on about contamination of material carried in the aircraft.
English version by: Samantha A.L. Takatui, Mayor Edson da Silva and Eng. Nilson Carneiro, Prefeitura Municipal de Araraquara, São Paulo, Universidade Federal de São Carlos, PPGEU, Instituto de Pesquisa Tecnológica do Estado de São Paulo, IPT e Universidade de São Paulo, São Carlos.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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