DRAINO system Sensors at ZF2 LBA Manaus Site.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"861",leadTitle:null,fullTitle:"Nanomaterials",title:"Nanomaterials",subtitle:null,reviewType:"peer-reviewed",abstract:'The book "Nanomaterials" includes all aspects of metal-oxide nano-structures, nano-composites, and polymer materials instigating with materials survey and preparations, growth and characterizations, processing and fabrications, developments and potential applications. 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Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. 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He also works as a Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute, and has worked as Senior Manager in Accenture (2013-2014). He obtained his European PhD with a maximum distinction. He is a holder of the Runner Prize for Management Science and Engineering Management Nominated Prize (2020), Advancement Prize (2018), First International Business Ideas Competition 2017 Award (2017), Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM), and Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65 % ISI, 30% JCR, and 92% internationals), some recognized as follows: “Applied Energy” (Q1, as “Best Paper 2020”), “Renewable Energy” (Q1, as “Best Paper 2014”), “ICMSEM” (as “excellent”), “International Journal of Automation and Computing” and “IMechE Part F: Journal of Rail and Rapid Transit” (most downloaded), etc. He is an author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, IntechOpen, IGI, Marcombo, AlfaOmega, etc.), and 5 patents. He is also an Editor of 5 International Journals and Committee Member of more than 40 International Conferences. He has been a Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for universities, companies, etc. He is an European Union expert in AI4People (EISMD) and ESF. He is Director of www.ingeniumgroup.eu. 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The terrestrial biosphere is an important component of the global carbon system. The long term exchanges estimates of terrestrial biosphere is a challenge and has resulted in ongoing debate [Baldocchi, 2008; Aubinet, 2008]. For monitoring long-term net ecosystem exchange (NEE) of carbon dioxide, energy and water in terrestrial ecosystems, tower-based eddy-covariance (EC) techniques have been established worldwide [Baldocchi, 2008].
It is now recognized that the EC technique has serious restrictions for application over complex terrain and under calm and stable nighttime conditions with low turbulence or limited turbulent mixing of air [Goulden et al., 1996; Black et al., 1996; Baldocchi et al., 2001; Massman and Lee, 2002; Loescher et al., 2006; Aubinet, 2008, Tóta et al., 2008]. To overcome this problem, the friction velocity (u*)-filtering approach has been formalized by the FLUXNET committee for the estimation of annual carbon balances [Baldocchi et al., 2001; Gu et al., 2005]. This approach simply discarded calm night’s flux data (often an appreciable fraction of all nights) and replaced them with ecosystem respiration rates found on windy nights [Miller et al., 2004]. Papale et al., [2006] pointed out that this approach itself must be applied with caution and the friction velocity (u*) corrections threshold is subject to considerable concerns and is very site specific. Miller et al., [2004] reported that depending on the u* threshold value used to correct the flux tower data at Santarem LBA site (Easterly Amazon Region – Brazil), the area can change from carbon sink to neutral or carbon source to the atmosphere.
The transport of CO2 by advection process has been suggested by several studies as the principle reason for the ‘‘missing’’ CO2 at night [Lee, 1998; Finnigan, 1999; Paw U et al., 2000; Aubinet et al., 2003; Feigenwinter et al., 2004; Staebler and Fitzjarrald, 2004]. The search for this missing CO2 has spurred a great deal of research with the goal of explicitly estimating advective fluxes in field experiments during the last decade, in order to correct the NEE bias over single tower eddy covariance measurements (Aubinet et al., 2003, 2005; Staebler and Fitzjarrald, 2004, 2005; Feigenwinter et al., 2004; Marcolla et al., 2005; Sun et al., 2007; Leuning et al., 2008; Tóta et al., 2008; Yi et al., 2008; Feigenwinter et al., 2009a, b).
The complexity of topography and the presence of the valley close to the eddy flux tower have increased the importance to investigating if subcanopy drainage flow account for the underestimation of CO2respiration as past studies have asserted [Froelich and Schmid, 2006]. The Manaus LBA site (Central Amazon Region – Brazil), is an example of moderately complex terrain covered by dense tropical forest. The NEE bias is reported by preview works [Kruijt et al., 2004; de Araújo, 2009; and references there in], and a possible explanation for this is that advection process is happening in that site. This work examines subcanopy flow dynamics and local micro-circulation features and how they relate to spatial and temporal distribution of CO2 on the Manaus LBA Project site. The contribution of exchange of carbon between the atmosphere and the tropical Amazon Rainforest is discussed and correlated with the present work.
The study site is located in the Cuieiras Biological Reserve(54 58’W, 2 51’S), controlled by National Institute for Amazon Research (INPA), about 100 km northeast from Manaus city. At this site, named K34, was implemented a flux tower with 65m height to monitor long term microclimate, energy, water and carbon exchanges (Araújo et al., 2002), and various studies have been conducted in its vicinity. The measurements are part of the Large-Scale Biosphere-Atmosphere experiment in Amazonia (LBA). Figure 1 presents the study site location including the topographical patterns where the maximum elevation is 120m and the total area (upper panel) is 97.26 km2, with distribution of the 31% of plateau, 26% of slope and 43% of valley [Rennó et al., 2008]. The site area is formed by a topographical feature with moderately complex terrain including a landscape with mosaics of plateau, valley and slopes, with elevation differences about 50m (Figure 1), and with distinct vegetation cover (Figure 2). The eddy flux tower at Manaus K34 site has footprints that encompass this plateau-valley mosaic.
The vegetation cover on the plateau and slope areas is composed by tall and dense terra firme (non-flood) tropical forest with height varying between 30 to 40m, maximum surface area density of 0.35 m2m-3 (Figure 2b, see also Parker et al., [2004]), and average biomass of 215 to 492 ton.ha-1 [Laurance et al., 1999; Castilho, 2004].
On the valley area, the vegetation is open and smaller with heights from 15 to 25 m, but with significant surface area density more than the 0.35 m2m-3 (Figure 2b). The soil type on the plateau and slopes area is mainly formed by Oxisols (USDA taxonomy) or clay-rich ferrasolsultisols (FAO soil taxonomy), while on the valley area, waterlogged podzols (FAO)/spodosols (USDA) with sand soil low drained predominates. Also, in the valley area the presence of small patchy of Campinarana typical open vegetation with low biomass is also common [Luizão et al., 2004].
The precipitation regime on the site show wet (December to April) and dry (June to September – less than 100 mm.month-1) periods. The total annual rainfall is about 2400 mm and the average daily temperature is from 26 (April) to 28˚C (September). For more detailed information about the meteorology and hydrology of this site see Waterloo et al. [2006]and Cuartas et al. [2007].
Detailed measurements towers’s view in the ZF-2 Açu catchment (East-West valley orientation) from SRTM-DEM datasets.The large view in the above panel and below panel the points of measurements (B34 – Valley, K34 – Plateau, and subcanopyDraino system measurements over slopes in south and north faces (red square).
a) IKONOS’s image of the site at AçuCachment with level terrain cotes and vegetation cover and (b) vegetation structure measured from LIDAR sensor over yellow transect in (a). From (a) the valley vegetation (blue color) and vegetation transition to plateau areas (red colors).
The datasets used in this study include a measurement system to monitor airflow above and below the forest, horizontal gradients of CO2, and the thermal structure of the air below the canopy, named “DRAINO System” [see, Tóta et al., 2008]. The data used in this study were collected during the wet season (DOY 1-151) and the dry season (DOY 152-250) of the year 2006. Complementary information was used from flux tower K34 (LBA tower) on the plateau, and sonic anemometer data collected in the valley flux tower (B34, see de Araújo[2009] for details). The flux tower K34 includes turbulent EC flux and meteorological observations of the vertical profiles of the air temperature, humidity and CO2/H2O concentrations, and vertical profile of wind speed, as well as radiation measurements. The fast response eddy flux data were sampled at 10 Hz and slow response (air temperature and wind profiles) at 30 min average [see Araújo et. al., [2002] for details information].
- DRAINO measurement System – Manaus LBA ZF2 site
The Draino measurement system used in Manaus LBA Site was similar to that developed by State University of New York, under supervision of Dr. David Fitzjarrald, and applied at Santarem LBA Site, including the same methodological procedures and sampling rates [see, Tóta et al., 2008]. However, due to the terrain complexity, it was modified for Manaus forest conditions including a long distance power line and duplication of CO2 observations for different slopes areas (Figure 4). The Draino measurement system used in Manaus LBA Site was mounted in an open, naturally ventilated wooden house (Figure 3).
Draino measurement system used in Manaus LBA (South Face, see also Figure 4).
The system and sensors were deployed (Figure 4) with measurements of air, temperature and humidity (red points), CO2 concentration (green points), and wind speed and direction (blue points), for both south and north faces. The observations of the 3-D sonic anemometer were sampled at 10 Hz and all the other parameters (CO2, H2O, air temperature and humidity) were sampled at 1 Hz (Figure 4).
The acquisition system developed at ASRC was employed (Staebler and Fitzjarrald, 2005). It consists of a PC operating with Linux, an outboard Cyclades multiple serial port (CYCLOM-16YeP/DB25) collecting and merging serial data streams from all instruments in real time, the data being archived into 12-hour ASCII files. At Manaus LBA Site two systems in the both south and north valley slope faces were mounted (Figure 3 and 4).
For each slope face, a single LI-7000 Infrared Gas Analyzer (LI-COR inc., Lincoln, Nebraska, USA) was used. A multi-position valve (ViciValco Instrument Co., Inc.) controlled by a CR23x Micrologger (Campbell Scientific, Inc., Logan, Utah, USA), which also monitored flow rates was also used. This procedure minimizes the potential for systematic concentration errors to obtain the horizontal and vertical profiles. Following Staebler and Fitzjarrald [2004] and Tóta et al. [2008] a similar field calibration was performed during the observations at Manaus LBA Site, including initial instrument intercomparison.
Draino measurement system (South and North Slope face) implemented at Manaus LBA Site, including topographic view and instrumentation deployed.
The result was similar to that obtained by Tóta et al. [2008], with CO2 mean standard error was < 0.05 ppm and mean standard error of about 0.005 ms-1 for wind speed measurements. After intercomparison, the sonic anemometers and the CO2 inlet tubes were deployed as shown in Figure 4.
On the south face, the instrument network array (Figure 4 and Table 1) consisted of 6 subcanopy sonic anemometers, one 3-D ATI (Applied Technologies Inc., CO, USA) at 2m elevation in the center of the grid (named 3-D ATI), and 5 SPAS/2Y (Applied Technologies Inc., CO, USA), 2-component anemometers (1 sonic at 6m in the grid center and 4 sonic along the periphery at 2m, see Figure 4), with a resolution of 0.01 m s-1. Also, a Gill HS (Gill Instruments Ltd., Lymington, UK) 3-component sonic anemometer was installed above the canopy (38 m). The horizontal gradients of CO2/H2O were measured in the array at 2 m above ground, by sampling sequentially from 4 horizontal points surrounding the main tower location at distances of 70-90m, and from points at 6 levels on the main Draino south face tower, performing a 3 minute cycle. On the north face, similar CO2 measurements were mounted including a 6 level vertical profile and 6 points in the array at 2 m above ground, performing a 3 minute cycle.
On both slope faces the air was pumped continuously through 0.9 mm Dekoron tube (Synflex 1300, Saint-Gobain Performance Plastics, Wayne, NJ, USA) tubes from meshed inlets to a manifold in a centralized box. A baseline air flow of 4 LPM from the inlets to a central manifold was maintained in all lines at all times to ensure relatively “fresh” air was being sampled. The air was pumped for 20 seconds from each inlet, across filters to limit moisture effects. The delay time for sampling was five seconds and the first 10 seconds of data were discarded. At the manifold, one line at a time was then sampled using an infrared gas analyzer (LI-7000, Licor, Inc.). To minimize instrument problems, only one LI-7000 gas analyzer sensor, for each slope face, was used to perform vertical and horizontal gradients of the CO2.
Level (m) | Parameter | Instrument |
38 | u’ v’ w’ T’ | Gill 3D sonic anemometers |
2 | u’ v’ w’ T’ | ATI 3D sonic anemometer |
6,2 | u’ v’ w’ T’ | CATI/2 2D sonic anemometers |
2 | CO2 Concentration (horizontal array) | LI-7000 CO2/H20 analyzer |
38,26,15,3,2,1 | CO2, H20 Profile (Sourth face) | LI-7000 CO2/H20 analyzer |
35,20,15,11,6,1 | CO2, H20 Profile (North face) | LI-7000 CO2/H20 analyzer |
18,10,2,1 | Air Temperature and Humidity | Aspirated thermocouples |
u’, v’, w’: wind components and T’, air temperature fluctuation |
DRAINO system Sensors at ZF2 LBA Manaus Site.
The datasets analyzed in this study were obtained during the periods defined by dry (DOY 1-150 January to June) and wet (DOY 152-250 July to October) seasons of 2006. Figure 5 presents an example of the datasets cover, with 10 days composite statistic, for CO2 concentration and air temperature at south face area of the DRAINO system and the total precipitation on the plateau K34 tower measurements.
The measurements covered almost the entire year of 2006, including dry, wet and the transition from wet to dry season. The air temperature amplitude above canopy on the slope area of the DRAINO System was higher, as expected, in the dry season. A good relationship is observed between CO2 concentration and air temperature with much large amplitudes in the dry season than in the wet season. It is probably associates with less vertical mixing during dry than wet season producing much higher subcanopy CO2 concentration and vertical gradient along the forest.
The vertical profiles of air temperature from plateau K34 tower show a very different pattern from that on the slope area, probably due to canopy structure differences (Figure 2b,\n\t\t\t\t\t\tParker et al., [2004]). The canopy structure is important for characterizing its thermal regime as it can be seen in Figure 6. The mean canopy layer stores large quantity of heat during the daytime and distributes it downward and upward throughout the nighttime (Figure 6, 7).
Above canopy layer, over plateau area, the neutral or unstable conditions were predominant during the daytime for both seasons (Figure 6a, c). During the nighttime, stable conditions dominates during dry period (Figure 6b) and neutral to stable conditions for the wet period (Figure 6d). Similar pattern has been reported elsewhere for plateau forests in the Amazonia (Fitzjarrald et al., [1990]; Fitzjarrald and Moore, [1990]; Kruijt et al., [2000]; Goulden et al., [2006]).
days time series of the CO2 concentration (a), air temperature (b) (DRAINO System) and total precipitation (c) (plateau tower).
Boxplot of the virtual potential temperature vertical profile for dry (a, b) and wet periods (c, d) of the 2006 during night (b, d) and daytime (a, c), on the plateau K34 tower.
The below-canopy layer of ambient air on the plateau area was stable at all times (Figure 6a, b, c, d), indicating that this layer is stable where the cold air concentrated in the lower part of the canopy air space as shown in Figure 7.
Daily course of the vertical deviation of the virtual potential temperature ([?v=\'?vz-?v(z)A5.255]), during dry (a) and wet (b) periods of the 2006, over plateau K34 tower.
The Figure 7 presents daily course of the vertical deviation of the virtual potential temperature, e.g., ([
Note that a very interesting length scale can be extracted from the observation when the deviation is about zero. The vertical length scale has mean value of about 30 m during nighttime and 20 m during daytime (yellow color in the Figure 7a, b). Those values are comparable with above canopy hydrodynamic instability length scale used in most averaged wind profile models [Raupach et al., [1996]; Pachêco, [2001]; Sá e Pachêco, [2006]; Harman and Finnigan, [2007]].
On the slope area south face (see Figure 2) air temperature at 5 levels underneath the canopy (heights 17, 10, 3, 2, and 1 m) was measured. The observations of the air temperature profile inside canopy are used to monitor the possible cold or warm air layer that generates drainage flow on the slope area. Figure 8 presents observations of the virtual potential temperature vertical profile for both dry and wet periods, during both day and nighttime.
Boxplot of the virtual potential temperature vertical profile for dry (a, b) and wet periods (c, d) of the 2006 during night (b, d) and daytime (a, c), on the slope area DRAINO System tower (south face, see Figure 2).
The pattern on the slope area is clearly very different when compared with that on the plateau K34 area (Figure 6), except in dry period during daytime when the air was stable inside the canopy. During nighttime (wet and dry periods) a very stable layer predominates with inversion at about 9 m. These can likely be interpreted as a stable layer between two convective layers is associated with cold air (Figure 8). Yi [2008] hypothesized about a similar ‘‘super stable layer’’ developing during the night in sloping terrain at the Niwot Ridge AmeriFlux site. This hypothesis suggests that above this layer, vertical exchange is most important (vertical exchange zone) and below it horizontal air flow predominates (longitudinal exchange zone). The relationship between subcanopy thermal structure and the dynamic of the airflow on the slope area will be discussed in next section.
Figure 9 presents a daily cycle composite of the virtual potential temperature deviation from the vertical average ([
The cold air in the subcanopy upper layer is probably associated with top canopy radiative cooling, while the cold air just above floor layer is associated with upslope wind from the valley area (as discussed later in the next section).
The average of the vertical gradient virtual potential temperature was negative during nighttime and positive during daytime for both periods dry and wet (Figure 9). This observation shows that during the daytime a relative cooler subcanopy air layer predominates creating inversion conditions. In contrast, a relative hotter subcanopy air layer generates a lapse conditions during nighttime. In general that is not a classical thermal condition found on the sloping open areas without dense vegetation. This general pattern was present at several specific study cases not show here due limited size paper. A similar pattern was reported by Froelich and Schmid [2006] during “leaf on” season.
Daily course of the vertical deviation of the virtual potential temperature for dry (a) and wet (b) periods of the year 2006, and the virtual potential temperature vertical gradient (c), over slope area DRAINO System tower.
The LBA Manaus Site has moderately complex terrain when compared with the Santarem LBA Site (Figure 1, 2). This complexity generates a wind airflow regime much complex to be captured by standard measurement system like a single tower. At the Manaus LBA site, we implemented a complementary measurement system on the slope area to support the plateau K34 tower and better understand how the airflow above and below the canopy interact and also to describe how the valley flow influences the slope airflow regimes. It is important to note that the valley in the microbasin is oriented from East to West (Figure 2, 4).
Above the canopy (55m above ground level – a.g.l.) on the plateau area K34 tower, the wind regime was strongest (most above 2 m.s-1) during daytime for both dry and wet periods of 2006, with direction varying mostly from southeast and northeast for dry and wet period, respectively (Figure 10). During nighttime, the wind regime was slower (most below 3 m.s-1) and with same direction variation from northeast to southeast (Figure 10). As reported by de Araújo [2009], the above canopy valley area wind speed and direction was different from that of the plateau area, suggesting a decoupling mainly during nighttime. A clear channeling effect on the valley wind regime was observed; which was oriented by microbasin topography during both day and nighttime, with direction of the flow in the valley area determined by the valley orientation [as also reported by de Araújo, 2009].
Frequency distribution of the wind speed and direction. For dry (a, b) and wet (c, d) periods from the year 2006 during day (a, c) and nighttime (b, d), on the plateau K34 tower.
The above canopy (38 m above ground level – a.g.l.) on the slope area DRAINO system south face (see Figure 4, 3D sonic), the wind regime was very persistent from east quadrant direction during day and nighttime in both dry and wet periods of the 2006 (Figure 11). The daytime wind speed during the dry season was between 1 to 3 m s-1 and much stronger during the wet period with values up to 4 m s-1.
During the nighttime the wind speed was slower than 2 m s-1, except from northeast during the wet period. The wind direction pattern was similar to that on the plateau K34 tower (Figure 10) prevailing from northeast to southeast. This observation indicates that the airflow above the canopy on the slope area is related to how the synoptic flow enters in the eastern part of the microbasin (see Figure 2, 4).
Frequency distribution of the wind speed and direction above canopy (38 m above ground level – a.g.l). For dry (a, b) and wet (c, d) periods from the year 2006 during day (a, c) and nighttime (b, d), on the slope area at DRAINO system tower.
In Figure 12, the subcanopy array frequency distribution of the wind speed and directions is shown for both dry and wet periods of the year 2006, during both day and nighttime. The observations show that the airflow in the subcanopy is very persistent and with similar pattern during both dry and wet periods of the year 2006. It is important to observe that the south slope area in the DRAINO System (see Figure 4) isdownslopefrom south and upslope from north quadrants.
Subcanopydaytime wind regime
During daytime, in both dry (Figure 12a-c) and wet periods (Figure 12g-i), the wind direction prevailed from south-southeast (190-150 degrees) on the three slope regions [Figure 12, Top (a, d, g, j), Middle (b, e, h, k) and Low slope part (c, f, i, l)]. The airflow in the subcanopy was decoupled from the wind regime above the canopy (Figure 11) most of the time. The wind direction in the subcanopy airflow was dominated by a daytime downslope regime during the majority of the period of study, suggesting a systematic daytime katabatic wind pattern.
The wind speed in the subcanopy during the daytime was mostly from 0.1 to 0.4 m/s, and strongest at middle slope region (Figure 12b, e, h, k) about 0.3 to 0.5 m/s or above. A similar daytime katabatic wind regime was reported by Froelich and Schmid [2006] during “leaf on” season in Morgan-Monroe State Forest (MMSF), Indiana USA.
Frequency distribution of the wind speed and direction in the subcanopy array (2 m above ground level – a.g.l) on the microbasin south face slope area at DRAINO horizontal array system (see Figure 4). For dry (a-f) and wet (g-l) periods from the year 2006, during day (a, b, c, g, h, i) and nighttime (d, e, f, j, k, l).
The daytime downslope wind was also supported by the subcanopy thermal structure (Figure 9), where the air was cooling along the day by inversion of the virtual potential temperature profile with a positive vertical gradient (Figure 9c). This results shows that subcanopy flows in a sloping dense tropical rainforest are opposite to the classical diurnal patterns of slope flows studied elsewhere in the literature [e.g.; Manins and Sawford, 1979; Sturman (1987); Papadopoulos and Helmis, 1999; Kossmann and Fiedler, 2000]. It is important to note that few studies have been done in forested terrain and it is unclear why similar reversed diurnal patterns have not been observed in studies at other forested sites [Aubinet et al., 2003; Staebler and Fitzjarrald, 2004; Yi et al., 2005], except by a single point subcanopy measurement observed by Froelich and Schmid [2006].
Subcanopynighttime wind regime
The nighttimesubcanopy wind regime on the slope area (see the terrain on Figure 4) was very complex and differentiates from that one above the canopy vegetation. It was observed that, on the up-slope part, the nighttime airflow was southeast downsloping direction (130 -170 ) and northeast-northwest (45 -340 ) uphill direction (Figure 12d, j). In the middle-part of slope area, the wind moved uphill (from northeast; 30 -90 ) and also downsloping wind direction from southeast (Figure 12e, k), and with lightly higher wind speed. On the lower-part of the slope area (Figure 12f, l) the wind direction prevailed from the northeast (10 -70 ), indicating upsloping pattern (anabatic).It is interesting to note that, on the up-slope area, the wind direction regime (northeast-northwest, 45 -340 ) suggest a reversal lee side airflow (re-circulation or separation zone) probably in response to the above canopy wind (see Figure 11b, d). It is has been suggest by Staebler [2003] and reported by simulations using fluid dynamic models [Katul and Finnigan, 2003; Poggi et al., 2008].
The upslopingsubcanopy flows pattern, on the lower-part the slope area, is supported by subcanopy relative heat air layer along the slope during the night, as observed by lapse rate condition of the virtual potential temperature negative vertical gradient (Figure 9c). This observation does not follow the classical concept of nighttime slope flow pattern, as commented previously (section 3.1.2), this is a example of non-classical microscale slope flow. Froelich and Schmid [2006], has reported similar feature where they found anabatic wind regime during nighttime in their seasonal forest study area. Figure 13 presents the frequency distribution of the subcanopy wind direction on the south face slope area at DRAINO horizontal array system during upsloping (from north quadrant) and downsloping (from south quadrant) events.
Several correction methods have been proposed to calculate the mean vertical velocity, e.g. linear regression method [Lee, 1998], coordinate rotation [Finnigan et al., 2003] and the planar fit method [Wilczak et al., 2001]. We use the linear regression method by Lee [1998] to determine the ‘‘true’’ mean vertical velocity:
Frequency distribution of the subcanopy wind direction (a) upsloping (from north quadrant) and (b) downsloping (from south quadrant) on the south face slope area at DRAINO horizontal array system (see Figure 4).
On the plateau area, the mean vertical velocity was always positive indicating upward motion or vertical convergence at the top of the hill during night and daytime. In the valley area during nighttime, negative or zero values were observed, indicating a suppression of vertical motion (mixing) in the valley, as also reported by de Araújo [2009].
However, during the daytime a transition is observed, where beginning in the morning, downward motion is observed, changing after mid-morning to upward motion (Figure 14b). This suggests that probably the cold air pooled during night moved downslope and started to warm, resulting in a breakdown the inversion over the valley (see de Araújo[2009], for detailed description and references there in for this process). The mechanism of the breakdown, the inversion process over the valley is consistent with positive vertical velocity observed above canopy at slope area by the DRAINO system tower during daytime (Figure 14b).
The subcanopy diurnal pattern of the mean vertical velocity observed shows positive values during nighttime and negative during daytime, consistent with observed up and downsloping flow regime, respectively (Figure 13 a, b). This is consistent with thermal vertical virtual potential temperature gradient on the slope (see Figure 9c), where during nighttime (daytime) an unstable (inversion) condition is associated with upward (downward) mean vertical velocity (see Figure 9c).
The Figure 15 shows a schematic cartoon of local flow circulation from the previews sections observations.
Mean vertical velocity raw and correct vertical velocity (a) for DRAINO system slope tower (38 m), and hourly mean vertical velocity (b) for: plateau K34 tower (55 m), DRAINO system slope tower (above canopy - 38 m and subcanopy - 3 m) and for valley B34 (43 m) towers (see Figure 4, for details).
In Figure 15a is show the above canopy airflow over valley space (red arrow)and correspondent (induced) the most probable airflow above canopy over slope areas (blue arrow). In the same figure is show the main physical mechanisms (pressure gradient force) producing that micro-scale circulations. The observations result from preview sessions suggests that the balance of the buoyancy and pressure gradient forces generates the airflow or microcirculations patterns in the site studied.
During nighttime (Figure 15b), in the subcanopy, there is an upslope flow reaching about 10 m height above the ground, associated with positive mean vertical velocity (indicating upward movement). Also, above canopy, there is a downslope flow associated with negative mean vertical velocity, with downward convergence above the canopy. The microcirculation along the plateau-slope-valley is promoted by an feedback mechanism of accumulation of cold air drainage above canopy into the valley center (Figure 15b), creating the forcing needed to sustain nighttime pattern. The air temperature structure above canopy in the valley (see de Araújo, 2009) is a good indication of cold air pool in the center of the valley. Maybe the local pressure gradient force due to the cold air accumulation promotes the upward airflow in both the slopes of the valley. During daytime periods an inverse pattern is found (not show), indicating that this microcirculation is a systematic pattern in the site.
Schematic local circulations in the site studied, valley and slopes flow (a), 2D view from suggested below and above canopy airflow (b).
The CO2 concentration was measured by DRAINO system on the south face slope area for dry and wet periods of the year 2006, and on the north face slope during the dry period (Figure 4). The Figure 16 presents an example, for midnight (local time), of the horizontal wind field and spatial CO2 concentration over the DRAINO System south face domain.
The wind field was interpolated from the blue points onto a 10 m grid. Similar procedures have been reported in the literature (Sun et al., 2007; Feigenwinter et al., 2008). The horizontal wind regime plays an important role in modulating the horizontal spatial distribution of CO2 concentration (Figure 16).
Example at midnight (local time) of the horizontal CO2 concentration (ppmv) over the DRAINO System south face domain including an interpolated horizontal wind field (10 m grid).Note the geographic orientation and the red arrow indicating slope inclination (see Figure 4).
In Figure 17 (a, b, c) the typical pattern observed is shown for both dry and wet periods of the year 2006 measured by the DRAINO system on the south-facing slope area. During the daytime (Figure 17c), the wind prevailed downslope inducing a strong horizontal gradient of CO2 in the slope area (about 0.2 ppmv m-1). In the evening, periods of changes of the horizontal wind pattern (as described in section 3.1) show an upsloping regime in the lower-part and downsloping in the upper-part of the slope areas (Figure 17b). The wind regimes produce direct responses in the spatial feature of the horizontal gradient of CO2 concentration. Later during the night, the upsloping regime is well established and also the horizontal gradient of CO2 is growing from lower part of the slope to the top (Figure 17a).
These observations suggest a subcanopy drainage flow and its influence on the scalar spatial distribution. Therefore, as discussed in the previews sections, the flow above the canopy indicates a reverse pattern of downward motion (negative mean vertical velocity, see section 3.2.3) that suggests vertical convergence and possible horizontally divergent flow during nighttime. The report by Froelich and Schmid [2006] and more recently Feigenwinter et al., [2009a, b] describing similar features of the airflow interaction between above and below canopy.
Hourly average of the subcanopy (2 m) CO2 concentration and horizontal wind speed over DRAINO System south face area during dry period of the year 2006, note the geographic orientation and the red arrow indicating slope inclination (see Figure 4). The axis represents distances from center of the main tower. Daytime (a), transition period - evening (b), established nighttime (c).
The spatial distribution of the horizontal CO2 concentration, Figure 18 along the north face, shows a similar pattern than the south face described previously. Despite there being no wind information in that area, if one assumes the same spatial correlation between horizontal wind and CO2 concentration, it is possible predict that the wind should present an inverse pattern from the south face suggesting that during daytime the downslope wind direction should be from the northeast (Figure 18c, from blue to red color).
During evening period (Figure 18b), it should be indicating downslope (from northeast) in the upper part of the north face slope and upslope (from southeast) in the lower part of the slope, an inverse feature from Figure 17b. Finally, later in the night, on the north face slope, the wind pattern should present an upslope wind direction regime from southeast, an inverse regime that one from Figure 17a on the south face slope.
Hourly average of the subcanopy (2 m) CO2 concentration on the DRAINO System north face area during dry period of the year 2006. Note the geographic orientation and the red arrow indicating slope inclination (see Figure 4). The axis represents distances from center of the main tower. Daytime (a), transition period - evening (b), established nighttime (c).
One possible explanation to this subcanopy slopes wind regime and spatial distribution of CO2 concentration, is the valley wind channeling effect and how it is meandering when it enters in the valley topography [as described by de Araújo, 2009]. This valley wind pattern, probably causes oscillations as those observed on the CO2 concentration along the day (Figures 17, 18), the known “Seiche phenomena” (Spigel and Imberger, 1980).
The main objective of this study was to measure and understand the local circulation over a dense forest site in Manaus with moderately complex terrain and to verify the existence of the drainage flow regimes on slope and valley areas. The main pattern of the airflow above and below the canopy in dense tropical forest in Amazonia was captured by a relative simple measure system, as also has been done by more sophisticated measurements system as those described recently by Feigenwinter et al., [2009a, b].As described and discussed in preview sections, it was identified as drainage flow in both day and nighttime periods in the site studied. Evidence of the drainage current above canopy was suggested by Goulden et al, (2006) similar to the one observed here. The study highlighted that the local micro-circulation was complicated and presented tri-dimensional nature. Where to estimate the advection flux at this site seems uncertain and not possible with the limited measurement system employed. As reported recently by Feigenwinter et al., [2009a, b], even using a more sophisticated measurement design, the level of uncertainties is still high and some processes are not yet known and need more exploration perhaps using a more complete spatial observation network or even applying model resources (Foken, 2008; Belcher et al., 2008).
In summary, the drainage flow exists and is observed at K34 LBA site. Very large carbon uptake estimates reported previously should be questioned [Kruijt et al., 2004; Araújo et al., 2002] and more research is warranted. The use of nighttimeu* correction to avoid estimating canopy storage is inappropriate. One cannot get by using only above canopy turbulence information. The interactions between motions above and below canopy question the foundations of the footprint analysis [Schuepp et al., 1990; Schimid, 2006]. The representativeness of the eddy flux tower is most in question for complex terrain, especially on calm nights).
In 2017, total world energy consumption was approximately 13,511 million-ton equivalent of petroleum (MTEP). With the fast industrial growth of developing nations over the last decade, the industrial sector consumed approximately 2852 MTEP. It is estimated that in 2035, the world consumption of energy will increase by more than 30% [1].
\nApproximately 33% of the total energy consumed in the industry is rejected as residual heat, presenting as a major problem the fact that the most of this rejected energy is identified as low-quality residual heat [2]. This type of waste heat has a small working potential, and the temperatures are below 230°C, which implies a low energy density [3]. Concurrently with the concern for global warming and the issues of diminishing oil consumption, there is a strong incentive for the development of more efficient and clean technologies for heat recovery and energy conversion systems using waste heat.
\nIn order to minimize the waste of energy with residual heat, energy recovery systems have been more explored. These systems can become an important object of research and/or application if, at least, part of the thermal energy expelled by industrial equipment to the atmosphere can be reused [4]. In this context, experimental analysis of the direct conversion of thermal energy into electric energy, using thermoelectric generators, was carried out.
\nThe Seebeck effect is related to the appearance of a difference of electric potential between two different materials, placed in contact, however, at different temperatures [5]. Basically, this is the same effect that occurs in thermocouples, where two different materials are connected and submitted to a temperature difference, causing a potential difference to be generated and translated into a temperature reading. In addition to this application, the thermoelectric effect can be explored in the generation of energy for wristwatches and aerospace applications or, even, in the generation of electric energy from the heated gases released in the internal combustion of engines, boiler gases, and/or the geothermal sources. The thermoelectric generators (TEG) have as main characteristics the reduced dimensions, easy adaptation in complex geometry, and very low maintenance [6]. Its conversion efficiency is about 5%; however, studies conducted at the NASA laboratory have reached 20% efficiency for high temperatures [2].
\nThe studied thermoelectric generator consists of an arrangement of small blocks of bismuth telluride (Bi2Te3) doped with n-type and p-type, mounted alternately, electrically in series, and thermally in parallel between two plates of good thermal conduction [7], as shown in Figure 1.
\nSchematic diagram of a thermoelectric generator.
The top of the p-n junction is heated, and the bottom of the set is cooled; in this way, a temperature gradient is generated. The free electrons of the n-type doped elements and the interstices of the p-type elements begin to move toward the cold part, that is, the lower part of the system. In the cold part, the n-type doped elements acquire negative polarity, while the p-type elements get positive polarity. Closing the circuit between the p-n elements, an electric potential is generated [7], and, with the electron accumulation at the cold side, an internal electric field is created, causing the Seebeck voltage.
\nThe experimental apparatus and procedure developed for this research are described in details in this section.
\nThe experimental bench developed to obtain the thermoelectric generator characteristic curve, shown in Figure 2(a), consisted of a laptop (Dell™), an uninterruptible power supply (NHS™), an aluminum block containing electrical resistors in cartridge, a thermoelectric generator (TEG1-12611-6.0), a water-cooled heat exchanger, a controlled automated resistive load variation system controlled by an Arduino™, data logger (Agilent™ 34970A with 20 channels), a power supply unit (Politerm™ 16E), an ultrathermostatized bath (SOLAB™ SL-130), and a variable area flowmeter with throttle (Omega Engineering™ FL-2051). In Figure 2(b), the heating and cooling system of the thermoelectric generator and the data acquisition system to obtain generated power by the thermoelectric generator TEG1-12611-6.0 are shown in detail.
\nExperimental apparatus. (a) Experimental bench. (b) Test section and data acquisition system.
The thermoelectric generator used in this experiment is made of bismuth telluride (Bi2Te3) and has dimensions of 56 mm by 56 mm with a height of 3.3 mm, totaling a surface area of 0.003136 m2. An illustration of the generator and, also, its main specifications can be seen in Figure 3 and Table 1, respectively.
\nIllustration of the thermoelectric generator TEG1-12611-6.0.
Parameters | \nValue | \nUnit | \n
---|---|---|
Hot-side temperature | \n300 | \n[°C] | \n
Cold-side temperature | \n30 | \n[°C] | \n
Open-circuit voltage | \n8.4 | \n[V] | \n
Matched load resistance | \n1.2 | \n[ohms] | \n
Matched load output voltage | \n4.2 | \n[A] | \n
Matched load output current | \n3.4 | \n[A] | \n
Matched load output power | \n14.6 | \n[W] | \n
Heat flow across the module | \nApproximately 365 | \n[W] | \n
Heat flow density | \n11.6 | \n[W/cm2] | \n
AC resistance under 27°C at 1000 Hz | \n0.5 to 0.7 | \n[ohms] | \n
Main specifications of the thermoelectric generator TEG1-12611-6.0.
To measure the temperatures of the thermoelectric generator, K-type thermocouples with mineral insulation Omega Engineering™, fixed inside machined channels in the heater block and the water-cooled heat exchanger, from the ultrathermostatized bath, were used. The thermocouples were positioned in order to obtain the temperatures of the heated and cooled surface of the generator (Thermocouple 1 and Thermocouple 2—Figure 4), resulting in the obtaining of the temperature difference between these surfaces. Figure 4 shows the schematic diagram of the experimental system used to obtain the characteristic curve.
\nSchematic diagram of the data acquisition in the experimental apparatus.
For the performance of the experimental tests, the ambient temperature was maintained at 16°C ± 1°C by thermal conditioning system Carrier™, and the temperature of the water in the ultrathermostatized bath was also maintained at 16°C ± 1°C.
\nAs can be seen in Figure 4, the thermoelectric generator was positioned between the heat exchanger and the heating system. The heat exchanger consists of an aluminum block with machined channels inside. Water is fed by an ultrathermostatized bath with a flow rate of 1 L/min, passes through the channels in order to exchange heat with the upper surface of the TEG, and returns to the ultrathermostatized bath. The heat exchanger, the water inlet, and the water outlet in the exchanger can be observed in items (1), (2), and (3) in Figure 2(b), respectively.
\nThe heating system consists of an aluminum block located inside the base of the experimental apparatus, item (4) in Figure 2(b). The block contains two cartridge resistors associated in parallel, which are responsible for the heating of the block and, consequent, the dissipation of the heat to the TEG. Each cartridge resistor has power of 200 W. In order to increase the heat exchange between the surfaces, the heating system was insulated on its sides using aeronautic thermal insulation. In addition, an aluminum bracket and two threaded rods were used to exert constant pressure in the whole system, increasing the contacts between heat exchanger - TEG and TEG - heating system. These contacts will ensure the required temperature gradient in order to get the Seebeck voltage.
\nThe tests were performed by varying the potential difference (pd) applied to the heating system resistances, which simulate the residual heat, in order to obtain thermal loads from 40 to 320 W, with a variation of 40 W. Each thermal load was maintained for 20 minutes, where the quasi-permanent operating condition was reached. To ensure the data reliability, the experimental tests were repeated 3 (three) times for each dissipated power, and errors were compared taking into account that the differences between the mean values which were less than 0.5°C.
\nThe temperature data of the hot and cold surfaces of the TEG were recorded every 10 seconds using the two thermocouples and the data acquisition system, which are saved by the software Agilent™ Benchlink Data Logger 3. To obtain information about the thermoelectric generator, a data acquisition system based on Arduino™, presented as item (5) in Figure 2(b), was used. The system was responsible for switching resistive loads and obtaining values of voltage and current and, consequently, the power produced by the thermoelectric generator.
\nFor this, an Arduino™ programming was done to work with a set of relays and a bank of resistors, which supports up to 10 W of power, and the resistances are in a range of 1–50 Ω. In Figure 2(b), the set of relays and the bank of resistors are presented as items (6) and (7), respectively. Different parallel associations between the resistors were activated, obtaining distinct loads that the thermoelectric generator was subjected. As a result, the values of voltage and current are acquired as experimental data and, consequently, the corresponding power. This system was only activated when the condition of the thermal system reached the quasi-permanent condition.
\nThe analysis of experimental uncertainties aims to quantify the validity of the data and their accuracy and, therefore, to allow the estimation of the random error present in the experimental results. The error is defined as the difference between the actual value and the indicated value [8].
\nThe experimental uncertainties present in this research were associated to direct and indirect measurements, shown in Tables 2 and 3, respectively, and they were calculated according to the error propagation method described in [9]. The direct uncertainties are those relative to the parameters obtained using a measuring tool, being the current, input voltage and temperature, the voltage generated, and other temperatures. The uncertainty of indirect measurement is calculated in the function of other parameters, and a specific tool did not measure it. In the case of this experiment, the only indirect uncertainty is related to the power supplied by the power supply unit to the cartridge resistors, in the heating system. The power quantity was obtained by multiplying the voltage and current supplied by the source.
\nParameters | \nMeasuring instrument | \nUnit | \nUncertainty | \n
---|---|---|---|
Input current | \nPower source | \n[A] | \n± 0.01 | \n
Input temperature | \nUltrathermostatized bath | \n[°C] | \n± 1.00 | \n
Other temperatures | \nK-type thermocouple | \n[°C] | \n± 1.27 | \n
Input voltage | \nPower source | \n[V] | \n± 0.01 | \n
Generated voltage | \nArduino™ | \n[V] | \n± 0.01 | \n
Uncertainties of direct measurements.
Power of the power supply unit [W] | \nUncertainty [W] | \n
---|---|
40 | \n± 0.38 | \n
80 | \n± 0.53 | \n
120 | \n± 0.64 | \n
160 | \n± 0.72 | \n
200 | \n± 0.83 | \n
240 | \n± 1.01 | \n
280 | \n± 1.29 | \n
320 | \n± 1.72 | \n
Uncertainties of indirect measurements.
First of all, the water flow through the heat exchanger, the power dissipated by the electric resistances, and the constant ambient temperature are fixed. After obtaining the quasi-permanent condition, the data collection and treatment were performed. Figure 5 illustrates the output current (Iout) versus the output voltage (Vout), respectively, in [mA] and [mV], for different dissipated powers in the heating resistors.
\nOutput voltage versus output current for different power dissipations.
It may be noted that the output current and voltage increase according to the temperature difference between the sides of the thermoelectric generator. It is evident from the analysis of Figure 5 that there is high linearity obtained in the results. It may be further noted that curves have similar slopes; this means that the internal resistance of the thermoelectric generator changes minimally when the operating temperature is varied.
\nIn Figure 6, for each temperature difference value, the thermoelectric generator has different internal resistance values. It is possible to note that the resistance values increase in a quadratic form with the rise of the temperature difference.
\nInternal resistance of the thermoelectric generator.
A curve adjustment with coefficient of determination (R2) was performed close to 1. The value of the internal resistance of the generator as a function of the temperature difference (∆T) between the sides of the thermoelectric generator can be defined by
\n\n
Therefore, the characteristic curve of the thermoelectric generator studied can be expressed by
\n\n
Figure 7 shows the open-circuit voltage (Vopn) and closed circuit voltage (Vclc) as a function of temperature differences (∆T). The open- and closed circuit voltage increase linearly with the rise of the temperature difference. The points obtained for the closed circuit voltage are points where the maximum power generated is obtained.
\nOpen-circuit voltage and closed circuit voltage versus temperature difference.
The curve fit for the open-circuit voltage (Uopn) as a function of the temperature difference (∆T), with a determination coefficient of 0.996, can be determined by
\n\n
\n
Figure 8 shows the behavior of the output power (Pout) as a function of the output voltage (Vout) parameterized in the temperature difference between the faces of the thermoelectric generator. It can be observed that with the increase in temperature difference between the faces of the thermoelectric generator, the power generated is also raised.
\nOutput power versus output voltage for different temperature differences.
As expected, the characteristic curves obtained follow a highly quadratic behavior, indicated by the coefficient of determination R2 close to 1. The maximum power generated occurs when the resistance of the external load is equal to the resistance of the internal load. At this point, the power of 7068 mW is generated with a voltage of 2340 mV for the temperature difference of 230°C.
\nFigure 9 indicates an alternative way to illustrate the output power data: output power (Pout) as a function of output current (Iout) parameterized in temperature differences (∆T) between the surfaces of the thermoelectric generator. It can be seen, again, that the maximum power point is 7068 mW for a current of 3020 mA considering a temperature difference of 230°C.
\nOutput power versus output current for different temperature differences.
Another important point to analyze is the comparison of the amount of power generated by different types of generators. For this, it is of extreme relevance to consider the size of the generators and the generated power density and not just the amount of generated power [W]. As a result, it is important to evaluate how much power is generated per square meter of the generator [W/m2]. Thus, Table 4 shows the power generated by the thermoelectric generator area as a function of the temperature gradient.
\nTemperature difference [°C] | \nGenerated power per area [W/m2] | \n
---|---|
30 | \n25.65 | \n
60 | \n114.14 | \n
90 | \n262.75 | \n
110 | \n567.12 | \n
140 | \n888.71 | \n
170 | \n1345.38 | \n
200 | \n1723.54 | \n
230 | \n2253.93 | \n
Generated power in relation to temperature difference per area.
As shown in Table 4, it can be seen that in a condition such as that found in processes with residual heat release, with a temperature gradient in the range of 60°C, the thermoelectric generator is capable of generating 114.14 W/m2. This value is almost the produced power by a photovoltaic solar panel of monocrystalline cells (Siemens™ SM46 [10]), for example, which has A-level efficiency and can generate up to 152.60 W/m2. In addition, the thermoelectric generator can support higher temperature gradients and, if exposed to more extreme conditions, can generate up to 2253.93 W/m2 of electrical power for a temperature gradient of 230°C.
\nFigure 10 shows the behavior of the output power (Pout) as a function of the temperature difference (∆T). It may be noted that with the increase in temperature difference between the surfaces of the thermoelectric generator, the power generated raises in a quadratic form.
\nOutput power versus temperature difference.
Furthermore, the output power is limited only by the operating temperature of the thermoelectric generator, which is 270°C. Eq. (5) correlates the values of temperature difference for the electric power with a coefficient of determination of 0.996, for temperature differences greater than 7°C.
\n\n
In this chapter, experimental analysis of the direct conversion of thermal energy into electric energy was carried out. An experimental analysis was performed to obtain the operating characteristics of a thermoelectric generator of bismuth telluride (Bi2Te3). For this, it was necessary to develop an experimental apparatus to provide the necessary operating conditions. Therefore, it was possible to obtain the operating curves of the thermoelectric generator for a temperature difference between the surfaces of the thermoelectric generator of 30 and 230°C. It is noted that the highest power values delivered by the thermoelectric generator were for the greater temperature differences. Normalizing the power generated by the photovoltaic solar panel and the thermoelectric generator, in conditions close to the real ones of use, both have values of generated power, around 152.60 and 114.14W/m2, respectively. Also, the thermoelectric generator can reach values of 2253.93 W/m2 for a temperature gradient of 230°C. In conclusion, the application of thermoelectric generators in the recovery of residual heat is a great instrument to be explored. This kind of device is compact, requires very low maintenance, and has a geometry that can be coupled in most of the industrial systems.
\nAcknowledgments are provided to the CAPES, the CNPq, the PROPPG/UTFPR, the DIRPPG/UTFPR, the PPGEM/UTFPR/Ponta Grossa, and the DAMEC/UTFPR/Ponta Grossa.
\n\n electromotive force of the electric generator [mV] electric current [mA] power [mW] resistance of the thermoelectric generator [ohms] coefficient of determination temperature difference [°C] output voltage of the generator [mV] voltage [mV] output internal generator open circuit closed circuit
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",metaTitle:"IntechOpen events",metaDescription:"In our mission to support the dissemination of knowledge, we travel worldwide to present our publications, authors and editors at international symposia, conferences, and workshops, as well as attend business meetings with science, academia and publishing professionals. We are always happy to host our scientists in our office to discuss further collaborations. Take a look at where we’ve been, who we’ve met and where we’re going.",metaKeywords:null,canonicalURL:"/page/events",contentRaw:'[{"type":"htmlEditorComponent","content":"16-18 June 2020 Smart University Forum #SUF20, Ankara, Turkey
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