A Review on Phase Change Materials for Sustainability Applications by Leveraging Machine Learning

Abstract

Phase change materials (PCMs) have been envisioned for thermal energy storage (TES) and thermal management applications (TMAs), such as supplemental cooling for air-cooled condensers in power plants (to obviate water usage), electronics cooling (to reduce the environmental footprint of data centers), and buildings. In recent reports, machine learning (ML) techniques have been deployed to improve the sustainability, performance, resilience, robustness, and reliability of TES platforms that use PCMs by leveraging the Cold Finger Technique (CFT) to avoid supercooling (since supercooling can degrade the effectiveness and reliability of TES). Recent studies have shown that reliability of PCMs can be enhanced using additives, such as nucleators and gelling agents, including for organic (paraffin wax) and inorganic (e.g., salt hydrates and eutectics) PCMs. Additionally, material compatibility studies for PCMs with different metals and alloys have also garnered significant attention. Long-term studies for demonstrating the material stability and reliability of candidate PCMs will be summarized in this review book chapter.

1. Introduction

Phase change materials (PCMs) are pivotal in advancing sustainability across various industries, offering enhanced energy efficiency, reduced environmental impacts, and bolstered sustainability. Central to thermal energy storage (TES) and thermal management application (TMA), PCMs facilitate innovative solutions in cooling systems, electronics, and building designs. Their application ranges from conserving water in power plant condensers to minimizing the environmental footprint of data centers through efficient cooling strategies. Additionally, PCMs revolutionize building architecture by improving energy efficiency and lessening dependence on traditional heating, ventilation, and air conditioning (HVAC) systems, aligning with sustainability objectives. This chapter delves into the application of PCMs in TES systems, exploring their use with additives like nucleators and gelling agents to improve performance in both organic and inorganic forms. It addresses material compatibility with various metals and alloys to ensure the long-term reliability of PCM-based solutions. Moreover, the chapter underscores the potential of machine learning (ML) in enhancing the sustainability, performance, and reliability of PCMs. Through the Cold Finger Technique (CFT) and ML, it tackles challenges such as supercooling, showcasing the significant role of PCMs in sustainability efforts and the innovative impact of ML on optimizing their application and environmental benefits.

1.1 Phase change material (PCM)

PCMs are commonly employed to enable efficient TES in compact configurations, leading to the categorization of these TES systems as Latent Heat Storage Units (LHSUs) [1]. TES platforms can be categorized as illustrated in Figure 1.

Figure 1.

Classification of thermal energy storage (TES) materials [2].

PCMs store energy during melting or solidification through both sensible and latent heat. Initially, a PCM begins in a subcooled solid state, warming up via sensible heating until it reaches its phase transition point. At this juncture, energy input causes a phase transformation, maintaining a constant temperature within a portion of the PCM. This phase change absorbs energy, crucial for the transformation process across its volume, eventually transitioning into a liquid phase. Further energy input then increases the temperature, facilitating sensible heat storage based on temperature change (ΔT) and the PCM’s specific heat capacity. Although the storage of latent heat, determined by the PCM’s enthalpy, is the primary method in PCM TES systems, a minimal amount of sensible heat storage also occurs in both phases, constituting a small portion of the total energy stored. Melting and solidification behaviors, based on the thermal energy stored in the PCM, are depicted in Figure 2.

Figure 2.

Melting and solidification behavior of PCM.

1.2 A brief history of PCM research

Research on PCMs has evolved significantly since the 1970s and 1980s, initially focusing on enhancing building comfort and energy efficiency. This foundational work has led to the innovative use of PCMs in construction materials for sustainable building practices, demonstrating their importance in clean energy solutions, such as in large-scale solar power plants and domestic hot water systems [3, 4]. NASA’s advancements in thermal management for space shuttles during the late 1970s further underscored PCMs’ utility [5]. By the early 1990s, PCM research expanded to address electronic cooling challenges in high-performance computing, marking the beginning of its application in high heat flux scenarios [6]. The early 2000s saw PCM research diversify into both high- and low-temperature applications, including waste heat recovery and utilization, essential for sustainable energy practices [7]. More recently, the integration of ML techniques has marked a significant milestone, enhancing PCM selection, design, and utilization. This has led to accelerated discoveries of optimal PCMs, improved thermal energy storage systems, and advanced thermal management strategies, showcasing the potential for ML to revolutionize PCM research and applications in sustainability and advanced technology domains [8].

1.3 Literature survey

PCMs are integral to TES systems, celebrated for their ability to store heat energy efficiently in compact designs through the significant enthalpy changes during phase transitions. Ideal PCMs exhibit high latent and specific heat capacities, excellent thermal conductivity, and minimal volume change, and are nontoxic and noncorrosive, ensuring effective heat transfer [9]. TES, utilizing PCMs, is crucial for balancing thermal energy supply and demand, leading to the development of compact and efficient Latent Heat Thermal Energy Storage Systems (LHTESS) [1, 10]. These systems operate within a limited temperature range, focusing on the phase transition temperature [11]. PCMs have broad applications across various domains, including underfloor heating [12], building energy management [13], solar power [14], waste heat recovery [15], domestic water heating [16], electronics thermal management [17], passive cooling [18], and thermal insulation for unmanned underwater vehicles [19], showcasing their versatility and effectiveness in enhancing energy efficiency and management.

Nanofluids have enhanced TES by incorporating nanoparticles to enhance specific heat capacity [20]. PCMs are grouped into organics (e.g., ice, paraffin), inorganics (e.g., salt hydrates [LiNO₃·3(H₂O)]), and eutectics [21]. Organic PCMs, for instance, paraffin wax, offer uniform freezing and reliability but have low storage capacity (i.e., needs larger quantities) and poor power ratings due to low thermal conductivity. Solutions like finned tubes and metal matrices have been explored to enhance power ratings [22, 23]. Inorganic PCMs like salt hydrates have high latent heat capacity but suffer from subcooling, phase segregation, degradation, poor nucleation, significant volume changes, and corrosiveness [24, 25]. Efforts to address these issues have been ineffective and costly. Eutectic Phase Change Materials (EPCMs), such as Na₂CO₃∙10H₂O-Na₂HPO₄∙12H₂O, offer stable PCM with high enthalpy and durability. EPCMs can achieve unique phase change temperatures, by adding stearic acid. However, EPCMs also face challenges like low thermal conductivity, phase separation, supercooling, and corrosion due to their diverse PCM blend [26, 27].

Subcooling or supercooling poses challenges to the power rating and reliability of thermal energy storage (TES) systems by delaying solidification in phase change materials (PCMs), crucial for time-sensitive applications. The phenomenon is quantified by the difference between the phase transition and nucleation temperatures, typically assessed using the T-History method [28]. Factors like nucleation types, surface finish, and experimental conditions significantly impact subcooling. To mitigate subcooling in salt hydrates, researchers emphasize the importance of nucleation methods. Seeding techniques, introducing nucleating agents with compatible lattice structures [29], and the Cold Finger Technique, which initiates nucleation by creating a cold spot [30], are explored alongside dynamic methods employing high-pressure and shock waves to prompt PCM solidification, enhancing the thermal performance of Latent Heat Thermal Energy Storage Systems (LHTESS) [31].

The CFT addresses subcooling by leaving part of the PCM unmelted for nucleation, with controlling the melt fraction posing a challenge. ML, particularly through an artificial neural network (ANN) employing radial basis functions, offers a robust solution. It predicts the PCM’s melt fraction more accurately than traditional methods, improving thermal capacity utilization and mitigating subcooling. This approach is effective across various conditions, given sufficient data for model training [32].

2. PCM and their applications in TES

PCMs are cutting-edge tech with potential to boost efficiency, reduce environmental impact, and enhance sustainability in engineering applications. The area of PCM application has been discussed in the following sections.

2.1 Thermal management of electronics

Moore’s Law, which forecasts the doubling of computing chips’ density and processing power every 2 years, has significantly increased the need for advanced cooling solutions due to higher heat dissipation and reduced form factors. This trend poses challenges for thermal engineers in developing efficient chip-cooling technologies to prevent chip damage and maintain performance by keeping temperatures below critical levels. Portable electronic devices, designed for intermittent high computational activity, require cooling systems that can efficiently manage sudden heat spikes without draining the battery or accelerating aging. Phase change materials (PCMs) are ideal for this purpose, absorbing energy peaks and gradually releasing heat during standby, thus maintaining a stable operating temperature and effectively managing the thermal load. A study by Joshi and Pal demonstrated the efficacy of PCMs, like paraffin and eutectic alloys (Figure 3), in providing passive thermal management for mobile devices, showcasing their ability to control system temperature during variable power operations [33, 34].

Figure 3.

Comparison of temperature response of a heat dissipating system with and without PCM.

A study by Amon and Vesligaj on the impact of transient loads on electronics, with a duty cycle involving a 10 W peak for 45 minutes followed by 30 minutes of standby, showed that Phase Change Materials (PCMs) effectively mitigate temperature fluctuations, limiting the temperature to 31°C compared to 37°C without PCMs [35]. This demonstrates that PCMs provide reliable passive cooling for small electronic packages, reducing the need for moving parts found in active cooling systems. Incorporating PCM-based heat spreaders addresses design challenges in modern applications by meeting geometric constraints and minimizing solidification time.

2.2 Advances in PCM technology for data center cooling

Data centers, crucial for digital infrastructure, comprise IT equipment, infrastructure support, communication links, and security devices, operating continuously. This operation generates significant heat, requiring management for reliability and sustainability. User load varies diurnally, with “peak hours” during the day and “off hours” at night, challenging cooling systems, especially as server upgrades alter thermal characteristics.

Data centers utilize various cooling strategies, including Computer Room Air Conditioning (CRAC) units, airflow management, free cooling, liquid cooling, and thermosyphons, broadly categorized into airside and liquid-side systems [36]. Air-side cooling, particularly CRACs involving chillers, water pumps, fans, and cooling towers, is preferred for its reliability, lower initial costs, and maintenance ease. However, vapor compression systems, integral to CRACs, consume substantial energy due to year-round operation. Current practices also involve energy management, airflow optimization, single-phase, and phase change cooling to enhance efficiency, as detailed in Figure 4.

Figure 4.

Currently available high-performance and effective cooling technologies applied in data centers [37].

A review by Ma et al. [38] highlights the limited research on data center cooling systems incorporating cold energy storage for power outages. Simulations by Wang et al. demonstrated that PCM plates could maintain data center temperatures below 35°C for 9 hours during emergency power failures [39]. Another study optimized a latent heat storage system with a tube-in-tank design, finding that thermal conductivity enhancements significantly 0.2 to 1 W/(m·K) [40] increased its capacity effectiveness. Zheng et al. designed an air-based phase change material storage system for emergency cooling in data centers, showing through simulations that it could discharge over 2.5 kW for 30 minutes during power outages, offering a promising solution for maintaining critical temperature thresholds [41].

Phase change cooling (PCC) vastly surpasses traditional air-cooling methods, offering over a thousand times the cooling capacity [42]. It utilizes an under-evaporation technique, allowing for the use of various low-boiling electrolyte fluids and refrigerants [43], with the choice of refrigerant being key to its performance. Extensively researched and applied in data centers, PCC demonstrates exceptional thermal efficiency and energy conservation [44]. The incorporation of phase change materials (PCMs) further boosts PCC’s efficiency by enhancing evaporation and condensation processes, enabling the temporary storage and release of excess heat [45, 46]. This prevents overheating in data centers, reducing dependence on conventional cooling methods and lowering energy consumption.

2.3 PCM technology for buildings

To create buildings with a comfortable indoor atmosphere, effective ventilation is key, achievable through the smart placement of diffusers and exhausts, alongside managing heating by circulating warm air and minimizing losses with PCM [47, 48]. The COVID-19 pandemic has further emphasized the importance of indoor space management to curb virus transmission. Building materials, like thick walls, are crucial for insulation, absorbing heat during the day and releasing it at night to maintain comfortable temperatures and mitigate temperature fluctuations in arid climates. Since the late 1980s, PCMs have been incorporated for efficient thermal management, absorbing excess heat to reduce temperature peaks.

This integration enhances energy density in building materials, reducing temperature spikes and HVAC energy needs, with PCM phase transition temperatures ideally between 20 and 30°C. Various PCM applications have been explored, including macro-encapsulated PCMs in gypsum boards, immersion in gypsum, mixing in concrete, and use in roofing, offering several strategies for improving thermal management in construction (Figure 5).

Figure 5.

Application of PCM in buildings: Encapsulated PCM (a) internally bonded, (b) sandwiched between concrete walls, (c) externally bonded, and (d) roofed and internally bonded room models.

Through compression molding, Zhang et al. used microencapsulated PCM with gypsum and glass fiber. The results indicate that a mix of 50/50 composite gypsum board can absorb energy and moderate temperature rise for 48 mins and reduce peak temperature values to those of standard gypsum board. The major concerns with the use of PCM for buildings are availability, cost, additional weight, structural integrity, and suitable range of melting temperature afforded by PCM [49].

2.4 Supplemental cooling in air-cooled power plant condensers

Power plants, pivotal in electricity generation, necessitate efficient heat management, particularly in nuclear reactors, to ensure operational safety. Heat generated within a reactor core is dissipated into a water pool with submerged heat exchangers during maintenance. Without proper intervention, this can lead to thermal stratification, affecting the system’s efficiency [50, 51, 52]. Utilizing adiabatic shrouds around heat exchangers can prevent such stratification [53, 54]. Moreover, incorporating PCMs in the pool’s surrounding walls offers dual benefits: mitigating concrete damage and providing additional energy storage to postpone boiling. Given the extensive water and energy demands of conventional cooling systems, PCMs emerge as a vital innovation, exploiting their latent heat of fusion for significant thermal energy absorption during phase transition. This approach not only enhances cooling efficiency but also promotes sustainability by substantially reducing water consumption in power plant cooling operations.

2.4.1 Thermal power plant

Industrial cooling towers in thermal power generation are the largest consumers of freshwater. As energy demands continue to surge, the availability of freshwater for cooling purposes is expected to fall short, placing significant stress on freshwater resources. Consequently, there is a need for alternative technologies. One such alternative is dry cooling, utilizing air-cooled heat exchangers to eliminate the requirement for cooling towers. But the approach comes with trade-offs, including reduced operational efficiency, heightened capital and operational costs, and decreased reliability. In arid climates, air-cooled heat exchangers can become inoperative on scorching summer days when ambient air temperatures surpass the steam temperature at the turbine exhaust, resulting in power plant shutdowns and grid instability. To overcome this, supplemental cooling solutions like TES systems incorporate PCMs in LHTESS. It enhances performance and reliability, especially in areas with soaring daytime temperatures that exert backpressure on turbines and risk tripping failures.

2.4.2 Thermoelectric power generation

Thermoelectric power generation is a significant freshwater consumer in the United States, with a large part of the water used lost to evaporation. These power plants, operating at 35–55% efficiency, contribute to thermal pollution, endangering local ecosystems. While wet cooling systems, which utilize cooling towers, are more efficient and cost-effective due to water’s superior heat transfer capabilities, their extensive use is increasingly seen as unsustainable due to the strain on freshwater resources. As a response, there is a growing need to improve dry cooling technologies. Latent Heat Thermal Energy Storage Systems (LHTESS), incorporating phase change material (PCM)-based heat exchangers, offer a promising solution. LHTESS enhances the efficiency and reliability of dry cooling systems, particularly beneficial in arid areas prone to significant temperature swings and power generation challenges [55].

2.5 Solar energy systems and power plants

The global population increase has escalated energy demands, largely fueled by economic and technological advancements. Traditional energy sources such as fossil fuels and nuclear energy, while crucial, are finite and contribute significantly to greenhouse gas emissions, posing a threat to the environment [56, 57]. Hence, there is an imperative shift toward renewable and sustainable energy sources like solar, wind, hydro, and bioenergy to minimize environmental impact. Solar energy, in particular, has gained global traction due to its universal availability and potential in power production, drying, and space heating [58, 59, 60, 61, 62]. Enhancements in Solar Air Heaters (SAH) efficiency through ribbed heating surfaces and PCM [63, 64, 65], alongside the development of computational and mathematical models for PCM and nanofluids [66, 67, 68, 69], mark significant progress. Yet solar power systems face challenges such as irregular energy supply, efficiency fluctuations [70], and nighttime power generation gaps. Concentrated Solar Power (CSP) systems, which concentrate sunlight to heat a transfer fluid for power generation or storage in TES systems, offer solutions to extend operational hours and improve efficiency. Despite these advancements, residential solar thermal applications encounter distinct challenges, further explored in subsequent sections. The integration of TES in CSP, illustrated in the System Advisor Model (SAM) for a parabolic trough plant, is a step toward addressing these limitations, enhancing efficiency, and reducing costs (Figure 6).

Figure 6.

System Advisor Model (SAM) sketch of a parabolic trough plant. Credit NREL/SAM [71].

Traditional TE systems use molten salts for sensible heat storage, preferred for their high operating temperature ranges and low vapor pressures. Innovations in molten salt formulations are being explored to extend these temperature ranges, aiming for cost reduction and enhanced thermal efficiency. Research by Robak et al. on using PCMs in CSP applications showed that a PCM-based system employing heat pipes could significantly reduce the volume and overall system cost by 65 and 15%, respectively, compared to traditional two-tank models [72]. Nithyanandam and Pitchumani supported this, finding an 11% cost reduction in electricity production with PCMs over conventional sensible heat storage. The adoption of PCMs in TES could lead to substantial savings, though challenges like limited high-temperature PCM availability, phase segregation, and low thermal conductivity need addressing [73].

3. Enhancing PCM reliability with additives and addressing challenges of supercooling and compatibility

To harness the full potential of the PCMs and their reliability, additives or specific material types can be used, which helps overcome challenges related to PCM stability, supercooling, and degradation, making PCMs more dependable for various applications. For the efficiency and effectiveness of PCM-based thermal energy storage systems, additives are added to enhance their thermal properties, stability, and overall performance. A list of additives includes graphite, carbon nanotubes, metal nanoparticles, expanded graphite, cellulose fibers, silicon dioxide, polymers, microencapsulated PCMs, salt additives, paraffin wax modifiers, phase change enhancers, nanoencapsulations, fibrous materials, inorganic materials, and organic materials. To improve the stability and reliability of the PCMs, stabilizers are added. The list of stabilizers includes encapsulation, nanoencapsulation, inorganic matrices, polymer matrices, thickeners, phase change enhancers, surfactants, crosslinking agents, anti-agglomerating agents, antioxidants, thermal stabilizers, chemical modifiers, hybrid materials, and composite materials.

Li et al. discuss the benefits of incorporating nanoparticles, such as metal oxides and carbon-based particles, into phase change materials (PCMs) to enhance thermal conductivity, reduce temperature fluctuations during phase transitions, and improve thermal cycling performance [74]. Organic polymer-based additives are used to ensure uniform phase distribution, preventing phase separation and leakage, common issues in certain PCMs. The employment of stabilizers enhances the long-term reliability of PCMs by preventing phase segregation and chemical degradation, crucial for thermal insulation and renewable energy applications. Additives significantly improve PCM performance, addressing challenges like supercooling and enhancing stability, durability, and thermal efficiency.

3.1 The PCM supercooling

Supercooling occurs when a phase change material (PCM) like water does not solidify at its equilibrium freezing point, staying liquid even below this temperature due to the absence of nucleation sites, which are necessary for phase change initiation. This state hampers the PCM’s ability to store and release thermal energy efficiently, impacting the effectiveness, reliability, and performance of thermal energy storage (TES) systems. The degree of supercooling is influenced by factors such as impurities and surface conditions. To counteract supercooling, strategies like incorporating nucleators, using gelling agents, and applying the Cold Finger Technique are employed, enhancing the operational efficiency of TES platforms [75].

3.2 Nucleators as additives for reducing supercooling

Nucleators play a pivotal role in the nucleation process, acting as catalysts that trigger the initial solid crystal formation from a supercooled liquid, a crucial phase in various applications. These nucleators provide necessary sites for molecules or particles in a liquid or solution to begin clustering into solid crystals, significantly addressing the challenge of supercooling in PCMs. Research has explored the use of nucleation agents to mitigate supercooling, with typical nucleators including inorganic materials like talc or alumina nanoparticles and specific organic compounds. The choice of nucleator depends on the PCM used, its application, and the desired properties. For example, a study found that adding 20% by mass of NaCl to n-octadecane-based PCMs reduced supercooling by 6°C, demonstrating the impact of nucleators in enhancing PCM performance [76], provided in Table 1.

PCM	Thickener	Tm (°C)	Nucleating agent (size, μm)	Supercooling
				w/o nucleator	w/nucleator
$N{a}_{2}S{O}_4.10{\text{H}}_2\text{O}$	SAP	32	Borax (20 × 50–200 × 250)	15–18	3–4
$N{a}_2{\text{S}}_2{\text{O}}_3.5{\text{H}}_2\text{O}$	CMC	57	${\text{K}}_{2}S{O}_4$; $N{a}_2{\text{P}}_2{\text{O}}_7.10{\text{H}}_2\text{O}$	30	0–3; 0–2
${\text{CH}}_2\text{COONa}.3{\text{H}}_2\text{O}$	CMC	46	$N{a}_{2}S{O}_4$; $SrS{O}_4$; Carbon (1.5–6.7)	20	4–6; 0–2; 4–7
$N{a}_{2}HP{O}_4.12{\text{H}}_2\text{O}$	SAP	36	Borax (20 × 50–200 × 250); Carbon (1.5–6.7); TiO2 (2–200); Copper (1.5–2.5); Aluminum (8.5–20)	20	6–9; 0–1; 0–1; 0.5–1; 3–10

Table 1.

Supercooling range of the thickened PCMs with the respective nucleating agents [45].

3.2.1 Mechanisms and benefits of nucleator

Nucleators are essential in phase change materials (PCMs) for providing sites where PCM molecules can organize and solidify. Without them, PCMs may not transition to a solid state efficiently, remaining disordered and liquid below the intended phase change temperature. By reducing the energy barrier, nucleators facilitate an organized crystalline formation, enabling a more immediate phase change near the desired temperature. This significantly enhances thermal energy storage efficiency by mitigating supercooling and ensuring predictable, consistent phase change behavior, crucial for reliable thermal energy storage applications [77].

3.2.2 Recent developments in the use of nucleators

Recent advancements in nucleator technology have significantly improved the effectiveness and adaptability of PCMs across various domains. The strategic incorporation of nanoparticles enhances nucleation, reducing supercooling and boosting the efficiency of thermal energy storage systems [77]. Custom-tailored nucleators now meet the specific needs of different PCMs, optimizing phase change performance. Microencapsulation techniques extend nucleation effects, enabling controlled thermal release in PCMs. This innovation integrates nucleators with renewable energy sources, enhancing energy management. Additionally, the use of nucleators in high-temperature PCMs expands their application in areas like solar power and electronics cooling. Advanced characterization methods offer insights into nucleator behavior, leading to the development of more efficient materials. The exploration of hybrid PCM-nucleator composites and eco-friendly nucleators highlights a commitment to sustainability and reduced environmental impact in thermal storage solutions [78].

3.3 Gelling agents in PCMs

Gelling agents, or thickening agents, are added to PCMs to combat supercooling by altering their physical state to a semisolid or gel-like consistency, enhancing viscosity and cohesiveness. These agents help in stabilizing PCMs, preventing leakage, and improving handling. For instance, adding borax to Glauber’s salt significantly reduces its supercooling from 15 to 3–4°C [24]. However, the effectiveness of these agents may decrease over time due to phase separation, agglomeration, or sedimentation. Common gelling agents include organic compounds, polymers, and inorganic materials like silica nanoparticles and clay minerals as given in Table 2.

PCM component	Wt (%)	Function	Tm (°C)	ΔH (J/g)	Cps (J/g·K)	Cpl (J/g·K)	Ks (W/m·K)
$N{a}_{2}S{O}_4.10{\text{H}}_2\text{O}$; SAP; Borax	95.0	PCM	32	227	3.4	4.6	0.64
	3.0	Thickener
	2.0	Nucleator
${\text{CH}}_2\text{COONa}.3{\text{H}}_2\text{O}$; CMC; K2SO4	95.0	PCM	46	176	2.11	3.68	0.60
	3.0	Thickener
	2.0	Nucleator
$N{a}_{2}HP{O}_4.12{\text{H}}_2\text{O}$; SAP; TiO2	92.8	PCM	36	104	3.7	4.1	0.40
	3.5	Thickener
	3.7	Nucleator
$N{a}_2{\text{S}}_2{\text{O}}_3.5{\text{H}}_2\text{O}$; CMC; SrSO4	92.0	PCM	57	206	2.2	—	—
	3.0	Thickener
	5.0	Nucleator

Table 2.

Composition and thermophysical properties of PCM with thickener and nucleators [45].

3.3.1 Mechanisms and benefits of gelling agents

Gelling agents play a pivotal role in modifying the molecular arrangement and behavior of phase change materials (PCMs), rendering them more amenable to phase changes occurring at or near the desired temperature. These agents serve as nucleation sites within the PCM, initiating the crucial first step in the formation of solid crystals during the phase change process. By promoting nucleation, gelling agents encourage the PCM to initiate crystallization more readily, simultaneously increasing the viscosity of the PCM. This heightened viscosity hampers the movement of PCM molecules, compelling them to transition away from the supercooled state and toward the solid phase more expeditiously, effectively reducing the supercooling effect. Additionally, gelling agents lower the energy barrier necessary for nucleation and phase change processes to take place. Supercooling, a phenomenon where PCM molecules require a specific amount of energy to surmount this barrier and shift to the solid phase, is effectively mitigated by gelling agents. This reduction in the energy barrier ensures that the PCM solidifies more promptly, aligning with the intended phase change temperature. Moreover, certain gelling agents can augment the thermal conductivity of the PCM. This heightened thermal conductivity facilitates the efficient distribution and extraction of heat during the phase change process, further curtailing supercooling and enhancing the effectiveness of the PCM for thermal energy storage. Gelling agents also contribute to the creation of a more homogeneous PCM mixture within the material. This homogeneity significantly reduces the occurrence of regions within the PCM where supercooling can manifest, fostering uniform phase change behavior [67, 79, 80].

Gelling agents significantly alter the molecular arrangement of phase change materials (PCMs), facilitating phase changes at desired temperatures. Acting as nucleation sites, they initiate solid crystal formation, promoting crystallization and increasing PCM viscosity. This increased viscosity restricts molecular movement, accelerating the transition from a supercooled to a solid state, thereby reducing supercooling. Gelling agents also lower the energy barrier for nucleation, ensuring quicker solidification. Some agents enhance PCM thermal conductivity, improving heat distribution and extraction, further minimizing supercooling, and boosting thermal energy storage effectiveness. Additionally, they contribute to a more homogeneous PCM mixture, preventing uneven supercooling and ensuring uniform phase change behavior [69, 79].

Gelling agents enhance energy storage efficiency by diminishing supercooling, thereby reducing energy losses, and ensuring predictable phase change behavior through consistent nucleation. They improve thermal management by keeping the PCM within its intended temperature range and extend its lifespan by preventing phase separation across thermal cycles. Additionally, gelling agents boost heat transfer and thermal conductivity for quicker charging and discharging, minimize leakage risks by solidifying the PCM, and ensure compatibility with system components, reducing corrosion. Their use simplifies handling due to the semisolid state and offers versatility for various thermal energy storage needs.

3.3.2 Recent developments in gelling agents

Recent advancements in gelling agents have aimed to enhance performance, sustainability, and versatility across industries. The focus has shifted toward natural and renewable agents like agar-agar, carrageenan, and pectin, alongside bio-based alternatives to lessen environmental impacts of PCM-based systems. Microencapsulation techniques enable precise control over thermal energy release, improving efficiency and stability in PCM systems. Stable PCM emulsions created with gelling agents offer better stability and heat transfer. Hybrid gelling agents combine traditional and nanomaterials for superior thermal properties, while customization for specific PCM materials optimizes phase change temperature and viscosity. Sustainability is emphasized through biomass-derived or waste-material gelling agents, aligning with broader environmental goals [55].

3.4 Addressing supercooling challenges using the Cold Finger Technique (CFT)

The CFT is an innovative method aimed at mitigating supercooling in PCMs, ensuring their solidification at or near the intended phase change temperature. Praised for its simplicity and reliability over extended periods, CFT finds application in various sectors including energy storage, HVAC, and refrigeration, where precise temperature control and phase change efficiency are critical. Its versatility allows for customization to meet specific PCM temperature requirements and adapt to unique industry demands, highlighting its importance in improving thermal management and energy efficiency across a broad spectrum of applications [32].

3.4.1 Mechanisms and benefits of CFT

The cold finger is a device designed to prompt nucleation and crystallization in phase change materials (PCMs) at their phase change temperature. Functioning as a metal rod or plate cooled below the PCM’s phase change point, it directly contacts the PCM, initiating cooling or solidification at its surface first. This method accelerates the PCM’s shift to the solid phase by offering a surface for nucleation, thereby reducing the energy barrier for transition. Consequently, the Cold Finger Technique enhances the predictability and consistency of PCM phase changes, crucial for temperature stability and reliable performance in applications like thermal energy storage systems [79].

3.4.2 Recent developments in CFT

Researchers are innovating Cold Finger designs by integrating nanoparticles to enhance nucleation and combat supercooling, providing more effective nucleation sites. Focus has also been placed on refining temperature control within these setups to improve PCM efficacy. Customizing Cold Finger configurations to match the specific needs of different PCMs is key to optimizing phase change behavior. Hybrid approaches combining the Cold Finger Technique with other methods aim to offer comprehensive solutions. Efforts to adapt and optimize Cold Finger Techniques for thermal energy storage, incorporating renewable energy and ensuring grid stability, are underway. Advances in materials for Cold Finger components seek to improve durability and heat transfer, while developments in control and monitoring systems enhance understanding and optimization of PCM performance [81].

4. Issues of material compatibility with PCMs

Material compatibility with PCMs is crucial for applications in thermal energy storage and temperature control systems. As PCMs undergo phase transitions between solid and liquid states, absorbing or releasing thermal energy, potential interactions with surrounding materials may cause compatibility issues such as chemical reactions, corrosion, and leakage. Selecting materials that are chemically stable, corrosion-resistant, and can accommodate PCM volume changes is vital for the reliable and efficient operation of PCM-based systems. Proper compatibility ensures enhanced PCM reliability and addresses issues arising from interactions with additives and adjacent materials, preventing PCM degradation and ensuring system longevity [82].

4.1 PCMs with different metals and alloys

PCMs are strategically paired with different metals and alloys to augment storage capabilities. For instance, paraffin wax is blended with aluminum, preparing composite exhibiting improved thermal conductivity. Eutectic salt mixtures, such as LiCl-KCl, are integrated with copper to enhance thermal conductivity, for efficient heat transfer. For high-temperature TES systems, the combination of calcium sulfate hexahydrate (CaSO₄·6H₂O) with copper proves invaluable. Copper enhances thermal conductivity, rendering it suitable for elevated temperature application. Additionally, sodium sulfate decahydrate (Na₂SO₄·10H₂O) can be harmoniously merged with copper, ameliorating its thermal properties. Potassium nitrate (KNO₃), when paired with aluminum, becomes an option for low-temperature TES systems. Glauber’s salt (Sodium sulfate decahydrate (Na₂SO₄·10H₂O)) benefits from a copper infusion to boost thermal conductivity, critical for TES applications. In TES systems, the amalgamation of magnesium hydride (MgH₂) with nickel proves instrumental in augmenting hydrogen absorption and release properties. Moreover, sodium acetate trihydrate (NaCH₃COO·3H₂O), combined with either copper or aluminum, finds utility in low-temperature thermal energy storage systems, fostering efficient heat exchange. Fatty acids, such as stearic acid, exhibit improved thermal conductivity and stability for thermal energy storage when intertwined with various metals like aluminum, copper, or graphite. Furthermore, organic PCMs like paraffins and polyethylene glycols stand to benefit from the inclusion of metal particles or nanoparticles, such as aluminum and copper, thereby enhancing their thermal properties [83, 84].

4.2 Importance of compatibility of PCMs in TES applications

Compatibility is crucial in PCM-based thermal energy storage (TES) systems, safeguarding against degradation and ensuring system reliability, efficiency, and safety. Incompatible materials can cause chemical reactions with PCMs, leading to deterioration and affecting system lifespan and performance. Compatibility reduces corrosion risks, which is vital since some PCMs can corrode container materials, risking structural integrity and causing leaks. It also prevents contamination, ensuring accurate temperature control and optimal energy storage. Safety is enhanced by preventing leaks and structural failures, especially important in high-temperature applications. Efficient heat transfer, crucial for system efficiency, relies on compatible materials. In critical applications like renewable energy integration, reliability is key, and compatibility supports this. Moreover, choosing environmentally responsible materials aligns with sustainability goals, reducing the ecological footprint and promoting responsible resource management. Compatibility not only ensures long-term cost-effectiveness by avoiding system failures but also contributes to sustainability by minimizing waste [85].

4.3 Recent findings on material interactions with PCM

Recent research on phase change material (PCM) interactions with various materials has significantly improved PCM-based system performance. Studies on compatibility between PCMs, container materials, additives, and surrounding components have been crucial in identifying and mitigating potential issues like chemical reactions, corrosion, and leakage. Advanced coating techniques and tailored additives have been developed to enhance compatibility, prevent phase separation, and improve phase change behavior. Analytical techniques such as spectroscopy and thermal analysis have provided deeper insights into PCM material interactions. Strategies like corrosion-resistant coatings and the exploration of innovative container materials, including composites and polymers, have improved resistance to chemical reactions and corrosion. Life cycle assessments and computational modeling have furthered the understanding of PCM behavior and system design, promoting more sustainable and efficient PCM solutions [82, 86].

5. Role of ML for TES and TMA systems and enhancing PCM reliability

ML has had a significant impact on enhancing the performance of TES platforms. It plays a pivotal role in predictive modeling for both TES and TMA. A study published by Li et al. in 2020 exemplifies the utilization of ML algorithms for predicting energy demand patterns with heightened precision. These ML models rely on diverse data sources such as weather conditions, user behavior, and historical data to anticipate energy requirements. ML empowers TES systems to store and release energy with pinpoint accuracy, effectively minimizing energy wastage and bolstering overall system efficiency. The outcome is a TES solution that is not only more sustainable but also cost-effective. Moreover, ML finds application in real-time temperature control within TES systems. It continuously monitors temperature fluctuations, fine-tuning the processes of energy storage and release accordingly. This dynamic adaptability ensures that TES systems can perform optimally even as conditions change, thereby enhancing their reliability and overall performance. ML modules can be strategically implemented to fortify TES systems, taking on essential roles in fault detection, resilience, and robustness.

In real time, ML algorithms are harnessed to identify potential faults or irregularities within TES systems. ML models are adept at detecting anomalies and can promptly trigger preventive maintenance or system adjustments. This, in turn, minimizes downtime and augments system resilience. Furthermore, ML models prove invaluable in the selection of the most suitable PCMs. Through an analysis of the thermal properties of various materials, ML-based optimization leads to an increase in energy storage capacity and an overall enhancement of TES system performance. The integration of TES with renewable energy sources, an integral aspect of modern energy systems, is seamlessly facilitated by ML. The ML models proficiently predict renewable energy generation patterns, enabling TES systems to efficiently adapt and store surplus energy. This integration ensures a reliable and uninterrupted energy supply, thereby promoting the sustainability of renewable energy sources. Lastly, ML models wield substantial influence in the management of TES systems within the broader energy grid. ML algorithms dissect grid data and make real-time decisions regarding energy charging and discharging. This grid management competency plays a pivotal role in balancing energy supply and demand while concurrently reducing stress on the energy grid [87, 88].

5.1 Reducing supercooling of PCM using CFT combined with ML

In addition to precise control of the TES system functionality, the ML models can also be used to solve the problem of supercooling. Integrating the CFT with suitable forecasting methods like ML has the potential to enhance the reliability and consistency of CFT. A previous study shows that the degree of supercooling was reduced to 1°C for LiNO₃·3H₂O, which was demonstrated over 800 cycles of charging and discharging—with less than 6% degradation in energy storage capacity [89]. The successful implementation and execution of CFT techniques necessitates accurate forecasting models that need to provide accurate control of the melting process and robust predictions for different operating conditions. Hence, the history of PCM charging and discharging needs to be incorporated.

One potential approach for implementing CFT involves achieving a specific melting fraction of the PCM, such as 90% melt-fraction, with a high degree of accuracy and precision. To make this technique successful, it is crucial to predict in advance when a given mass of PCM will reach a target melt-fraction, like 90 or 95%, at which point the melting process should cease, and solidification should commence. The ability to predict the time required to reach the target melt-fraction should be robust and resistant to variations in environmental conditions, especially when dealing with multiple cycles of melting and solidification. Analytical and numerical models, which rely on energy and enthalpy balance approaches, often prove inadequate for providing reliable forecasts. These models can be overly sensitive to even minor fluctuations in environmental conditions, slight variations in process parameters, minor deviations in experimental procedures, and a narrow range of measurement uncertainties and experimental errors.

In such situations, ML algorithms prove to be well-suited for the precise prediction of melt fractions, a task that is often challenging for theoretical models due to their inherent complexity and sensitivity to transients. Theoretical models struggle to fully capture the system’s behavior and predict the coupled and nonlinear thermal-hydraulic dynamics at the system level. ML techniques, on the other hand, rely on analyzing statistical variations within experimental data, especially during transient system states. The effectiveness of ML can be significantly enhanced when a substantial amount of “training data” is accessible for the algorithms to learn from [32, 90].

5.2 Artificial neural network principles as the basis of ML

The concept of artificial neural networks (ANNs) draws inspiration from the information processing in biological neural systems, such as the human brain [91]. Unlike structured programming methods, which rely on explicit instructions, ANNs operate by detecting patterns and relationships in data, learning from prior experiences or training data. This unique approach to forecasting allows ANNs to discern connections between parameters without requiring an in-depth understanding of the system [92]. One common implementation of ANN is the fully connected multilayer perceptron (MLP) model, consisting of nodes, often called “neurons,” organized in sequential layers: input, hidden, and output. Neurons within a layer communicate through two-way connections, with the input layer having neurons equivalent to the input parameters and the output layer’s neuron count determined by the number of output parameters. The hidden layer, positioned between the input and output layers, facilitates communication among neurons to solve problems. This mimics the functioning of the brain. The first layer is called the input layer (receiving input data vectors), while the last layer is called the output layer. A schematic of the MLP network is shown in Figure 7.

Figure 7.

Schematic of the MLP network [87].

Depending on the output vector length, it may have more than one node. The middle layers are known as hidden layers. Each layer is connected to the previous layer using connectors. A single node is characterized by two entities—(1) bias, b, (2) and active function, f, while connectors have a weight, w. In each neuron, the activation function acts on the input a_n. received from the node in the preceding layer. The output value (a_n + 1) from one layer serves as the input for the subsequent layer with a serial number (n + 2). This sequential relationship can be expressed mathematically as shown in the following equation.

$${\text{a}}_{\text{n}+1}=\text{f}\left({\text{w}}^{\text{T}}{\text{a}}_{\text{n}}+\text{b}\right)$$E1

The development of ANN models includes three major stages including data training, result validation, and efficacy testing [32]. Initially, during the data training phase, weights and biases are initialized randomly, and subsequently, outputs are generated based on the training data. Following this, in the result validation stage, the produced output is compared with the actual output, utilizing a cost function to compute the extent of disparity or error between them. The specific cost function employed during the result validation phase is the sum of squared error (SSE), as represented in the following equation.

$$SSE=\sum _{i=1}^N{\left(p_i-a_i\right)}^2$$E2

Here, N is the number of result values, $p_i$ is predicted value from ANN, and $a_i$ is the actual value. The error denoted as SSE undergoes a backward propagation process from the result layer to the input layer, essentially implementing a gradient descent algorithm. This computational method is employed to adjust the biases and weights iteratively, to minimize the error between the predicted and actual values through multiple numerical iterations. This feedback mechanism continues until the desired level of error reduction is achieved. In the following stage, the efficacy of the ANN model is evaluated using a separate test dataset that was not utilized during the data training and result validation stages.

In previous applications, ANN techniques have been employed to predict the specific heat capacity of molten salt nanofluids used in TES applications, particularly in concentrated solar power (CSP) and solar thermal power generation [93]. Another category of ANNs, known as radial basis function neural networks (RBF-NNs), is particularly well-regarded for modeling material properties, including the thermophysical properties of nanofluids. RBF-NNs, first introduced by Broomhead and Lowe, also consist of three layers: the input layer, hidden layer, and output layer [94]. The primary function of RBF-NN ANNs is function approximation. The key distinction between an MLP model and an RBF-NN model lies in the computation process within the neurons.

In the RBF-NN model, the activation function is a radial basis function ϕ, which operates based on the Euclidean norm between the neuron’s center and the input vector. A connection weight is then applied to the result to produce outputs, as represented mathematically in the following equation.

$$y\left(x\right)=\sum _{i=1}^n{w}_iø\left(x-x_i\right)$$E3

Prior research has demonstrated the viability of predicting melt fraction using RBF-NN models based on temperature measurements [95]. The effectiveness of a fully developed MLP model enables real-time predictions of the time required to achieve a predefined melt fraction (e.g., 90% melt fraction) based on temperature measurements at any given moment during a melting cycle. This predictive capability helps to optimize the utilization of thermal energy storage capacity while eliminating the need for supercooling to initiate nucleation during solidification. Additionally, ANN-based methods help predict the material properties of PCMs while also forecasting their transient performance, particularly their temperature responses for battery thermal management [96]. Therefore, the MLP/ANN-based predictive models can significantly enhance the reliability of PCM TES platforms while simultaneously improving their overall systemic and thermodynamic efficiencies.

5.3 ML approaches used for examining PCM reliability

ML techniques can be utilized to assess the reliability of PCMs to improve the efficiency of TES systems. Table 3 presents various ML approaches and their respective purposes in this context [97, 98].

ML approaches	Intended function or application
Predictive modeling	Anticipates PCM responses across varying conditions. Utilizes models for projecting phase change behaviors, supercooling tendencies, and other attributes. Contributes to the evaluation of reliability.
Anomaly detection	Foresees unexpected PCM system behaviors. Detects deviations from anticipated patterns, signaling potential reliability issues
Regression analysis	Predicts material behavior in future scenarios. Evaluate long-term reliability and degradation.
Failure Mode Analysis	Envisions potential PCM failure scenarios. Adopts a proactive stance in averting reliability issues.
Data-driven decision-making	Offers guidance on maintenance, repair, or replacement to uphold reliability.
Optimization	Projects optimal operating conditions for TES systems, reducing wear and tear and elevating reliability.
Probabilistic modeling	Forecasts the probability of PCM failure or degradation over time, aiding in reliability assessments.
Pattern recognition	Identifies patterns indicative of potential reliability concerns, allowing for timely interventions.
Life cycle assessment	Conducts comprehensive life cycle assessments of TES systems, accounting for factors like environmental impact and long-term reliability.
Real-time monitoring	Maintains ongoing assessments of PCM behavior through ML-based sensors and monitoring systems.

Table 3.

Machine learning approaches and their intended functions.

5.4 Recent developments in ML approaches for PCM-based TES systems

ML approaches offer versatile solutions adaptable to address future challenges in TES systems. Ongoing advancements involve the development of sophisticated ML models capable of predicting PCM behavior with increased precision, encompassing complex phase change processes and accounting for various influencing factors. The fusion of ML with artificial intelligence (AI) is being leveraged to tackle supercooling issues by creating intelligent control systems that actively manage phase transitions. Furthermore, the evolution of ML algorithms aims to provide comprehensive reliability assessments, including the prediction of long-term PCM system behavior and early detection of potential failure modes. Advanced ML systems enable autonomous and initiative-taking maintenance of PCM-based TES systems, detecting anomalies, scheduling maintenance tasks, and optimizing performance without human intervention. ML is also contributing to the rapid discovery and design of innovative PCM materials, enhancing thermal properties and system reliability. Additionally, ML plays a pivotal role in integrating PCM-based TES systems with smart grids and renewable energy sources, optimizing the interaction between energy storage and generation to bolster grid stability and energy efficiency. Evaluating the environmental impact of PCM materials and components and assessing the long-term sustainability of TES solutions are facilitated by advanced ML techniques. Finally, the development of real-time monitoring and control systems utilizing ML-based sensors empowers the effective management of PCM-based TES systems [99, 100].

5.5 Optimizing PCM selection and configuration with ML

The integration of ML with PCM optimization represents a significant advancement in the development of TES systems. By utilizing data science, this method addresses the complex task of selecting the ideal PCM, factoring in thermal conductivity, melting point, latent heat capacity, and material compatibility [101]. Recent strides in ML, particularly through deep learning and reinforcement learning, have demonstrated substantial success in predicting PCM thermophysical behaviors in varying conditions, thus surpassing the limitations of traditional selection methods. The process involves training ML models on extensive datasets, including PCM properties and performance metrics across different scenarios, enriched with computational fluid dynamics (CFD) and finite element analysis (FEA) simulations [102]. These models, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel at detecting intricate patterns and correlations, accurately forecasting PCM responses [103]. Additionally, the deployment of optimization algorithms, such as genetic algorithms (GAs) and particle swarm optimization (PSO), alongside ML models, meticulously identifies PCM combinations that optimize thermal efficiency while minimizing costs and environmental impact. This innovative approach not only boosts the thermal effectiveness of TES systems but also aligns with sustainable design principles, marking a forward leap toward a more sustainable, energy-efficient future in thermal management solutions.

6. Conclusion

PCMs are integral to TES and TMA, offering significant benefits across industries by storing and releasing energy to manage grid demands, improve cooling systems, and manage energy spikes. Widely applied in electronic cooling, data center regulation, energy-efficient architectural design, and renewable energy systems, the effectiveness of PCMs is dependent on their reliability and stability. Addressing supercooling and compatibility through additives is crucial for their broader application. ML models have become vital in enhancing PCM performance, aiding in selection, compatibility assessment, supercooling mitigation, and thermal behavior prediction. These models optimize TES system performance and facilitate the discovery of novel PCMs, driving sustainability in engineering systems. The collaboration between PCMs and ML offers potential for significant advancements in energy efficiency and environmental sustainability, marking a promising direction for research and development toward an eco-friendly future across various sectors.

Acknowledgments

This work received no funding.

Conflict of interest

The authors declare no conflict of interest.