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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗
DOE OSTI2026

Origin of Metal-Insulator Transition in Rare-Earth Nickelates

Rare-earth nickelates RNiO3 (R=rare-earth element) undergo coupled structural, metal-insulator, and magnetic changes as temperature is lowered. Because the metal-insulator transition often occurs together with a symmetry-lowering distortion, it is commonly viewed as lattice driven. Here we use QSGW calculations to separate the roles of spin and structure. In the high-symmetry Pbnm phase, imposing spin disproportionation already starts to open an electronic gap, although the undistorted lattice cannot stabilize a full insulating state in both spin channels. In the low-symmetry P21/n phase, removing the spin disproportionation destroys the insulating solution even though the bond disproportionation remains. These tests show that spin disproportionation is the primary electronic driver of gap formation, while structural disproportionation acts as the enabler that allows the inequivalent Ni states to become spatially separated and fully insulating. As an explicit finite-temperature example, machine-learned molecular dynamics for NdNiO3 at 220 K shows that a nominally high-symmetry Pbnm structure dynamically samples local Ni-O bond disproportionation, while retaining Pbnm symmetry on average. This illustrates a general structural channel by which thermal fluctuations in the high-symmetry phase can help stabilize locally spin-disproportionated states.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Genesis of a novel high-rate composite manufacturing process using large-scale additive manufacturing – compression molding (AM-CM) system: Possibilities and limitations

Oak Ridge National Laboratory (ORNL) has developed a highly automated manufacturing process for thermoplastic composites that combines the benefits of Additive Manufacturing and Compression Molding (AM-CM) to produce high-performance functional composite structures at automotive production rates. Here, the AM-CM process creates highly precise preforms by additively placing extruded fiber-filled polymers (with controlled fiber orientations and multi-material configurations) in the desired mold location before undergoing a secondary compression molding process immediately before the preform cools down. Preforms can be in the form of short, long-chopped, or continuous fiber-filled thermoplastic polymers (e.g., CF/GF-filled ABS, PC, LM-PAEK, etc.). The AM-CM process combines the benefits of controlled fiber alignment, that is only achievable in AM-printed parts with the classical CM process, which eliminates porosity and good surface finish. A preform created using AM-CM can integrate various materials to enable additional architectural functionalities, including over-molding, selective stiffening, and the incorporation of electrically or thermally conductive channels. All these advantages come with a fast part production cycle time. The AM-CM process can manufacture multi-material, multi-functional parts in under 3 min, starting from raw material (pellets) to the final product. The novel AM-CM process offers superior microstructural control and enhanced multi-functionality previously unattainable with any other traditional high-rate thermoplastic composite manufacturing method. This work covers the manufacturing concept, system development, materials and applications of AM-CM process in detail.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2025

From bench to biofactory: high-throughput technologies and automated workflows to accelerate biomanufacturing

Microbial production of target molecules has advanced significantly in recent years driven by innovations in enzyme engineering, DNA synthesis, and genomic editing. However, to access the massive potential of microbial production, a vast parametric space remains to be investigated to optimize these biobased processes for a robust bioeconomy. Here, we review the current state of the art, some key challenges and possible solutions. We see a critical role of automation, high-throughput technologies, self-driving and cloud labs, and data management to enable Artificial Intelligence/Machine Learning and mechanistic models to overcome the design space challenges and accelerate the development of novel bio-based solutions. Accurate models will expedite the development and scale-up of engineered microbes for a range of final products from many starting materials.

Petzold, Christopher J↗
DOE OSTI2025

Corrosion of 316 L stainless steel under the natural circulation of molten NaCl-MgCl 2 salt

The corrosion behavior of 316 L stainless steel (SS) was studied via the natural circulation of molten eutectic NaCl-MgCl 2 salt through a microloop. The post-corrosion tested 316 L SS microloop sections were characterized with microscopy techniques to determine the microstructural and microchemical changes that occurred at the alloy/salt interface. It was found that 316 L SS showed heterogeneous dissolution at the hot-leg, whereas deposition of corrosion products occurred at the cold-leg. For the first time, experimentally obtained molten salt flow-induced corrosion of 316 L SS results were combined with computational thermodynamic-kinetic models to validate the dissolution and deposition in terms of elemental compositional changes at the alloy/salt interface. The thermodynamic-kinetic modeling predicted that the heterogeneous dissolution of Cr and Fe from the hot-leg section of 316 L SS persisted throughout the salt circulation. The model also estimated that, despite Cr deposition starting earlier than Fe, the total redeposition of Fe is expected to be significantly greater than that of Cr over the circulation of salt. Furthermore, the modeling accurately predicted the subsurface enrichment of Mo which is attributed to the reduced Cr activity and the relatively higher diffusion rate of Mo within the alloy matrix. Here, the agreement between modeling and experimental results confirms that Fe chlorides dissolve at the hot-leg and subsequently deposit at the cold-leg due to activity changes driven by the thermal gradient. By contrast, Cr was not detected in the cold-leg deposits, which is attributed to its weaker temperature dependence on activity, limiting its redeposition under these conditions.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Characterization of thermally heat-treated polyacrylonitrile carbon fibers

This study investigates the graphitization process of polyacrylonitrile (PAN) carbon fibers by subjecting commercial fibers to thermal heat treatment at temperatures ranging from 1400 to 2100 °C in 100 °C increments, using either argon or nitrogen gas atmospheres. Changes in crystallinity, surface morphology, and lattice parameters were analyzed for two commercial carbon fibers using X-ray diffraction, scanning electron microscopy, and Raman spectroscopy. Results indicated minimal changes in surface morphology with increasing heat-treatment temperature; however, crystallinity significantly increased. Crystallinity changes were more strongly dependent on temperature rather than gas atmosphere or fiber type. At intermediate heat-treatment temperatures (1600–1800 °C), fibers treated in argon showed a slight preference for graphitization. The highest level of graphitization was measured at 2100 °C. Crystallite size increased as the intensity ratio of the D1 to G Raman peaks increased, reaching a peak around ~1800 °C, after which the ratio started to decrease. This behavior aligns with Ferrari's three-stage model of carbon crystallization and is consistent with both the Marie-Mering degree of graphitization and Brubaker's Integrated Absolute Differential models, all of which describe the transformation from an amorphous to a more graphitic structure. At the higher heat-treatment temperatures, the changes between atmospheres and fiber types were measured to converge to similar levels of graphitization. In conclusion, this study evaluates the progressive change in commercial grade carbon fibers when heat-treated.

Characterization↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2025

On the dynamics of the fluoroethylene carbonate generated solid electrolyte interphase on silicon anodes during calendar life aging

Here, the widespread use of silicon (Si)-rich anodes in lithium-ion batteries (LIBs) is impeded by an unstable solid electrolyte interphase (SEI) incurring insufficient cell life. Fluoroethylene carbonate (FEC) additive in the electrolyte significantly improves cycle life. However, the gains on calendar life remain unclear; the SEI structure still undergoes detrimental alterations at rest. Thus, elucidating the SEI dynamics during calendar aging is critical to mitigating time-dependent capacity degradation. ATR-FTIR, XPS, and ToF-SIMS are used herein to investigate the SEI structure before and after calendar aging. Si cycled without FEC exhibits no notable SEI chemistry changes Pre- and Post-aging, leaving poor passivation as the main failure pathway. Conversely, the FEC-SEI starts as short oligomeric species from FEC/EC electroreduction prior to aging; after calendar aging, polymerized carbonates become consistently more prominent. Unexpectedly, the deposition of self-polymerized FEC species results from time exposure to the delithiated Si specifically as opposed to the lithiated surface. This unexpected finding is supported by another recent Si calendar-aging research, which albeit not investigating FEC, finds global failure of the SEI upon delithiation resulting in ∼247 fold more reactive surface compared to the lithiated.

Batteries↗
DOE OSTI2024

Parameter identification methods for low-order gray box building energy models: A critical review

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Component-based SHGC determination of BIPV glazing for product comparison

Building-integrated photovoltaic (BIPV) systems are intrinsically designed to generate electricity and to provide at least one building-related function. When BIPV modules act as glazing products in windows, skylights or curtain walls, their ability to control the transmission of solar energy into the building must be characterised by a Solar Heat Gain Coefficient (SHGC) or g value (also known as Total Solar Energy Transmittance – TSET – or “solar factor”). For the comparison of BIPV glazing products consisting of one PV laminate and possibly further, conventional glazing layers separated by gas-filled cavities, the procedures documented in international standards for architectural glazing (e.g. ISO 9050 and EN 410) form a suitable starting point. Easily implemented modifications to these procedures are proposed to take both optical inhomogeneity (if relevant) and extraction of electricity from BIPV glazing units into account. Geometrically complex glazing and shading devices, and light-scattering glazing layers, are outside the scope of the proposed methodology; SHGC determination for obliquely incident solar radiation is also excluded. For these cases, the experimental calorimetric approach documented in [ISO 19467:2017; ISO 19467-2:2021] is recommended. The paper also presents results and conclusions from an implementation exercise and sensitivity study carried out by participants of the IEA-PVPS Task 15 on BIPV. The cell coverage ratio in the PV laminate, the thermal resistance offered by the glazing configuration, the choice of boundary conditions and the effect of extracting electricity were all identified as parameters which significantly affect the SHGC value determined for a given type of BIPV glazing. A practicable approach to accommodate the great variety of dimensions typical for BIPV glazing is also proposed. These findings should pave the way for modifying the existing component-based standards for architectural glazing to take the specific features of BIPV glazing into account.

14 SOLAR ENERGY↗
DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Techno-Economic Analysis of Recycling Strategies for Catalyst and Acid During Catalytic Graphitization

With the aim of meeting the urgent demand for active anode materials (AAM) in energy storage systems, bio-based graphite (biographite) emerges as an affordable solution to de-risk the turbulent supply chain of critical minerals. Anode grade biographite requires high crystallinity and purity, which can be achieved by catalytic graphitization with iron, followed by acid washing. Therefore, a well-conceived process integration that recycles catalyst can be the starting point to commercialization. This study evaluates closed-loop catalyst recovery, and byproducts valorization scenarios through a technoeconomic framework to help understand the scale-up potential of biographite. For the acid washing, three reactors in series meet the required biographite purity at 99.95%. Iron and acid recovery can reduce material consumption and waste generation by ~95%, albeit at the expense of ~80% increase in capital costs. Recovery scenarios present similar capital and operational expenses, yielding minimum selling prices (MSP) near $6 kg-1 of biographite. Monte Carlo methodology reveals that feedstock price accounts for ~60% of MSP variance, followed by plant capacity ~20%. The likelihood of reaching a competitive profit margin of 30% in the U.S. AAM market sits at 85% average for recovery scenarios, and 103% when iron oxide is sold as byproduct. Additionally, an IRR >= 15% can be achieved for half of Monte Carlo simulations, representing promising early-stage results. Biographite production offers a strategic pathway to stabilize the anode market beyond China by integrating established technologies for a scalable, economically viable, and sustainable process. The role of catalyst recovery and byproducts utilization is critical for advancing the biomaterials industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A collective trigger for widespread planetesimal formation revealed by accretion ages

The formation of planetesimals was an integral part of the cascading series of processes that built the terrestrial planets. To illuminate planetesimal formation, here we develop a refined thermal evolution model to calculate the formation ages of meteorite parent planetesimals. This model includes chemical reactions and phase changes during heating, as well as natural variations in the proportions of the constituent phases of these planetesimals. We find that the parent bodies of non-carbonaceous (NC) and carbonaceous (CC) iron meteorites start forming at very similar times ( ∼ 0.95 Myr after calcium-aluminium-rich inclusion [CAI] formation) and occupy overlapping time windows. NC and CC chondrite parent bodies formed later during non-overlapping periods. We combine these ages with proportions of isotopic end-members we recover from mixing models to construct records of motion throughout the protoplanetary disk. These records argue that NC and CC material traversed the barrier in the disk after ∼ 0.95 Myr after CAI formation. The onset of this motion coincided with planetesimal formation, indicating that the phenomenon that drove motion also triggered planetesimal formation. We argue that this feature also served as the semi-permeable barrier in the disk. Although its identity is uncertain, the effects this phenomenon had on the timing of planetesimal formation and motion through the disk can now serve as constraints on models of disk evolution. Models that reproduce these effects would elucidate the nature and implications of this phenomenon, which is key to unlocking a holistic model of terrestrial planet building.

Astronomy and AstroPhysics↗
DOE OSTI2025

Sparse chronology strategy for integrating seasonal energy storage in capacity expansion models

Here, this study develops the sparse chronology method to enhance the representative period framework in capacity expansion models, enabling the effective integration of long-duration energy storage modeling. Traditional representative period methods cannot capture the state of charge of seasonal energy storage systems because they do not establish effective inter-day linkages to connect the state of charge between periods. The sparse chronology approach addresses this limitation by establishing inter-day linkages that allow state of charge to shift inter-seasonally. At the same time, it groups identical representative days into partitions, applying constraints sparsely and implicitly to reduce computational load further. Validation results demonstrate that this method successfully simulates long-duration energy storage patterns, achieving close alignment with a continuous yearly benchmark model, with seasonal trends and state of charge cycles clearly represented. The computational load analysis reveals that the sparse chronology method efficiently applies constraints on maximum and minimum state of charge limits within the representative day framework, eliminating the need for detailed constraints on each individual day. By partitioning representative days and constraining only the start and end of each partition, the method significantly decreases computational requirements. Simulation results show that sparse chronology closely approximates the continuous yearly method's accuracy, even with as few as 20 representative days, achieving correlation values with the benchmark of nearly 0.9 in state of charge plots. Furthermore, it maintains computational efficiency, requiring only 4 % of the solver time compared to the continuous yearly method with 20 representative days. This approach allows capacity expansion models to incorporate long-duration energy storage with high temporal, spatial, and technological resolution, enabling more detailed modeling for large-scale power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Flow dynamics and heat transfer in simplified battery energy storage systems with heated battery modules

Large-scale energy storage systems (ESSs) composed of batteries show promise in addressing current energy challenges, but dissipation of generated heat is important. Here, this paper focuses on buoyant convective flows in simplified ESS battery racks. Natural convection is not generally the primary cooling strategy but can be important in abnormal scenarios where there is module overheat or potentially thermal runaway. We use computational fluid dynamics to investigate the flow dynamics and heat transfer mechanisms in a simplified parameterized rack design. Despite its simplicity, this configuration produces many of the relevant features expected in real ESSs without details of module geometry or hardware, allowing broad conclusions independent of manufacture-specific designs. We start by providing visualizations of the flowfield and measurements of entrainment, heat flux, and pressure. To characterize the dependence on the system parameters, we develop an integral-scale analysis of the average temperature equation to highlight the dominant source terms. We use results from this analysis to derive a steady network model composed of simple algebraic expressions to provide first-order predictions of entrainment through the rack. The network model leads to a linear scaling of the Reynolds number based on convective mass flux with respect to the Grashof number based on the heat source. We deduce empirical relationships that relate the heat exchanged between modules using a surface-averaged Nusselt number as a function of the local Reynolds and Rayleigh numbers. Lastly, we investigate how space between the modules and rack in the spanwise direction creates flow bypass, resulting in different flow pathways.

Battery thermal management↗