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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Renewable Thermal Hybridization Framework for Industrial Process Heat Applications

Renewable thermal energy systems (RTES) could be hybridized with different renewable options (e.g., flat plate collectors (FPCs) with parabolic collectors), or combined with existing heat supplies (e.g., fossil fuels), to give options for targeted solar IPH applications, industrial decarbonization and the reduction of fuel consumption. Buildings and industrial thermal energy applications require different temperature ranges, quantities, and rates of thermal energy, and as such require flexible, cost competitive solutions, able to provide heat over various temperature ranges. Hybrid solutions and thermal energy storage (TES) will be important for the dispatch of heat at optimal times needed by the demand side of the buildings and industrial applications. A variety of tools and platforms, such as the System Advisor Model (SAM), can provide hourly thermal yield simulations from single renewable energy (RE) technology options, including FPCs or CSP for solar IPH. We have investigated a variety of approaches to hybrid system modeling for RTES at different temperatures or combinations of technologies and developed an initial framework. The hybridization framework starts by creating a heat stream and raising the temperature of that heat stream by various combinations of RE technologies and other sources such as fossil fuels, new fuels or electric heating in multiple stages, with options for TES and/or waste heat recovery (WHR).

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Interpreting T-History Data on Supercooling and Phase Change Enthalpy of a Salt-Hydrogel Complex for Building Thermal Management

Phase change materials (PCMs) are promising materials for storing and discharging latent heat in buildings to regulate the thermal environment. Measurement of salt hydrate PCM thermal performance is often challenging because of supercooling and phase segregation. Though differential scanning calorimetry (DSC) has been widely used to measure thermal properties of various phase change materials, DSC uses milligrams of sample and is unrealistic for predicting large-scale materials performance. The T-history method [1], in contrast, handles higher volumes but the analysis typically requires a lumped capacitance assumption. Here, we report and analyze T-History data on a novel salt-hydrogel complex with high viscosity, that renders low Biot number sample preparation extremely challenging. We re-examine the standard T-history method and compared against DSC data in this complex material. We report details of an experimental setup, built with necessary additions to a controlled environmental chamber. We carefully examine the validity of the lumped capacitance assumption and suggest a modified analysis using a computational approach for reliable data. Experimental parameters such as ramp rate and temperature range of the environmental chamber are considered via computational modeling to understand the effect of ramp rate on supercooling [2]. Finally, we report thermal cycling experiments on the salt-hydrogel complex. Our modified approach to measuring supercooling and enthalpy of fusion at large scales is important in better understanding the performance of phase-change materials at scale for building thermal storage. References: [1] Marin, Jose M, Belen Zalba, Luisa F Cabeza, and Harald Mehling. “Determination of Enthalpy Temperature Curves of Phase Change Materials with the Temperature-History Method: Improvement to Temperature Dependent Properties.” Measurement Science and Technology 14, no. 2 (February 1, 2003): 184–89. https://doi.org/10.1088/0957-0233/14/2/305. [2] Safari, A., R. Saidur, F.A. Sulaiman, Yan Xu, and Joe Dong. “A Review on Supercooling of Phase Change Materials in Thermal Energy Storage Systems.” Renewable and Sustainable Energy Reviews 70 (April 2017): 905–19. https://doi.org/10.1016/j.rser.2016.11.272

thermodynamics↗
DOE OSTI2020

Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions

We consider the problem of optimal feedback control of an unknown, noisy, time-varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature fluctuations. In this paper, we present an approach that applies to systems of practical interest. The method presented here is model independent; does not require knowledge of the objective function; is robust to measurement noise; is applicable for any set of initial conditions; is applicable to simultaneously controlling an arbitrary number of parameters; and may be implemented with a broad range of continuous or discontinuous functions such as sine or square waves. For systems with convex cost functions we prove that our algorithm will produce controllers that approach the minimal cost. For linear systems we reproduce the cost minimizing linear quadratic regulator optimal controller that could have been designed analytically had the system and cost function been known. We demonstrate the effectiveness of the algorithm with simulation studies of noisy and time-varying systems.

42 ENGINEERING↗
DOE OSTI2025

Sieving Hydrogen Isotopes via Machine Learning Assisted Chemical Vapor Deposition (CVD) of High‐Quality Monolayer Hexagonal Boron Nitride (h‐BN) on Iron Foils

Atomically thin two-dimensional (2D) ceramics, such as monolayer hexagonal boron nitride (h-BN), present potential for disruptive advances in separations. However, sub-atomic scale separation of hydrogen isotopes (H + /D + ) require near pristine 2D material membranes, and scalable synthesis of such high-quality h-BN comparable to mechanically exfoliated crystals remains a significant challenge. Here, we report a scalable Fe-catalyzed chemical vapor deposition (CVD) process for bottom-up synthesis of large-area, high-quality monolayer h-BN films, overcoming key limitations of conventional ammonia-based routes. By leveraging mechanistic insights and higher CVD temperatures, we suppress multilayer formation and achieve uniform monolayer h-BN coverage on commercially available Fe foils. Machine learning enables systematic exploration of the complex, multi-dimensional CVD parameter space (growth time, temperature, precursor temperature, multilayer faction, coverage), providing data-driven approaches to visualize and identify process regimes facilitating predominantly monolayer h-BN growth with minimal secondary nuclei/ad-layers. The optimized Fe-catalyzed CVD h-BN membranes show high-quality as observed by proton/deuteron (H + /D + ) selectivity ≈8.45, approaching the highest quality benchmark of mechanically exfoliated h-BN (H + /D + selectivity ≈10) as well as significantly outperforming Cu-catalyzed CVD h-BN membranes (H + /D + selectivity ≈3.62, control selectivity ≈1.7). Our work provides a scalable cost-effective route for high-quality monolayer h-BN synthesis for sub-atomic scale separations (H + /D + ) and demonstrates the broader potential of machine learning-guided optimization of CVD for advancing synthesis of 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High‐Performance Rechargeable Lithium‐Chlorine Batteries with ALD Conformal Starburst Porous Graphene Positive Electrodes

Abstract Rechargeable alkali metal‐chlorine batteries are emerging as a promising high‐energy‐density solution. However, they confront significant challenges, including the primary issue stemming from the weak binding affinity of cathode materials for Cl 2 , which leads to a sluggish and inadequate supply of Cl 2 during the redox reactions, resulting in a shortened cycle life and low Coulombic efficiency (CE), particularly when operating at ultrahigh specific capacity outputs. Herein, an Al 2 O 3 ‐skinned heterostructured starburst porous graphene with conformal metasurfaces (Al 2 O 3 @rGO) is reported, crafted from a hierarchical porous starburst graphene arranged in a unique layered structure by the PTFE microemulsion skin effect, leveraging subsequent fluidized bed atomic layer deposition (FBALD) of Al 2 O 3 groups. Al 2 O 3 @rGO features superhydrophilicity, effective adsorption, fast kinetics from stable dynamic respiratory interface, high electrical and thermal conductivity anisotropy, intelligent thermal management and safe operation over a wide temperature range. Consequently, the Li‐Cl 2 @Al 2 O 3 @rGO battery achieves an ultrahigh discharge specific capacity of 5000 mAh g −1 at ≈100% CE, and even delivers stable cycling over 200 cycles with 2000 mAh g −1 at an average CE of 99.8% under low temperature environment of ‐40 °C. The scalable heterostructure approach offers a sustainable perspective of the development of functionalized metamaterials and metasurfaces for next‐generation safe and energy‐dense batteries and broader applications.

Chemistry↗
DOE OSTI2023

In situ probing of interfacial roughness and transient phases during ceramic cold sintering process

The ceramic cold sintering process (CSP) offers an eco-friendly approach to producing fully dense ceramics at low temperatures. However, an incomplete mechanistic understanding hinders its optimization and widespread adoption. In this study, we analyze the microstructural and structural changes in ZnO, a model CSP system, using in situ synchrotron-based high-energy small-angle X-ray scattering and X-ray diffraction techniques. Our results reveal the time evolution of ZnO particles' surface area and roughness, reflecting the dissolution and re precipitation processes that enable densification. The in situ measurements supply valuable kinetic data for these stages of CSP. Alongside microstructural changes and densification, we observed the evolution of secondary phases representing reaction products between ZnO and acetic acid, the solvent used. The initial ZnO/solvent mixture's dominant secondary phase is attributed to zinc acetate, which is gradually replaced by a zinc soap-type structure during CSP. This structure has a large (≈ 21 Å) lattice parameter and is assumed to have a layered nature. The formation of this soap phase, which is retained in the sintered product as an intergranular component, appears to be a signature of successful cold sintering as it facilitates mass transport, leading to densification. Here, our study underscores the potential of in situ synchrotron characterization for revealing microstructural and phase-evolution details during CSP. These findings, which would be challenging to obtain through ex situ measurements, provide crucial data to guide and validate theoretical models, ultimately enhancing CSP's effectiveness and adoption.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
DOE OSTI2024

Improved thermoelectric performance of α- and β- Cu 2 Se through suppression of hole density using extrinsic copper vacancies

Modulating Cu + ion disorder in Cu 2 Se can enable control over the polymorphism and the carrier density leading to enhanced thermoelectric properties for both α- and β-Cu 2 Se. Here we report that the incorporation of Cr 3+ into the Cu 2 Se crystal lattice facilitates the stabilization of α-Cu 2 Se at 300 K leading to a large (~140%) reduction in the carrier density both below and above the phase transition. This is attributed to the reduction in the density of intrinsic copper interstitials (Cu$^{•}_{i}$) within the Cu (2-δ-λ) (Cr$^{··}_{Cu}$)λ(V$^{'}_{Cu}$)δ(Cu$^{π}_{i}$) δ-2λ (h • ) δ-2λ Se crystal lattice. Such optimization of the carrier density led to a large (63%) increase in the thermopower and a drastic (46%) reduction in the total thermal conductivity for both α- and β-Cu 2 Se matrices. Consequently, a significant enhancement of the thermoelectric performance is observed in the entire temperature range from 300 K to 773 K. This results in high average ZT values for both α-Cu 2 Se (ZT ave = 0.60) and β-Cu 2 Se (ZT ave = 0.97), which paves the way for both near room temperature and high temperatures applications. Furthermore, this work provides a new approach to optimize the thermoelectric performance of Cu 2 Se-based materials by leveraging the interaction between mobile intrinsic Cu$^{·}_{i}$ and extrinsic V$^{'}_{Cu}$ to suppress the hole density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗
DOE OSTI2023

Systematic mapping of the conformational landscape and dynamism of soluble fibrinogen

Background: Fibrinogen is a soluble, multisubunit, and multidomain dimeric protein, which, upon its proteolytic cleavage by thrombin, is converted to insoluble fibrin, initiating polymerization that substantially contributes to clot growth. Fibrinogen contains numerous, transiently accessible “cryptic” epitopes for hemostatic and immunologic proteins, suggesting that fibrinogen exhibits conformational flexibility, which may play functional roles in its temporal and spatial interactions. Hitherto, there have been limited integrative approaches characterizing the solution structure and internal flexibility of fibrinogen. Methods: Here, utilizing a multipronged, biophysical approach involving 2 solution-based techniques, temperature-dependent hydrogen-deuterium exchange mass spectrometry and small angle X-ray scattering, corroborated by negative stain electron microscopy, we present a holistic, conformationally dynamic model of human fibrinogen in solution. Results: Our data reveal 4 major and distinct conformations of fibrinogen accommodated by a high degree of internal protein flexibility along its central scaffold. We propose that the fibrinogen structure in the solution consists of a complex, conformational landscape with multiple local minima. This is further supported by the location of numerous point mutations that are linked to dysfibrinogenemia and posttranslational modifications, residing near the identified fibrinogen flexions. Conclusion: Further, this work provides a molecular basis for the structural “dynamism” of fibrinogen that is expected to influence the broad swath of its functionally diverse macromolecular interactions and fine-tune the structural and mechanical properties of blood clots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Uncertainty quantification (UQ) in nuclear reactors for transients is directly linked with safety assessment through the cross-sections uncertainties, provided as a covariance matrix, which are propagated through the reactor system to output of interest pertaining to reactor safety, such as peak temperatures in fuel/clad/coolant. Using a two-step approach, uncertainties are first quantified and propagated from basic input variables (such as reaction cross-sections) to intermediate quantities (such as reactivity feedback coefficients) through lattice level calculations. Uncertainties of intermediate quantities (from the first step) are then propagated through the system transient calculations, in the second step, to obtain uncertainties on reactor safety output parameters of interest. The scope of this work consists of Uncertainty Quantification & Propagation of nuclear data uncertainties that are highly correlated through unprotected transient overpower and unprotected loss of flow to assess their impact on core safety parameters. This two-step approach in the presence of covariance renders the sensitivity analysis very challenging. In fact, usually the sensitivity analysis is restricted to each step, which limits its application since the sensitivities between the system output quantities and the basic input variables are difficult to obtain. Here, in this work, we address this issue by proposing a simple, general methodology to combine the sensitivity indices obtained in each step by assuming the model behavior being linear. For the first step Generalized Perturbation theory based indices are used while in the second step the recently studied Johnson indices. The uncertainty quantification and sensitivity methodologies discussed here are demonstrated on a generic LFR design which is based on the 500 MWth demonstration Lead-cooled fast reactor (DLFR) using oxide fuel, developed by Westinghouse Electric Company (WEC).

42 - ENGINEERING↗
DOE OSTI2025

A review of Geological Thermal Energy Storage for seasonal, grid-scale dispatching

Energy storage is essential for the decarbonization of the U.S. energy grid, especially with the increasing deployment of variable renewable energy sources like solar and wind. Geological thermal energy storage (GeoTES) has emerged as a promising long duration, grid scale solution, providing stability and security through flexible operations and valuable grid services. GeoTES utilizes subsurface reservoirs to store thermal energy for power generation and direct-use heating and cooling. This approach significantly enhances the use of low-temperature reservoirs, which would otherwise be unsuitable for geothermal power plants. It also aligns well with depleted oil and gas reservoirs, concentrating solar power, non-flexible renewables (photovoltaic and wind), and geothermal-related power cycles. Given the favorable marginal costs of GeoTES as storage duration increases, it becomes particularly competitive for seasonal, grid-scale dispatch, where few technologies are viable. This paper provides a comprehensive review of GeoTES systems and the research underpinning itsr development. This analysis begins by defining and categorizing the unique characteristics of thermal energy storage techniques, setting GeoTES apart from other technologies. The various components, configurations, subsurface characteristics, and modeling efforts that guide GeoTES development are then explored. Finally, challenges in GeoTES research, development, and deployment are discussed, along with mitigation strategies and lessons from related technologies. Beyond their economic benefits, GeoTES systems support grid resilience and decarbonize industrial processes. Their scalability, broad distribution, seasonal storage potential, and flexible dispatch capacity make GeoTES a valuable tool for expanding renewable energy deployment and addressing climate change.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2023

The transition from field emission to collisional space-charge limited current with nonzero initial velocity

Abstract Multiple electron emission mechanisms often contribute in electron devices, motivating theoretical studies characterizing the transitions between them. Previous studies unified thermionic and field emission, defined by the Richardson-Laue-Dushman (RLD) and Fowler–Nordheim (FN) equations, respectively, with the Child-Langmuir (CL) law for vacuum space-charge limited current (SCLC); another study unified FN and CL with the Mott-Gurney (MG) law for collisional SCLC. However, thermionic emission, which introduces a nonzero injection velocity, may also occur in gas, motivating this analysis to unify RLD, FN, CL, and MG. We exactly calculate the current density as a function of applied voltage over a range of injection velocity (i.e., temperature), mobility, and gap distance. This exact solution approaches RLD, FN, and generalized CL (GCL) and MG (GMG) for nonzero injection velocity under appropriate limits. For nonzero initial velocity, GMG approaches zero for sufficiently small applied voltage and mobility, making these gaps always space-charge limited by either GMG at low voltage or GCL at high voltage. The third-order nexus between FN, GMG, and GCL changes negligibly from the zero initial velocity calculation over ten orders of magnitude of applied voltage. These results provide a closed form solution for GMG and guidance on thermionic emission in a collisional gap.

42 ENGINEERING↗
DOE OSTI2024

Measurements of nematic susceptibility with phase sensitive nuclear magnetic resonance in pulsed strain fields

Here, we present nuclear magnetic resonance data in BaFe 2 As 2 in the presence of pulsed strain fields that are interleaved in time with the radio frequency excitation pulses. In this approach, the preceding nuclear magnetization acquires a phase shift that is proportional to the strain and pulse time. The sensitivity of this approach is limited by the homogeneous decoherence time, T 2 , rather than the inhomogeneous linewidth. We measure the nematic susceptibility as a function of temperature and demonstrate a three orders of magnitude improvement in sensitivity. This approach will enable studies of the strain response in a broad range of materials that previously were inaccessible due to inhomogeneous broadening.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy, nanoscale skyrmions are observed in the [Pt/Co/Ir] 15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Néel-type skyrmions and the bobber-like Néel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Néel-type skyrmions can be effectively reduced by the spin-orbit torques, which leads to the formation of 20 nm Néel-type skyrmions. In conclusion, our findings could be important for understanding the formation dynamics of nanoscale Néel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Milky Way white dwarfs as sub-GeV to multi-TeV dark matter detectors

Here, we show that Milky Way white dwarfs are excellent targets for dark matter (DM) detection. Using Fermi and H.E.S.S. Galactic center gamma-ray data, we investigate sensitivity to DM annihilating within white dwarfs into long-lived or boosted mediators and producing detectable gamma rays. Depending on the Galactic DM distribution, we set new constraints on the spin-independent scattering cross section down to 10 -45 -10 -41 cm 2 in the sub-GeV DM mass range, which is multiple orders of magnitude stronger than existing limits. For a generalized NFW DM profile, we find that our white dwarf constraints exceed spin-independent direct detection limits across most of the sub-GeV to multi-TeV DM mass range, achieving sensitivities as low as about 10 -46 cm 2 . In addition, we improve earlier versions of the DM capture calculation in white dwarfs, by including the low-temperature distribution of nuclei when the white dwarf approaches crystallization. This yields smaller capture rates than previously calculated by a factor of a few up to two orders of magnitude, depending on white dwarf size and the astrophysical system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Current-based metrology with two-terminal mesoscopic conductors

The traditional approach to quantum parameter estimation focuses on the quantum state, deriving fundamental bounds on precision through the quantum Fisher information. In most experimental settings, however, performing arbitrary quantum measurements is highly unfeasible. In open quantum systems, an alternative approach to metrology involves the measurement of stochastic currents flowing from the system to its environment. However, the present understanding of current-based metrology is mostly limited to Markovian master equations. Considering a parameter estimation problem in a two-terminal mesoscopic conductor, we identify the key elements that determine estimation precision within the Landauer-Büttiker formalism. Crucially, this approach allows us to address arbitrary coupling and temperature regimes. Furthermore, we obtain analytical results for the precision in linear-response and zero-temperature regimes. For the specific parameter estimation task that we consider, we demonstrate that the boxcar transmission function is optimal for current-based metrology in all parameter regimes.

Landauer formula↗
DOE OSTI2021

Multiscale Electrothermal Design of a Modular Multilevel Converter for Grid-Tied Applications

As a key feature of modular multilevel converters (MMCs), a large number of semiconductor devices are employed in the converter and distributed over a stack of submodules. Each submodule has a strict temperature limit that imposes constraints on the operating range of the converter. Different loading conditions/losses in the submodules lead to unavoidable temperature variations inside the MMC, which consequently affect the system-level performance and reliability. This paper is focused on the electrothermal analysis and design of a medium-voltage silicon carbide (SiC)-based MMC system, from submodule power semiconductors to the overall MMC system integration of multiple submodules. Loss calculations are performed to estimate the cooling requirements and aid thermal design at different levels. The performance of forced-air cooling approach is analyzed within a numerical modeling framework. Maximum temperature of the SiC power modules is predicted using numerical tools. Thermal design of the MMC cabinet with various arrangement of air inlet(s) and outlet(s) is investigated and compared from a cooling performance perspective.

CFD↗