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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2023

Characterizing the Impact of Oligomerization on Redox Flow Cell Performance

Abstract Redox flow batteries (RFBs) are hindered by complex failure modes, particularly crossover through the membrane, resulting in capacity fade and reduced cycling efficiencies. Redox‐active oligomers (RAOs) have recently been proposed for mitigating this phenomenon while maintaining sufficient transport properties; however, to date, few studies have quantified how the chemical and electrochemical properties of RAOs influence their performance in redox flow cells. Here, we demonstrate that oligomeric derivatives of 2,2,6,6‐tetramethylpiperidine 1‐oxyl (TEMPO) exhibit lower diffusivities than the monomeric species but retain facile charge transfer characteristics. The size‐dependent variations in mass transport rates directly translate to differences in flow cell polarization and symmetric cycling performance. Post‐mortem analyses reveal that oligomerization does not meaningfully alter decay processes as evinced by similar capacity fade across all species. Broadly, these findings corroborate and extend upon previously developed relationships between molecular size, electrochemical properties, and flow cell performance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Two‐Membrane Dual Non‐Aqueous/Aqueous Electrolyte Flow Cell Operation for Electrochemical Conversion of CO 2 to Methyl Formate

Recently, tandem cathodic reactions have been demonstrated in non-aqueous solvents to couple CO 2 reduction to a secondary reaction to create novel species that are not produced in aqueous CO 2 electrolysis. One reaction that can be performed with high selectivity and durability is the electrochemical conversion of CO 2 to formic acid and in-situ esterification with methanol to produce methyl formate. However, the translation to a high-performance flow electrolyzer is far from trivial, as the non-aqueous catholyte leads to reactor challenges including flooding the gas diffusion electrode. Here, in this study, a two-membrane flow electrolyzer with both anion and cation exchange membranes was used with flowing methanol catholyte and aqueous anolyte. This design prevented methanol from flooding the cathode, which was a pervasive limiting issue for electrolyzers with a single membrane. Methyl formate production at 42.9 % faradaic efficiency was achieved with pure methanol in a flow electrolyzer with stable performance beyond 80 min. However, low-water-content catholyte compositions also led to increased cell resistance and lower operating current densities. Thus, with the present ionomer materials there is a tradeoff between methyl formate selectivity and current density depending on water concentration, highlighting a need for new ionomers tailored for desirable non-aqueous solvents such as methanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗
DOE OSTI2026

Relaxations of the steady optimal gas flow problem for a non-Ideal gas

Natural gas ranks second in U.S. primary energy consumption. Because most production sites are remote, gas must be transported through pipeline networks equipped with compressors, valves, and other components. For both economic efficiency and system reliability, it is desirable to operate these networks optimally. The governing physics across pipeline components entails nonlinear, non-convex equality and inequality constraints, and the most general steady-flow operations problem is a Mixed-Integer Nonlinear Program (MINLP).This work focuses on one such steady-flow problem-the Optimal Gas Flow (OGF) for a natural gas pipeline network-which minimizes production cost subject to the steady-flow physics. For day-to-day operations, the ability to quickly compute a globally optimal solution and a strong lower bound for varying demand profiles is crucial. A promising strategy is to build tight relaxations of the OGF’s nonlinear constraints. However, many nonlinearities arising from non-ideal equations of state either lack relaxations or have relaxations that do not scale to realistic network sizes. We address this gap by combining recent advances in polyhedral relaxations for univariate functions to construct tight, computationally efficient relaxations of the OGF with a non-ideal equation of state. These relaxations solve within seconds on a standard laptop. In conclusion, we demonstrate their quality through extensive numerical experiments on very large-scale test networks from the literature and find that the proposed approach proves optimality in 92% of tested instances.

03 NATURAL GAS↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2024

Effect of CO 2 on the water slip flow at silica surfaces for nanometer slit pores of talc

In our previous study, Molecular Dynamics (MD) simulated water flow with no dissolved gas in nanometer slit pores of the hydrophobic talc surfaces revealed a slip length of about 0.5 nm, which is close to the size of “water exclusion zone” but much less than experimental slip length. Atomic Force Microscopy (AFM) imaging confirmed the presence of pancake shape nanobubbles at the hydrophobic talc (001) surface (size from tens to hundreds of nanometers). The effect of CO 2 on water slip flow in talc nanopores is reported to reveal the importance of dissolved gas for confined fluid flow. MD simulated 7 nm CO 2 nanobubbles were found to attach and spread at the talc (001) surface, consistent with the AFM observation. Simulated CO 2 saturated water flow in the slit pores of talc (001) surfaces predicted the generation of CO 2 nanobubbles at the end of the slit pore, due to the stabilization of a CO 2 film at the talc (001) surfaces and a water barrier at the edge surfaces of talc. A “critical thickness” of about 1.5 nm was found for CO 2 nanobubbles to be stabilized at the end of the talc (001) surface. With 1.5 nm CO 2 nanobubbles at the talc (001) surface of a 6 nm slit pore, a water slip length of 1.8 nm was determined. Furthermore, the nanometer slit pore simulations with dissolved CO 2 in water revealed an increased slip length. It is expected that an increased slip length value would be found with an increased CO 2 film thickness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
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↗
DOE OSTI2026

Theoretical background for a fast flow liquid metal divertor experiment

Tokamak Energy has developed a liquid metal experiment featuring a lithium loop that circulates lithium through the HIDRA stellarator at the University of Illinois at Urbana-Champaign. The loop includes a replaceable divertor module, designed to demonstrate fast, steady, open-surface liquid metal flow. The first-generation divertor module was deliberately kept simple—an open-surface chute—to enable modelling validation and establish a benchmark for future design iterations. This paper presents the theoretical foundations of the experimental design. Specifically, we identify module overflooding as the primary challenge and determine the limits of fluid velocity and magnetic field strength necessary to prevent it. The steady-state flow patterns are expected to exhibit relatively slow surface velocities, which do not fully align with the fast-flow concept. Nevertheless, the experiment is designed to achieve controllable, continuous open-surface flow within an operational fusion device, representing a significant step toward the development of liquid metal divertor technology.

Experiment↗
DOE OSTI2025

Experimental investigation of flow distribution in enhanced geothermal systems with deep eutectic solvent

Geothermal energy has been recognized as a valuable alternative to fossil fuels and nuclear power, as it is renewable and reliable. Enhanced Geothermal Systems (EGSs) have the potential to expand geothermal energy production by enabling access to previously untapped geothermal resources. Geothermal short-circuiting poses a significant challenge to EGS development, leading to reduced heat extraction. Deep Eutectic Solvent (DES) exhibits favorable thermal and rheological properties, making it a candidate for geothermal applications. Here, this paper examines Choline Chloride-Based Deep Eutectic Solvent (DES) as a working fluid in geothermal applications and its potential to mitigate geothermal short-circuiting. Hydraulic experiments using a dual fracture flow loop were conducted at high temperatures. The results showed that DES exhibited higher differential pressure behavior compared to water. Flow distribution results revealed that DES enhances flow allocation within the small fracture, particularly when a temperature difference exists between fractures. Specifically, DES increased flow distribution by an average of 11% when the temperature difference was 85°C, and by 13% when the difference was 45°C, relative to water. These findings suggest that DES responds to thermal fracture differences, making it a potential remedy to address geothermal short-circuiting.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Temperature statistics in a radiatively heated particle-laden turbulent square duct flow

Radiation absorption by preferentially concentrated particles in a turbulent square duct flow is studied experimentally. The particle-laden flow is exposed to near-infrared radiation, and the gas phase temperature statistics are measured along the wall bisector of the duct. It is found that the instantaneous temperature fluctuations are comparable to the overall mean temperature rise. The temperature statistics at the duct centerline and near the wall are qualitatively different. The former reflects preferential concentration in isotropic flows while the latter displays evidence of particle clustering into streamwise elongated streaks. Comparison of the experimental data to a simplified heat transfer model suggests that the Lagrangian evolution of particle clusters and voids, and turbulent mixing in the vicinity of particle clusters, are important. This work was motivated by particle solar receiver technology, but the findings are also relevant to systems where there is localized heat release or mass transfer from disperse particles or droplets. It shows that obtaining Lagrangian histories of particle trajectories is an important next step towards understanding thermal transport phenomena in particle-laden turbulent flows.

42 ENGINEERING↗
DOE OSTI2020

Elucidating the Impact of Flow on Material-Sensitive Critical Heat Flux and Boiling Heat Transfer Coefficients: An Experimental Study with Various Materials

In this work, steady-state internal flow boiling critical heat flux (CHF) experiments were carried out at 10°C inlet subcooling and atmospheric pressure (84 kPa at an altitude of about 5300 feet) with mass flux ranging from 200 to 2,000 kg/m 2 -s on seven tube materials, including the accident-tolerant fuel cladding candidate FeCrAl (C26M) alloy. At a relatively low flow rate, the sensitivity of CHF to different materials was appreciable (the average difference among the tested materials was 19% at 200 kg/m 2 -s). As the absolute CHF differences stayed constant for higher flow rates, the increased flow rate led to reduced relative CHF differences among the tested materials (average relative difference of 15% at 1,000 kg/m 2 -s). FeCrAl alloy claddings that were oxidized under simulated Light Water Reactor (LWR) conditions yielded CHF comparable to as-received FeCrAl alloy cladding, with less than 1.8% difference. Experimental CHF results imply marginal material sensitivity impacts on the departure from nucleate boiling ratio in steady-state nuclear reactor cores. Yet, for accident progression analysis, material sensitivity may hold importance, as boiling behavior variation at low mass flux is appreciable. Macroscopic material parameters (surface wettability, surface roughness, and thermal properties) do not provide a suitable explanation for observed material-sensitive CHFs. Yet, surface roughness and thermal effusivity revealed power and linear relations, respectively, to the nucleate boiling heat transfer coefficients at low mass flux. At high mass flux, material sensitivity on nucleate boiling heat transfer significantly decreased due to the increased shear stress on boiling surface overwhelming material-related boiling implications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Eulerian three-fluid flow framework for the study of fuel dispersal behavior under loss-of-coolant accident conditions

As the nuclear industry explores new fuel designs to accommodate increased burnup, studying fuel behavior during loss-of-coolant accidents is essential for ensuring the safe operation of light-water reactors. Here, this study presents a computational model to simulate the intricate three-phase flow dynamics of fuel dispersal following cladding breach, where solid fuel particles and carrier fission gases interact with the surrounding liquid or gas medium. The model utilizes a Eulerian three-fluid framework to depict the phase interaction within interpenetrating continua, treating fragmented fuel as granular material. Closure relationships for bulk-flow properties of the solid phase and detailed interfacial exchange terms in high-temperature, high-speed gas-solid-liquid flow scenarios are proposed. The model was validated using adiabatic experimental results in the literature, focusing on characterizing fuel dispersal behavior during loss-of-coolant accidents conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched probability density functions reported in experimental studies. Key highlights of this study include the theoretical description of a coupled three-phase flow with phase change, interpretation of the high-pressure boundary conditions and insights into the transient behavior of gas-solid-liquid phase dynamics during a simulated high-speed dispersal event.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Continuum Correlations from CFD-DEM Modeling of Conduction Heat Transfer in Granular Flows

Heat transfer between a surface and flowing particles is analyzed to improve the accuracy of continuum models for wall-to-bed heat transfer in a fluidized bed. Discrete element modeling (DEM) is used to model a fluidized bed heat exchanger where heat enters the system through a heated wall. The DEM heat transfer predictions are validated against published experimental work (Brewster et al., 2024) with less than 15% error. In previous work by Morris et al. (2015), a continuum model was developed using data from high-fidelity DEM simulations of chute flows. In the current study, the continuum model is extended and validated for fluidized beds. The sensitivity of the continuum heat transfer model parameters, which was not quantified in previous studies, is also investigated. It is observed that for a given particle with specific properties, e.g. the particle size, roughness, and conduction lens radius, the continuum correlation developed for heat transfer from a heated boundary to the particle bed depends mainly on the solid fraction or porosity of the particle bed for a given fluid. The new continuum heat transfer model is then validated over a wide range of superficial velocities via comparisons to both discrete element and experimental data. It is shown that this correlation is valid for a large range of particle flow conditions from chute flows to fluidized beds with less than 10% error as compared to DEM predictions.

14 SOLAR ENERGY↗
DOE OSTI2022

Revealing melt flow instabilities in laser powder bed fusion additive manufacturing of aluminum alloy via in-situ high-speed X-ray imaging

Laser metal additive manufacturing technologies enable the fabrication of geometrically and compositionally complex parts unachievable by conventional manufacturing methods. However, the certification and qualification of additively manufactured parts are greatly hindered by the stochastic melt flow instabilities intrinsic to the process, which has not been explicitly revealed by direct observation. Here, we report the mechanisms of the melt flow instabilities in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-resolution synchrotron X-ray imaging. Here we identified powder/droplet impact, significant keyhole oscillation, and melting-mode switching as three major mechanisms for causing melt flow instabilities. We demonstrated the detrimental consequences of these instabilities brought to the process, and presented new understanding on the melt flow evolution and keyhole oscillation. This work provides critical insights into process instabilities during laser metal additive manufacturing, which may guide the development of instability mitigation approaches. The results reported here are also important for the development and validation of high-fidelity computational models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational study of the ECN spray C via one-way coupling of internal nozzle flow and ensuing spray

Here, in this study, computational fluid dynamics simulations of the Engine Combustion Network Spray C injector were performed to investigate the effect of the internal nozzle flow on the development of the ensuing spray in automotive fuel injectors. A set of simulation best practices was first developed to achieve an accurate representation of the internal nozzle flow. Detailed, spatiotemporally defined information was extracted at the injector’s orifice exit and stored in maps that were subsequently employed to initialize a Lagrangian spray by means of a static, one-way coupling (OWC) approach. To highlight the impact of the in-nozzle flow on the spray, the OWC approach was compared with the conventional rate-of-injection (ROI) approach, for which the spray parcels are commonly initialized using a blob injection model. The results indicated that, using the ROI approach, considerable evaporation was observed within the plume in the near-nozzle region. On the other hand, the OWC approach showed evidence of evaporation farther downstream and at the plume periphery. The different behavior observed with the two approaches was mostly due to substantial differences in fuel mass distribution. The liquid fuel concentration predicted by the OWC approach matched well with X-ray tomography measurements of projected fuel mass per unit volume, in terms of both magnitude and distribution. In contrast, an unphysical distribution of fuel mass characterized by an extremely low liquid concentration was predicted by the ROI approach. In the far-field, the effect of the in-nozzle flow was found to be weaker as the spray penetrated and interacted with the ambient gas. In this region, the OWC and ROI approaches provided similar predictions of vapor penetration, spray morphology, and mixing-related characteristics.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Code-verification techniques for hypersonic reacting flows in thermochemical nonequilibrium

The study of hypersonic flows and their underlying aerothermochemical reactions is particularly important in the design and analysis of vehicles exiting and reentering Earth's atmosphere. Computational physics codes can be employed to simulate these phenomena; however, code verification of these codes is necessary to certify their credibility. To date, few approaches have been presented for verifying codes that simulate hypersonic flows, especially flows reacting in thermochemical nonequilibrium. In this work, we present our code-verification techniques for verifying the spatial accuracy and thermochemical source term in hypersonic reacting flows in thermochemical nonequilibrium. Additionally, we demonstrate the effectiveness of these techniques on the Sandia Parallel Aerodynamics and Reentry Code (SPARC).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗