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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Open-Source Parallel EMT Simulation Framework: Preprint

As the integration level of inverter-based resources (IBR) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗
DOE OSTI2021

Fluid dynamic simulation and analysis of water-cooling systems for the Electron-Ion Collider

The Electron-Ion Collider is the newest large-scale project at Brookhaven National Laboratory. The collider’s purpose is to provide further advancements in the knowledge of the universe’s origin by accelerating particles near the speed of light. Our project for this 3.8 km ring was to create a thermal hydraulic steady-state simulation design of the water-cooling system to be cost-effective and energy efficient, as envisioned by Charlie Foltz, the EIC Infrastructure Division Director. The system would include a supply and return header, which cools several thousand components of the ring. The water would then be returned and cooled down using a system of cooling towers and plate and frame heat exchangers. Due to the size of the system and the complexity of the network analysis, a fluid dynamic simulation software, AFT Fathom, was used. Since previous methods of maintaining systems relied on building upon smaller real-life models and implementing empirical data, this flow model was unique and first of a kind in the domain of accelerator design, construction and operation. Therefore, our hydraulic team piloted a new method to perform network analysis on a large scale cooling system. We successfully created several test scenarios for system behavior in a shorter time compared to the method of performing hand calculations. Cooling specifications for heat rejection, pressure drop, flow rate, and pipe sizing were changed based on the individual systems of the vacuum, radio frequency (RF), magnet and power supply, and cryogenics sections. Finally, we used DOE guidelines to perform life-cycle cost analysis with net present value and carbon saving analysis on the systems where pipe size could be optimized.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2020

Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Abstract Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.

58 GEOSCIENCES↗
DOE OSTI2020

A Physically-Based Model for Thermo-Oxidative and Hydrolytic Aging of Elastomers

A computationally efficient model is proposed to capture the loss of mechanical performance due to chemical aging that are formed as the competition of chain scission and cross-link formation/dissolution, such as thermo-oxidative aging or hydrolytic aging. The model should be considered an extension of our recent models [1, 2, 3] which further simplifies the matrix behavior based on the assumption of independence of environmental and mechanical damage. The model uses this assumption to reduce the necessary material parameters needed to model constitutive and inelastic behavior of elastomers during aging. To this end, the model can provide accurate predictions of the material performance with the significantly fewer number of fitting parameters. The model is relevant for all decay mechanisms formed by the occurrence of two simultaneous micro-mechanisms; (i) formation/reduction of the cross-links, and (ii) chain scission, both of which are present in thermo-oxidation and hydrolytic aging. Assuming the alteration of the chain density along the aging trajectory is identical to the peroxide cross-link density for thermo-oxidation, and the change of the average molecular weight for hydrolysis, the strain energy of polymer matrix can be rewritten as a function of deformation, deformation history, storage time and aging temperature. Next, the modified network alteration model is formulated for implementation into Finite Element (FE) simulations. The model is built on the presumption of homogeneous and consistent oxygen/water absorption and thus is mainly relevant for relatively thin samples exposed to environmental loads for a long time. The proposed model includes only six physically inspired material parameters. Therefore, while it is computationally efficient, it shows good agreement with own experimental data, which performed on various range of accelerated aging temperatures and times. With respect to its computational efficiency, simplicity, accuracy, and interpret-ability, the model is the right choice for advanced implementations in FE programs.

42 ENGINEERING↗
DOE OSTI2024

Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning

Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. Here, we apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.

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

A Modified Network Alteration Model to Predict Quasi-Static Behavior of the Cross-Linked Polymers During Hydrolytic Aging

Abstract The network alteration theory is utilized to capture the effects of hydrolytic aging on the mechanical integrity of the polymer matrix. Hydrolytic degradation generally considered as a result of two simultaneous chemical phenomena; (i) reduction of the cross-links, and (ii) active chain loss. Assuming the alteration of the chain density is identical to the change of the number average molecular weight along the aging trajectory, the strain energy of polymer matrix can be rewritten as a function of deformation, deformation history, storage time, and aging temperature. The proposed model includes only five physically inspired material parameters. Thus, while it is computationally efficient, it shows good agreement with own experimental data, which performed on various range of accelerated aging temperatures and times. With respect to its computational efficiency, simplicity, accuracy, and interpret-ability, the model is a good choice for advanced implementations in FE programs.

Bahrololoumi, Amir↗
DOE OSTI2020

SUGAR: An improved empirical model of Type Ia supernovae based on spectral features

Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multiscale Modeling of Corrosion and Oxidation Performance and Their Impact on High-temperature Fatigue of Automotive Exhaust Manifold Components

The persistent need to reducing automobile emissions has led to the wide adoption of turbocharger technology and engine downsizing, which results in a substantial raise in exhaust temperature, reaching as high as 950°C. Therefore, the material selection for exhaust manifolds and turbocharger housings becomes significantly challenging. The successful development of the multiscale models on oxidation and corrosion performance, and their impact on high-temperature fatigue will enable the acceleration of new, high-performance, heat-resistant alloy solutions and the reduction of time and cost in achieving a durable design. In this project, we developed and validated multiscale models that can accurately predict the corrosion and oxidation performance and their impact on high-temperature fatigue of high-temperature automotive exhaust manifold components. The capability of accurately predicting the corrosion/oxidation-fatigue damage can significantly reduce the time and cost of achieving a durable design. The models were used to guide the development of new high thermomechanical fatigue (TMF) performance alloys.

Li, Mei↗
DOE OSTI2025

Structure Prediction of Ionic Epitaxial Interfaces with Ogre Demonstrated for Colloidal Heterostructures of Lead Halide Perovskites

Colloidal epitaxial heterostructures are nanoparticles composed of two different materials connected at an interface, which can exhibit properties different from those of their individual components. Combining dissimilar materials offers exciting opportunities to create a wide variety of functional heterostructures. However, assessing structural compatibility–the main prerequisite for epitaxial growth–is challenging when pairing complex materials with different lattice parameters and crystal structures. This complicates both the selection of target heterostructures for synthesis and the assignment of interface models when new heterostructures are obtained. Here, we demonstrate Ogre as a powerful tool to accelerate the design and characterization of colloidal heterostructures. To this end, we implemented developments tailored for the high-efficiency prediction of epitaxial interfaces between ionic/polar materials, which encompass most colloidal semiconductors. These include the use of pre-screening candidate models based on charge balance at the interface and the use of a classical potential for fast energy evaluations, with parameters automatically calculated based on the input bulk structures. These developments are validated for perovskite-based CsPbBr 3 /Pb 4 S 3 Br 2 heterostructures, where Ogre produces interface models in excellent agreement with density functional theory and experiments. Furthermore, we use Ogre to rationalize the templating effect of CsPbCl 3 on the growth of lead sulfochlorides, where perovskite seeds induce the formation of Pb 4 S 3 Cl 2 rather than Pb 3 S 2 Cl 2 due to better epitaxial compatibility. Finally, combining Ogre simulations with experimental data enables us to unravel the structure and composition of the hitherto unsolved CsPbBr 3 /Bi x Pb y S z interface, and to assign a structure to several other reported metal halide- and oxide-based interfaces. The Ogre package is available on GitHub or via the OgreInterface desktop application, available for Windows, Linux, and Mac.

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

Geologic hydrogen: From natural occurrences to anthropogenic generation – A review of fundamentals, potential, challenges and prospects

Growing demand for hydrogen is exposing the environmental and economic limits of reforming-based and carbon-managed supply chains, while the scale-up of electrolytic capacity remains capital-constrained. Geologic hydrogen, defined as molecular H₂ generated and stored within the Earth's crust offers a complementary, potentially lower-cost resource, yet exploration is still ad hoc. This review (1) revisits a global inventory of confirmed hydrogen seeps and subsurface occurrences; (2) analyzes the controlling reactions, migration pathways, and trapping conditions governing these occurrences; (3) proposes a process-based geologic hydrogen system concept analogous to, yet distinct from, the petroleum system; and (4) evaluates potential geologic hydrogen systems within the United States as a representative case study. Here, we contrast natural systems powered by serpentinization, mantle degassing or radiolysis with anthropogenic systems that stimulate the same reactions or convert in-situ hydrocarbons. Stable hydrogen accumulations require generation rates that outpace combined physical, chemical and microbial losses; the Bourakébougou field (Mali) exemplifies a self-recharging, free-gas reservoir sustained by meteoric-water serpentinization beneath an efficient caprock. Prospective geologic hydrogen resources are likely to occur in regions where iron-rich lithologies, deep-seated faults, and low-permeability sealing formations coexist. Applying this principle, we highlight three promising hydrogen play types in U.S. geological terrains: ophiolite belts (Appalachian and Californian regions), the Midcontinent Rift and the Lake Superior banded‑iron formations. Multiphysics numerical models and positive-unlabeled machine-learning workflows help to accelerate play screening and de-risk future production; yet, reaction kinetics, stimulation strategies, and full techno-economic and life-cycle assessments remain pivotal knowledge gaps.

Anthropogenic hydrogen generation↗
DOE OSTI2022

Prospects for additive manufacturing of nuclear fuel forms

Additive manufacturing (AM) is viewed as a critical enabling technology for achieving superior performance and improved economics across many sectors. Although the past decade has seen increased application of AM methods to nuclear reactor cladding and structural materials, exploration of AM as applied to the uranium-bearing nuclear fuel forms has been limited. The two major families of nuclear fuel forms (monolithic, particle/dispersion) are utilized with their own set of core objectives in mind, and their differences require distinct fabrication infrastructures. These reference fabrication methods impose many limitations on nuclear fuels. Further, both currently operating reactors and future concepts have the potential for improved performance if these accepted limitations are relaxed or removed entirely. The primary limitations of reference fabrication processes for the common nuclear fuel forms are outlined in this paper. This groundwork is then used to identify avenues of fuel performance specific to each of these fuel architectures that could be exploited if the restrictions of conventional fuel fabrication are removed. Moreover, multiple targets for AM studies are laid out for each of the major nuclear fuel variants. Finally, key strategic components to guide research activities in AM of nuclear fuels are outlined, with an emphasis on use of modeling and simulation to motivate research aims and embrace of an accelerated testing methodology to screen and quality new fuel forms.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

High-throughput design of high-performance lightweight high-entropy alloys

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity

Abstract Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Gyrokinetic profile prediction and validation of a negative triangularity plasma in ASDEX Upgrade

In this work, gyrokinetic simulations are performed with the CGYRO code (Candy et al 2016 J. Comput. Phys. 324 73–93) for a negative triangularity H-mode plasma in ASDEX Upgrade, and compared with experimental measurements. The PORTALS framework (Rodriguez-Fernandez et al 2024 Nucl. Fusion 64 076034) is used to accelerate the prediction of kinetic profiles for this plasma, using surrogate modeling and Bayesian optimization. Ion heat flux, electron heat flux, and electron particle flux are simultaneously matched across the simulated radial regime of the plasma (normalized radius $r/a = 0.35-0.90$), and the resulting ion temperature, electron temperature, and electron density profiles match well with the experimental profile data within this radial range. A synthetic Correlation Electron Cyclotron Emission diagnostic is applied to find well-matched electron temperature fluctuation properties between simulation and experiment. The flux-matched profiles provide a basis for investigation of the turbulence nature across the plasma radius, revealing the dominance of Trapped Electron Mode turbulence at $r/a = 0.35$, the dominance of Ion Temperature Gradient turbulence at $r/a = 0.55$, 0.75, and 0.83, and an instability boundary at $r/a = 0.90$.

gyrokinetic simulation↗
DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗
DOE OSTI2024

Linear Solvers for Collector Systems of Generalized Large-scale Inverter-Based Resources

Collector systems for inverter-based resources (IBRs) are typically represented by equivalent circuits for electromagnetic transient (EMT) simulations. Recent studies have revealed that modeling a detailed collector system is essential to accurately represent the behavior of IBRs, especially when dealing with partial tripping during external disturbances. However, there are several challenges in simulating a detailed EMT model of a collector system due to the time required to simulate such systems. Thus, this paper investigates the modeling of a detailed collector system, taking into account its configuration and components as defined in IEEE standard 2800. The configurations include the collector systems of generalized large-scale IBR plants. The components include the main IBR transformer, collector bus, and feeders with lines and/or cables. The EMT model of the collector system is represented by differential algebraic equations (DAEs) that are discretized to form linear equations that are solved using linear solvers. In this paper, linear solvers are proposed based on the Schur complement method, which are utilized for simulation of the EMT model of collector systems of generalized large-scale IBRs to accelerate simulation speed while maintaining the accuracy of the results. The proposed solvers are verified by comparing the performance to that of linear solvers provided in MATLAB.

Choi, Jongchan↗
DOE OSTI2021

A Computational Study of the Thermodynamic Conditions Leading to Autoignition in Nanosecond Pulsed Discharges

Nanosecond pulsed discharges have attracted the attention of engine manufacturers due to the possibility of attaining distributed ignition sites that accelerate burn rates while resulting in very little electrode erosion. Multidimensional modeling tools currently capture the electrical structure of such discharges accurately, but resolving the chemical structure remains a challenging problem owing to the disparity of time-scales in streamer propagation (nanoseconds) and ignition phenomena (microseconds). The purpose of this study is to extend multidimensional results toward resolving the chemical structure in the wake of streamers (or the afterglow) by using a batch reactor model (BRM). This can afford the use of very detailed chemical kinetic information. The full nonequilibrium nature of the electrons is taken into account, along with fast gas heating, shock wave propagation, and thermal diffusion. The results shed light on ignition phenomena brought about by such discharges.

42 ENGINEERING↗