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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The development and application of the stirred‐reactor coupon analysis (SRCA) test method

A new technique, termed the stirred‐reactor coupon analysis (SRCA) method, has been developed to measure the rate of glass dissolution in forward‐rate conditions. Monolithic glass coupons are partially masked with an inert material before placement in a large volume of well‐mixed solution with known chemistry and temperature for a predetermined duration. After the test, the mask is removed, and the difference in step height between the protected area and the exposed corroded portions of the sample coupon is measured to determine the extent of glass dissolution. The step height is converted to a rate measurement using the test duration and glass density. Test parameters such as sample surface preparation and test duration were evaluated to determine their effects on the measured rates. Additionally, results from an interlaboratory study (ILS) consisting of 12 laboratories from 11 different institutions are presented, where each laboratory performed 12 independent tests. When removing experimental outlier data, the 95% reproducibility limits for the SRCA method has no statistical difference with previously published standardized test methods used to determine the forward rate of glass dissolution. Overall, this paper describes steps necessary to perform the test method and provides the statistical calculations to evaluate test accuracy.

chemical durability↗
DOE OSTI2024

Spectral Analysis of Regular Material Point Method and its Application to Study High Pressure Reverse Osmosis Membrane Compaction and Embossing

Material Point Method (MPM) is gaining widespread interest in applied continuum mechanics. The fact that all the continuum properties are stored on the particles (or material points) and the governing equations are solved on these material points makes MPM extremely suited to problems involving severe material deformations, such as crack propagation, soil movement, and fluid flows. Despite its popularity, only a few studies have focused on the numerical properties of MPM. This presentation introduces a global spectral analysis of the regular material point method. Contrary to previous studies, the analysis focuses on the numerical properties of the method in the spectral space. The amplification factor is derived as a function of the non- dimensional wave numbers. It provides insights into the stability and dissipative properties of the method for various CFL and Fourier numbers. The effect of the grid shape functions, number of particles per cell and their locations inside the grid cell are also analyzed. The EXAGOOP MPM solver (https://github.com/NREL/Exagoop.git) is developed at the National Renewable Energy Laboratory as a part of the NAWI UHPRO project and is based on the AMReX framework. A single-level, uniform cartesian grid is used as the background mesh, while the particle class in AMReX is used to manage the material point operations. Linear hat and B-splines are used as grid shape functions, while the time integration is performed using explicit Euler time integration. EXAGOOP is both CPU and GPU compatible and has been demonstrated to work well on multiple compute architectures. The performance of EXAGOOP on various computing architectures is presented along with its application to study compaction and embossing of high-pressure reverse osmosis membranes. The MPM solution accurately reproduces the membrane deformation. The deformed pore size and structure simulated using MPM also agree well with experimental SEM images.

material point method↗
DOE OSTI2024

Investigating Formal Methods Tools and their Applicability for Hardware Vulnerability Remediation

Formal methods use mathematical logic and equations to prove that a system or code is secure. In this poster, I examine existing formal methods tools and their application for projects working to remediate vulnerabilities in hardware and hardware description language. This poster is focused on the tools ReWire and AutoGenILA and I hope to evaluate their benefits and weaknesses with the intention of creating an internal report on the application and weaknesses of existing formal methods tools and identifying gaps for future formal methods tool creation.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint

The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Adaptive tau-leaping methods for microscopic-lattice kinetic Monte Carlo simulations

Traditional Kinetic Monte Carlo (KMC) approaches, rooted in Gillespie’s stochastic simulation algorithm, become computationally demanding in systems with a large range of timescales. The goal of this work is to propose and study new adaptive lattice-KMC time integration strategies for spatially non-uniform systems. To that end, two novel adaptive tau-leaping methods and their corresponding time integration strategies are developed based on the idea of the “n-fold” direct KMC method. These strategies allow for the simultaneous execution of multiple reactions, advancing time by adaptively selected coarse increments. We present numerical experiments comparing the proposed methods with existing approaches in a catalytic surface kinetics application involving ammonia decomposition.

Bimolecular reactions↗
DOE OSTI2024

Discrete-Element and Material-Point Method (DEM and MPM) Based Solvers for Sustainable Technologies

We present the use of discrete element method (DEM) and material point method (MPM) in three relevant green technology applications that include biomass feedstock handling, lithium-ion battery manufacturing, and high-pressure reverse osmosis. Our open-source DEM and MPM solvers are developed using performance portable grid and particle management library, AMReX, thus enabling superior performance on NVIDIA and AMD GPUs with > 100 million particles. Our DEM solver resolves the motion of individual particles in a granular system and includes a bonded sphere method for modeling non-spherical particles along with Hertzian and liquid bridge-based contact models. We simulate highly variable biomass feedstock flows in large-scale hoppers for biofuel production and electrode calendering in battery manufacturing using DEM. Our simulations predict flow blockage in large scale biomass hoppers and electrode microstructure variations, thus providing valuable information for biofuel and battery manufacturers, respectively. The second half of the talk will be on MPM and its application towards pore resolved simulations of reverse osmosis membranes under compressive loads. We present a validation study of our MPM simulations with membrane microscopy imaging thus providing useful insights on membrane stability under high pressure conditions. We also present a spectral stability analysis of using linear hat, quadratic and cubic spline basis in MPM indicating regions of numerical stability.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Methods for Color Center Preserving Hydrogen‐Termination of Diamond

Abstract Chemical functionalization of diamond surfaces by hydrogen is an important method for controlling the charge state of near‐surface fluorescent color centers, an essential process in fabricating devices such as diamond field‐effect transistors and chemical sensors, and a required first step for realizing families of more complex terminations through subsequent chemical processing. In all these cases, termination is typically achieved using hydrogen plasma sources that can etch or damage the diamond, as well as deposited materials or embedded color centers. This work explores alternative methods for lower‐damage hydrogenation of diamond surfaces, specifically the annealing of diamond samples in high‐purity, non‐explosive mixtures of nitrogen and hydrogen gas, and the exposure of samples to microwave hydrogen plasmas in the absence of intentional stage heating. The effectiveness of these methods are characterized by x‐ray photoelectron spectroscopy (XPS), and comparison of the results to density‐functional modelling of the surface hydrogenation energetics implicates surface oxygen ligands as the primary factor limiting the termination quality of annealed samples. Finally, photoluminescence (PL) spectroscopy is used to verify that both the annealing and reduced sample temperature plasma methods are non‐destructive to near‐surface ensembles of nitrogen‐vacancy (NV) centers, in stark contrast to plasma treatments that use heated sample stages.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transfer of Millimeter‐Scale Strained Multiferroic Epitaxial Thin Films on Rigid Substrates via an Epoxy Method Producing Magnetic Property Enhancement

The demonstration of epitaxial thin film transfer has enormous potential for thin film devices free from the traditional substrate epitaxy limitations. However, large-area continuous film transfer remains a challenge for the commonly reported polymer-based transfer methods due to bending and cracking during transfer, especially for highly strained epitaxial thin films. In this work, a new epoxy-based, rigid transfer method is used to transfer films from an SrTiO 3 (STO) growth substrate onto various new substrates, including those that will typically pose significant problems for epitaxy. An epitaxial multiferroic Bi 3 Fe 2 Mn 2 O x (BFMO) layered supercell (LSC) material is selected as the thin film for this demonstration. The results of surface and structure studies show an order of magnitude increase in the continuous area of transferred films when compared to previous transfer methods. The magnetic properties of the BFMO LSC films are shown to be enhanced by the release of strain in this method, and ferromagnetic resonance is found with an exceptionally low Gilbert damping coefficient. The large-area transfer of this highly strained complex oxide BFMO thin film presents enormous potential for the integration of many other multifunctional oxides onto new substrates for future magnetic sensors and memory devices.

bismuth oxides↗
DOE OSTI2024

A Particle Method for the Multispecies Landau Equation

Abstract The multispecies Landau collision operator describes the two-particle, small scattering angle or grazing collisions in a plasma made up of different species of particles such as electrons and ions. Recently, a structure preserving deterministic particle method (Carrillo et al. in J. Comput. Phys. 7:100066, 2020) has been developed for the single species spatially homogeneous Landau equation. This method relies on a regularization of the Landau collision operator so that an approximate solution, which is a linear combination of Dirac delta distributions, is well-defined. Based on a weak form of the regularized Landau equation, the time dependent locations of the Dirac delta functions satisfy a system of ordinary differential equations. In this work, we extend this particle method to the multispecies case, and examine its conservation of mass, momentum, and energy, and decay of entropy properties. We show that the equilibrium distribution of the regularized multispecies Landau equation is a Maxwellian distribution, and state a critical condition on the regularization parameters that guarantees a species independent equilibrium temperature. A convergence study comparing an exact multispecies Bobylev-Krook-Wu (BKW) solution to the particle solution shows approximately 2nd order accuracy. Important physical properties such as conservation, decay of entropy, and equilibrium distribution of the particle method are demonstrated with several numerical examples.

Mathematics↗
DOE OSTI2025

Experimental Evaluation of Interfacial Bonding Strength Between 304 Stainless Steel Substrate and Electrodeposited Nickel Coating Using Mesoscale Mechanical Testing Methods

Electrodeposition is a commonly used method for depositing a metal layer on a metallic substrate, to provide a decorative finish or for functional purposes including wear and corrosion resistance. The interfacial bonding strength is a critical factor in determining the quality of electrodeposition, as it ensures the adhesion of the deposited layer to the substrate. Despite its importance, there has not previously been a suitable mechanical testing method to quantitatively characterize the bonding strength between the electrodeposited layers and the substrate, due to the high strength of the materials and to restrictions imposed by the geometry (due to the small thickness of the plating layer) to manufacture tensile bars. In this study, we introduce a novel mesoscale mechanical testing method to overcome these limitations. This technique was applied to assess the bonding strength between a 304 stainless steel (SS) substrate and electrodeposited nickel with three different plating formulae (nickel sulfamate, Watts bath, and hard nickel). The thickness of the pure nickel coatings achieved for each of the three electrolyte solutions was approximately 1 mm on each side of the substrate. Mesoscale tensile bars of 1 mm length were manufactured by a femtosecond laser, with the material interface at the center of the gauge section, and then tensile-tested with digital image correlation. Further, the results proved that the electrodeposition technique is able to produce a very high bonding strength that is close to the yield strength of both the substrate material and the electrodeposited nickel layer. Additionally, in the case of the Watts bath formula, the interfacial bonding strength between the SS 304 and electrodeposited nickel can exceed that of the nickel layer. This method is expected to be useful for quantifying the interfacial bonding strength in numerous applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Simplified Method for Predicting Shaker Voltage in IMMATs

Impedance Matched Multi-Axis Tests (IMMATs) can replicate in-service vibration induced stress more accurately than single axis shaker table tests as they can better match a part’s operational boundary conditions and excite it in multiple degrees of freedom simultaneously. Here, the shakers used in IMMATs are less powerful than shaker tables, so shaker force limits can be exceeded during tests if they are not placed adequately for the desired environment. The ability to predict shaker voltage and force before performing a test is, therefore, helpful in selecting shaker locations so that their limits are not exceeded. In this study, electrodynamic shakers were modeled as discrete electromechanical systems, and the shaker parameters were chosen to match experimentally obtained acceleration/voltage frequency response functions (FRFs). These models were coupled to a finite element model of the device under test (DUT) via dynamic substructuring, and the substructured model was demonstrated to accurately predict shaker voltage as well as the error in reproducing the environment at multiple accelerometer locations. A simple method called the FRF Multiplication method, in which the FRF of the substructured system is approximated as the product of two separate FRFs of the shaker and DUT respectively, was proposed and applied to the same system, yielding similar voltage and error predictions to those obtained using substructuring. Simple case studies were presented to explore the applicability of the proposed method, and it was demonstrated to have similar accuracy to the substructuring method in a range of cases. Additionally, we showed that while it was not possible to derive a unique model of the shakers from acceleration/voltage FRFs alone, the models that could be obtained were sufficient to predict test error almost perfectly and shaker voltage with less than 40 percent error.

42 ENGINEERING↗
DOE OSTI2025

An iterative dynamic chemical stiffness removal method for reacting flow simulations

Abstract An iterative dynamic chemical stiffness removal method (IDCSR) based on quasi-steady-state approximation (QSSA) is proposed. The IDCSR method is built on a previously developed non-iterative method which has proved to work well for small timestep sizes. A novel iterative procedure is designed in IDCSR to enable explicit time integration of stiff chemistry at relatively large timestep sizes relevant to practical reacting flow simulations. The effectiveness of the iterative procedure is first demonstrated with a toy problem and homogeneous auto-ignition with fixed integration step sizes, showing that larger timestep sizes can be allowed for explicit time integration using IDCSR compared with the previous non-iterative method. IDCSR is then compared with existing explicit chemistry solvers for simulations of homogeneous auto-ignition and shows similar or lower computational cost but significantly higher accuracy across a wide range of timestep sizes. IDCSR is further combined with an automatic adaptive time-stepping scheme for simulations of 0-D homogeneous auto-ignition and a 2-D laminar lifted n -dodecane jet flame. For the 0-D auto-ignition simulations, IDCSR is shown to reduce both the error (by 43%–90%) and computational cost (by 6–15 times) compared with existing explicit solvers, while achieving speed-up factors of up to 400 compared with VODE for a wide range of timestep sizes and reaction mechanisms. For the 2-D jet flame simulations, speed-up factors of 15 and 31 for chemistry integration, and 5 and 9 for overall simulation, are achieved by IDCSR compared with CVODE with and without analytic Jacobian, respectively.

Xu, Chao (ORCID:0000000153074159)↗
DOE OSTI2024

The Transient Multi-Level method for Monte Carlo reactor statics calculations

The Transient Multi-Level (TML) method is applied to a time-dependent Monte Carlo transport solver to offload some of the computational burden of the expensive Monte Carlo solve to lower-order Coarse Mesh Finite Difference (CMFD) and Exact Point Kinetics Equations (EPKE) solvers via factorization of the neutron flux at the transport and CMFD levels using the Predictor Corrector Quasi-Static Method (PCQM). The Monte Carlo transient is solved by a modified fission source iteration scheme that introduces a single transient source bank. The method is implemented in the production-level Monte Carlo code, Shift, and verified with prescribed reactivity ramps from the two-dimensional version of the C5G7-TD reactor benchmark. The results show that, as compared to other quasi-static methods, the TML reduces the stochastic noise inherent to the transient Monte Carlo solver by factors of ~2 to 6 for various norm comparisons of the reactor power amplitude. Finally, the TML additionally reduces the number of Monte Carlo evaluations needed to simulate the transient, leading to roughly an order of magnitude improvement in CPU time relative to the standard PCQM for the problems tested.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

127 Xe quantification method development and intercomparison exercise

Monitoring of the atmosphere for fission products ( 131m Xe, 133m Xe, 133 Xe, and 135 Xe) is performed by various laboratories to detect nuclear explosions. Quantification of 127 Xe is not routinely performed by laboratories measuring atmospheric radioxenon because it is not a fission product. 127 Xe was recently detected by a ground-based beta-gamma air monitoring system. When measured using beta-gamma coincidence detector systems, such as those in use on the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), 127 Xe can interfere with the quantification of fission product radioxenon due to overlap of the 127 Xe beta-gamma coincidence signatures with those of fission product radioxenon. Here, this work demonstrates quantification of 127 Xe at different laboratories with different measurement techniques. Production and purification of 127 Xe was performed by neutron activation of enriched 126 Xe. The purified 127 Xe was then split between laboratories, and detection and quantification methods were developed. At Idaho National Laboratory, a quantification method involving high purity germanium detectors was devised that included self-attenuation correction. At AWE, a beta-gamma coincidence counting method, as used in support of the IMS, was modified to enable the measurement and analysis of the 127 Xe samples. Corrections were made for self-attenuation, which showed a strong xenon volume dependency, for some coincidence signatures. The gas sample activity concentration was used as the comparison metric and it showed excellent agreement between the methods.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Design and evaluation of alphabetic and numeric input methods for virtual reality

In today’s virtual reality (VR), users have various ways to influence their VR experience, including alphanumeric input. While typing characters and numbers is straightforward on desktop computers, it presents challenges and opportunities in head-mounted display VR due to specific interaction methods and a lack of real-world visual stimuli. Addressing these open questions, our work implements and evaluates ten approaches to alphabetic and numeric inputs in VR. Here, we describe the design motivation behind these input methods and evaluate them in a user study with 40 participants divided into groups for alphabetic and numeric keyboards. This comparison investigates each method’s performance and user interactions. Our findings suggest that different input methods significantly impact words per minute and error rates, and that certain keyboard designs may receive better subjective evaluations despite poorer objective performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Computational methods in solution-based plastics purification

Plastic waste can be recycled into resins with near-virgin properties by solution-based purification processes that selectively dissolve polymers, remove contaminants, or detach printing residues. Here, in this review, we examine computational methods for predicting the behavior governing solution-based plastic purification, motivated by the vast polymer–solvent–contaminant compositional space. We discuss thermodynamic and machine learning methods for predicting polymer–solvent and polymer–contaminant interaction and review physics-based molecular dynamics simulations that resolve molecular-scale phenomena within polymer matrices inaccessible to screening methods. We highlight how these methods have informed experimental design for dissolution-based recycling and solvent-based contaminant removal. Finally, we discuss the prospective role of agentic AI in integrating these computational tools with real-time sorting data to adapt purification conditions to the compositional variability of real post-consumer feedstocks. This review charts a path toward computationally guided solution-based purification workflows that can respond to the complexity inherent in plastic waste streams.

Altamimi, Ali [Univ. of Wisconsin, Madison, WI (Un↗
DOE OSTI2026

A new self-adaptive reconstruction method to identify defects through Wigner–Seitz approach

A new self-adaptive reconstruction method based on local atomic structure at any given molecular dynamics (MD) step has been developed in this article. The method can be used in Wigner–Seitz defect analysis approach to correctly and efficiently explore the information of both point defects and complex defect clusters (e.g. dislocation loops and voids) formed after a displacement cascade where the cascade interacts with grain boundaries and/or dislocations. The algorithm and validation are provided in detail. Results for identification of radiation defects during and after cascades interacting with a dislocation network show that the new method can well recognize all simple and complex defects and defect clusters. Thus, this new method provides a totally new way to explore the density and size of radiation defects at atomic scale after complex MD evolution processes, providing correct information to understand and predict radiation damage in materials through atomic simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗