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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗
NASA NTRS1993

Particle kinetic simulation of high altitude hypervelocity flight

In this grant period, the focus has been on enhancement and application of the direct simulation Monte Carlo (DSMC) particle method for computing hypersonic flows of re-entry vehicles. Enhancement efforts dealt with modeling gas-gas interactions for thermal non-equilibrium relaxation processes and gas-surface interactions for prediction of vehicle surface temperatures. Both are important for application to problems of engineering interest. The code was employed in a parametric study to improve future applications, and in simulations of aeropass maneuvers in support of the Magellan mission. Detailed comparisons between continuum models for internal energy relaxation and DSMC models reveals that several discrepancies exist. These include definitions of relaxation parameters and the methodologies for implementing them in DSMC codes. These issues were clarified and all differences were rectified in a paper (Appendix A) submitted to Physics of Fluids A, featuring several key figures in the DSMC community as co-authors and B. Haas as first author. This material will be presented at the Fluid Dynamics meeting of the American Physical Society on November 21, 1993. The aerodynamics of space vehicles in highly rarefied flows are very sensitive to the vehicle surface temperatures. Rather than require prescribed temperature estimates for spacecraft as is typically done in DSMC methods, a new technique was developed which couples the dynamic surface heat transfer characteristics into the DSMC flow simulation code to compute surface temperatures directly. This model, when applied to thin planar bodies such as solar panels, was described in AIAA Paper No. 93-2765 (Appendix B) and was presented at the Thermophysics Conference in July 1993. The paper has been submitted to the Journal of Thermophysics and Heat Transfer. Application of the DSMC method to problems of practical interest requires a trade off between solution accuracy and computational expense and limitations. A parametric study was performed and reported in AIAA Paper No. 93-2806 (Appendix C) which assessed the accuracy penalties associated with simulations of varying grid resolution and flow domain size. The paper was also presented at the Thermophysics Conference and will be submitted to the journal shortly. Finally, the DSMC code was employed to assess the pitch, yaw, and roll aerodynamics of the Magellan spacecraft during entry into the Venus atmosphere at off-design attitudes. This work was in support of the Magellan aerobraking maneuver of May 25-Aug. 3, 1993. Furthermore, analysis of the roll characteristics of the configuration with canted solar panels was performed in support of the proposed 'Windmill' experiment. Results were reported in AIAA Paper No. 93-3676 (Appendix D) presented at the Atmospheric Flight Mechanics Conference in August 1993, and were submitted to Journal of Spacecraft and Rockets.

Haas, Brian L.↗
DOE OSTI2023

Automated Symbolic Upscaling: 1. Model Generation for Extended Applicability Regimes

Abstract In porous media theory, upscaling techniques are fundamental to deriving rigorous Darcy‐scale models for flow and reactive transport in subsurface systems. Due to limitations in classical techniques, a number of ad hoc approaches have been proposed to address physical regimes in which reactive time scales are similar to, or faster than, diffusive time scales. In Part 1 of this two part series, we present a strategy for expanding the applicability of classical homogenization theory by generalizing the assumed closure form. We detail the implementation of this strategy on two reactive mass transport problems with moderately reactive physics. The strategy produces nontrivial homogenized models with emergent terms and effective parameters that couple reactive, diffusive, and advective transport. The differences in equation forms between the macroscopic and pore‐scale descriptions advise caution to further studies where the forms of macroscopic equations are assumed, as opposed to rigorously derived. Numerical validation is provided for each problem to show that the error estimates of homogenization theory are satisfied, and to justify the implemented strategy. In Part 2, the presented strategy is automated using symbolic computing to expedite its implementation.

Pietrzyk, Kyle↗
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Magnus method for electronic structure calculations at extreme conditions

We present the application of Magnus based methods to the solution of first order coupled ordinary differential equations in High Energy Density (HED) physics applications. Our focus is on the application to quantum mechanical methods, specifically on the solution of the radial Dirac equation for real and complex energies. HED applications require accurate solutions across a wide range of spatial and energy domains, including regimes where the solutions exhibit pronounced oscillatory behavior. Such cases pose significant computational challenges. We demonstrate that Magnus-based integrators can efficiently and accurately address these challenges. We discuss the implementation of the Magnus method for the solution of the radial Dirac equation, including practical considerations such as the evaluation of matrix exponentials, numerical integration, error estimation, and adaptive step size control. We also discuss the application of these methods to complex energy Green’s function techniques and the efficient approximation of integrals of the solutions relevant to HED electronic structure calculations. Here, we demonstrate the accuracy and robustness of the resulting method in applications to the free-particle case, for which analytic solutions are available for comparison, as well as the challenging case of gold at HED conditions.

general physics↗
NASA NTRS2009

Neutralizer Hollow Cathode Simulations and Validation with Experiments

The fidelity of electric propulsion physics-based models depends largely on the validity of their predictions over a range of operating conditions and geometries. In general, increased complexity of the physics requires more extensive comparisons with laboratory data to identify the region(s) that lie outside the validity of the model assumptions and to quantify the uncertainties within its range of application. This paper presents numerical simulations of neutralizer hollow cathodes at various operating conditions and orifice sizes. The simulations were performed using a two-dimensional axisymmetric model that solves numerically a relatively extensive system of conservation laws for the partially-ionized gas in these devices. The results for the plasma are compared directly with Langmuir probe measurements. The computed keeper voltages are also compared with the observed values. Wherever model inputs and/or specific physics of the cathode discharge are uncertain, additional sensitivity calculations have been performed to quantify the uncertainties.

partially ionized gases↗
NASA NTRS2003

Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Simon, Dan↗
NASA NTRS1989

Advances in the computation of transonic separated flows over finite wings

Problems encountered in numerical simulations of transonic wind-tunnel experiments with low-aspect-ratio wings are surveyed and illustrated. The focus is on the zonal Euler/Navier-Stokes program developed by Holst et al. (1985) and its application to shock-induced separation. The physical basis and numerical implementation of the method are reviewed, and results are presented from studies of the effects of artificial dissipation, boundary conditions, grid refinement, the turbulence model, and geometry representation on the simulation accuracy. Extensive graphs and diagrams and typical flow visualizations are provided.

Kaynak, Unver↗
DOE OSTI2025

A Computational Tool Compatible with NEAMS Code Packages for Optimizing the Shape of Nuclear Reactor Components and of Whole Core Performance

We designed and implemented a shape optimization tool that functions with NEAMS codes, and that nuclear scientists and engineers can employ to optimize the shape of individual components and the whole core under the applicable single- or multi-physics model comprising the employed code(s). The shape-optimization tool enables varying the geometric shape itself as well as its dimensions to yield, potentially, new component designs that are not limited by the designer’s intuition and previous experience. In cases where the optimal-shape object is an individual component, we provide the capability for additional verification that the whole-core performance using the optimized component performs better, under the prescribed optimization criteria, than the initial design. Our shape-optimization tool couples to NEAMS codes via a flexible input- composer interface and enables the user to constrain the shape’s evolution to ensure the component’s manufacturability. Finally, we demonstrate our shape-optimization tool with single- and multi-physics NEAMS codes. This objective is motivated by the recent advances in manufacturing technology that, combined with rising interest in novel reactor concepts, are creating new opportunities for innovation in the design of individual components that affect the performance of the full reactor system. In particular, Additive Manufacturing (AM) enables mass production of highly precise, intricate and complex component shapes that are not feasible with traditional manufacturing techniques. To accomplish this goal we developed and implemented in MOOSE: (1) discrete shape optimization capability based on a state-space search that uses Artificial Intelligence strategies to find the optimal state/shape; (2) smooth shape optimization tool that employs PETSc’s toolkit for advanced optimization (TAO) to optimize node-displacement of the components’ model sidesets; (3) hierarchical core optimization workflow that recognizes the repeating patterns typical in a nuclear reactor and performs the optimization one level at a time with increasing length scale. Each of these tools is equipped with user-specified constraints to avoid optimal shapes that are not manufacturable. The developed shape optimization tool is verified and demonstrated on various nuclear reactor core components and models. The optimization process accounts for tightly coupled physics that govern the behavior of these target reactors, and exercises several NEAMS codes in a coupled multiphysics fashion. The impact of the delivered shape optimization tool will materialize in the optimal design, from the outset, of advanced reactors currently contemplated to regain the US’s leadership in nuclear energy R&D. Novel reactor concepts, e.g. Molten Salt Reactors, and sizes/capacities, e.g. micro- reactors, provide a unique opportunity to optimize performance from the early stages of development, before the investment in components’ production lines, validation experiments, and licensing regimes make future improvements in performance prohibitively expensive and force sub-optimal performance on the affected reactor concept in perpetuity. This benefit will be realized by the delivered shape optimization tool regardless of the applicable manufacturing process whether traditional or AM, thereby broadening the impact of this project on current and future reactor concepts and technologies

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Dynamics of voltage-driven oscillating insulator-metal transitions

Recent experiments demonstrated emerging alternating insulator and metal phases in Mott insulators actuated by a direct bias voltage, leading to oscillating voltage outputs with characteristic frequencies. Here, we develop a physics-based nonequilibrium model to describe the dynamics of oscillating insulator-metal phase transitions and experimentally validate it using a VO 2 device as a prototype. The oscillation frequency is shown to scale monotonically with the bias voltage and series resistance and terminate abruptly at lower and upper device-dependent limits, which are dictated by the nonequilibrium carrier dynamics. Here, we derive an approximate analytical expression for the dependence of the frequency on the device operating parameters, which yields a fundamental limit to the frequency and may be utilized to provide guidance to potential applications of insulator-metal transition materials as building blocks of brain-inspired non-von Neumann computers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Conservative discontinuous Galerkin schemes for nonlinear Dougherty–Fokker–Planck collision operators

Here, we present a novel discontinuous Galerkin algorithm for the solution of a class of Fokker–Planck collision operators. These operators arise in many fields of physics, and our particular application is for kinetic plasma simulations. In particular, we focus on an operator often known as the ‘Lenard–Bernstein’ or ‘Dougherty’ operator. Several novel algorithmic innovations, based on the concept of weak equality, are reported. These weak equalities are used to define weak operators that compute primitive moments, and are also used to determine a reconstruction procedure that allows an efficient and accurate discretization of the diffusion term. We show that when two integrations by parts are used to construct the discrete weak form, and finite velocity-space extents are accounted for, a scheme that conserves density, momentum and energy exactly is obtained. One novel feature is that the requirements of momentum and energy conservation lead to unique formulas to compute primitive moments. Careful definition of discretized moments also ensure that energy is conserved in the piecewise linear case, even though the kinetic-energy term, $v^2$ is not included in the basis set used in the discretization. A series of benchmark problems is presented and shows that the scheme conserves momentum and energy to machine precision. Empirical evidence also indicates that entropy is a non-decreasing function. The collision terms are combined with the Vlasov equation to study collisional Landau damping and plasma heating via magnetic pumping.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2023

DPFEHM: a differentiable subsurface physics simulator

The Earth’s subsurface is a key resource that provides energy via fossil fuels and geothermal sources, stores drinking water, and is used in the fight against climate change via carbon sequestration. Simulating the physical processes that occur in the Earth’s subsurface with computers enables better use of this resource. DPFEHM is a Julia package that includes computer models with a focus on the Earth’s subsurface, especially fluid flow, which is critical for the aforementioned applications. DPFEHM is able to solve the groundwater flow equations (single phase flow), Richards equation (air/water), the advection-dispersion equation, and the 2d wave equation. One of the key features of DPFEHM is that it supports automatic differentiation, so it can be integrated into machine learning workflows using frameworks such as Flux or PyTorch. The automatic differentiation capabilities give it the same performance as adjoint methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantum Devices for Neutrino and Rare Particle Detection (Final Report)

This report summarizes the technical work completed under the DOE-QuanISED program. This grant allowed the MIT group --and associated groups on the project, including UMass Amherst and Argonne National Laboratory -- to explore the use of superconducting bolometers for the purpose of neutrino and low mass dark matter detection. These low threshold bolometric detection techniques have wide applicability in nuclear and particle physics, including searching for low mass weakly-interacting dark matter; detecting neutrinos via coherent neutrino scattering; searching for axions and nuclear non-proliferation. In addition, we explored how using techniques readily used by the quantum computing community–such as multiplexed microwave resonators and quantum amplifiers—could be used as a low noise readout scheme for an array of superconducting detectors. We briefly summarized the accomplishments under the project, and what future research directions have spawned from this research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1997

Visualization of IACG data

The common data format (CDF), adopted by the international solar terrestrial physics (ISTP) program and the Interagency Consultative Group (IACG), is considered. Extensive data sharing and data analysis are needed to ensure the success of the ISTP/IACG program. The CDF user guidelines and tools for creation, visualization and manipulation are underlined. Computer software applications based on CDF data sets and designed using ISTP/IACG guidelines are shown.

Kessel, Ramona L.↗
NASA NTRS2015

Semi-Empirical Prediction of Aircraft Low-Speed Aerodynamic Characteristics

This paper lays out a comprehensive methodology for computing a low-speed, high-lift polar, without requiring additional details about the aircraft design beyond what is typically available at the conceptual design stage. Introducing low-order, physics-based aerodynamic analyses allows the methodology to be more applicable to unconventional aircraft concepts than traditional, fully-empirical methods. The methodology uses empirical relationships for flap lift effectiveness, chord extension, drag-coefficient increment and maximum lift coefficient of various types of flap systems as a function of flap deflection, and combines these increments with the characteristics of the unflapped airfoils. Once the aerodynamic characteristics of the flapped sections are known, a vortex-lattice analysis calculates the three-dimensional lift, drag and moment coefficients of the whole aircraft configuration. This paper details the results of two validation cases: a supercritical airfoil model with several types of flaps; and a 12-foot, full-span aircraft model with slats and double-slotted flaps.

Olson, Erik D.↗