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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. Furthermore, the results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Convection in the earth's mantle

The computer is used to solve for thermal convection within the earth's mantle. A review of the knowledge of surface displacements and of the present understanding of the mantle and its relevant physical and chemical properties is contained in the paper. Applicable equations assume a Newtonian fluid layer heated from below and within, with gravity acting downward. The numerical method employs finite differences and was constructed with a view toward the faithful simulation of coupling mechanisms. It enables surveying the effect of a parameter using a relatively coarse computing mesh. Some of the results obtained are presented.

Turcotte, D. L.↗
DOE OSTI2021

An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Rao, Nageswara↗
NASA NTRS2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recent work has shown that these errors can be effectively predicted usingmodern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previousstudies to apply an interpretable A.I. technique to not only predict model errors but also movetoward understanding the underlying reasons for successful error prediction. We use XGBoostclassification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors inthe prediction of lightning occurrence in the NASA GEOS model, a widely used Earth SystemModel. This explainable error prediction system can effectively predict the model error andindicates that the errors are strongly related to convective processes and the characteristics ofthe land surface.

Artificial intelligence↗
DOE OSTI2022

Grassmannian Shape Representations for Aerodynamic Applications

Airfoil shape design is a classical problem in engineering, science, and manufacturing. Our motivation is to combine principled physics-based considerations for the shape design problem with modern computational techniques informed by a data-driven approach. Traditional analyses of airfoil shapes emphasize a flow-based sensitivity to deformations which can be represented generally by affine transformations (rotation, scaling, shearing, shifting). We present a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) improved low-dimensional parameter domain for inferential statistics, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes.

blade representation↗
NASA NTRS2010

MIRO Computational Model

A computational model calculates the excitation of water rotational levels and emission-line spectra in a cometary coma with applications for the Micro-wave Instrument for Rosetta Orbiter (MIRO). MIRO is a millimeter-submillimeter spectrometer that will be used to study the nature of cometary nuclei, the physical processes of outgassing, and the formation of the head region of a comet (coma). The computational model is a means to interpret the data measured by MIRO. The model is based on the accelerated Monte Carlo method, which performs a random angular, spatial, and frequency sampling of the radiation field to calculate the local average intensity of the field. With the model, the water rotational level populations in the cometary coma and the line profiles for the emission from the water molecules as a function of cometary parameters (such as outgassing rate, gas temperature, and gas and electron density) and observation parameters (such as distance to the comet and beam width) are calculated.

Broderick, Daniel↗
DOE OSTI2024

Liquid metal walls

Here, the plasma performance of fusion devices depends strongly on the chosen wall materials. Solid plasma-facing components (PFCs) are predominantly used in present devices, and are the most investigated candidates for fusion designs. High-Z materials such as tungsten (W) are the leading solid PFC material candidates. To date, a material choice that scales to steady-state reactor conditions has not been identified. Moreover, if plasma transient events such as edge-localized modes and disruptions cannot be altogether avoided or sufficiently mitigated, the projected peak heat and particle loads far exceed the power exhaust capabilities of solids. Liquid metal (LM) PFCs represent an intrinsic self-healing boundary that are both resilient to surface damage from transients, and that could handle high steady-state heat and particle fluxes. Flowing LM PFCs can be designed to remove “slag,” the buildup of material erosion due to plasma-material interactions, including charge exchange sputtering in the main chamber. Further, LM offer the prospect to manage hydrogenic species otherwise retained in the PFCs, which is important from a safety and inventory standpoint. The two most promising LMs are lithium (Li) and tin (Sn), although Sn–Li eutectics may be considered. While Sn offers a higher temperature window with low vapor pressure and low hydrogen retention, Li offers the prospect of enhanced energy confinement and higher acceptable core contamination limits, and this section focuses on Li PFCs. An LM PFC development research program developed LM PFC concepts for a nuclear fusion device via engineering design calculations, single-effect experiments, and staged prototypical experiments. A self-consistent design window was identified with liquid Li flow speeds ~5–10 m/s; plasma contamination was negligible for predicted Li evolution rates. While these preconceptual designs hold promise, there is substantial R&D needed to advance the technical readiness levels of LM PFCs for application to fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

AMReX: Block-structured adaptive mesh refinement for multiphysics applications

Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.

Zhang, Weiqun↗
DOE OSTI2022

Three-dimensional electron temperature measurement of inertial confinement fusion hotspots using x-ray emission tomography

Here we present a novel approach to reconstruct three-dimensional (3D) electron temperature distributions of inertially confined fusion plasma hotspots at the National Ignition Facility. Using very limited number of two-dimensional (2D) x-ray imaging lines of sight, we perform 3D reconstructions of x-ray emission distributions from different x-ray energy channels ranging from 20 to 30 keV. 2D time-integrated x-ray images are processed using the algebraic reconstruction technique to reconstruct a 3D hotspot x-ray emission distribution that is self-consistent with the input images. 3D electron temperatures are computed using the energy channel ratios. We demonstrate the high accuracy and applicability of this method with different complex hotspot geometries in both synthetic and experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
NASA NTRS2012

A Computational Investigation of Gear Windage

A CFD method has been developed for application to gear windage aerodynamics. The goals of this research are to develop and validate numerical and modeling approaches for these systems, to develop physical understanding of the aerodynamics of gear windage loss, including the physics of loss mitigation strategies, and to propose and evaluate new approaches for minimizing loss. Absolute and relative frame CFD simulation, overset gridding, multiphase flow analysis, and sub-layer resolved turbulence modeling were brought to bear in achieving these goals. Several spur gear geometries were studied for which experimental data are available. Various shrouding configurations and free-spinning (no shroud) cases were studied. Comparisons are made with experimental data from the open literature, and data recently obtained in the NASA Glenn Research Center Gear Windage Test Facility. The results show good agreement with experiment. Interrogation of the validative and exploratory CFD results have led, for the first time, to a detailed understanding of the physical mechanisms of gear windage loss, and have led to newly proposed mitigation strategies whose effectiveness is computationally explored.

Hill, Matthew J.↗
DOE OSTI2024

ML-based Data Assimilation and History Matching: Application to the IBDP CCS Project

It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of large-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir.The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. The data assimilation process is implemented rapidly by the proposed workflow with given field measurements including distributed pressure and temperature sensing (DTS) data at an injection and a monitoring well. CO2 plume evolution is predicted by running the simulations of the calibrated reservoir models.

Nagao, Masahiro↗
DOE OSTI2021

Multi-scale Coupled Processes Modeling of Fractures as Porous, Interfacial and Granular Systems from Rock Images with the Numerical Manifold Method

Abstract The greatest challenges of rigorously modeling coupled hydro-mechanical processes in fractured rocks at different scales are associated with computational geometry. In addition, selections of continuous or discontinuous models, physical laws, and coupling priorities at different scales based on different geometric features determine the applicability of a numerical model for a certain type of problem. In this study, we present our multi-scale modeling capabilities that have been developed based on the numerical manifold method for analyzing coupled hydro-mechanical processes in fractured rocks. Based on their geometric features, the fractures are modeled as continua—finite-thickness porous zones, and discontinua—discontinuous interfaces and microscale asperities and granular systems. Different governing equations, physical laws, coupling priorities, and approaches for addressing fracture intersections and shearing are then applied to describe these. We applied these models to simulate coupled processes in fractured rocks using realistic geometry obtained from rock images at different scales. We first calculated shearing of a single fracture with different models and demonstrated the impacts of asperities on shearing. We then applied the continuous and discontinuous models to simulate a network of rough fractures, demonstrating that contact dynamics contribute significantly to the geometric, multi-physical evolution of systems where rough fractures are not mineral filled. For a discrete fracture network, our coupled processes modeling demonstrates that shearing of the discrete fractures can have a major impact on stress and pore pressure distribution. Lastly, we applied the discontinuous granular model to simulate evolution of a complex granular system with a deformation band, demonstrating that the deformation band can dominate contact dynamics, the structural and the stress evolution of the granular system.

58 GEOSCIENCES↗
DOE OSTI2022

Search for Majorana neutrinos exploiting millikelvin cryogenics with CUORE

The possibility that neutrinos may be their own antiparticles, unique among the known fundamental particles, arises from the symmetric theory of fermions proposed by Ettore Majorana in 1937. Given the profound consequences of such Majorana neutrinos, among which is a potential explanation for the matter–antimatter asymmetry of the universe via leptogenesis, the Majorana nature of neutrinos commands intense experimental scrutiny globally; one of the primary experimental probes is neutrinoless double beta (0νββ) decay. Here we show results from the search for 0νββ decay of 130 Te, using the latest advanced cryogenic calorimeters with the CUORE experiment3. CUORE, operating just 10 millikelvin above absolute zero, has pushed the state of the art on three frontiers: the sheer mass held at such ultralow temperatures, operational longevity, and the low levels of ionizing radiation emanating from the cryogenic infrastructure. We find no evidence for 0νββ decay and set a lower bound of the process half-life as 2.2 × 10 25 years at a 90 percent credibility interval. We discuss potential applications of the advances made with CUORE to other fields such as direct dark matter, neutrino and nuclear physics searches and large-scale quantum computing, which can benefit from sustained operation of large payloads in a low-radioactivity, ultralow-temperature cryogenic environment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Electrically tuned hyperfine spectrum in neutral Tb( II )(Cp iPr5 ) 2 single-molecule magnet

Molecular spin qubits with long spin coherence time as well as non-invasive operation methods on such qubits are in high demand. It was shown that both molecular electronic and nuclear spin levels can be used as qubits. In solid state systems with dopants, an electric field was shown to effectively change the spacing between the nuclear spin qubit levels when the electron spin density is high at the nucleus of the dopant. Inspired by such solid-state systems, we propose that divalent lanthanide (Ln) complexes with an unusual electronic configuration of Ln 2+ have a strong interaction between the Ln nuclear spin and the electronic degrees of freedom, which renders electrical tuning of the interaction. Furthermore, as an example, we study electronic structure and hyperfine interaction of the 159 Tb nucleus in a neutral Tb(II)(Cp iPr5 ) 2 single-molecule magnet (SMM), which exhibits unusually long magnetization relaxation time, using the complete active space self-consistent field (CASSCF) method with spin–orbit interaction included within the restricted active space state interaction (RASSI). Our calculations show that the low-energy states arise from 4f 8 (6s,5d z 2 ) 1 , 4f 8 (5d x 2 -y 2 ) 1 , and 4f 8 (5d xy ) 1 configurations. We compute the hyperfine interaction parameters and the electronic–nuclear spectrum within our multiconfigurational approach. We find that the hyperfine interaction is about one order of magnitude greater than that for Tb(III)Pc 2 SMMs. This stems from the strong Fermi contact interaction between the Tb nuclear spin and the electron spin density at the nucleus that originates from the occupation of the (6s,5d) orbitals. We also uncover that the response of the Fermi contact term to electric field results in electrical tuning of the electronic–nuclear level separations. This hyperfine Stark effect may be useful for applications of molecular nuclear spins for quantum computing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗