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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wassersplines for Neural Vector Field-Controlled Animation

Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. Here we introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in continuous normalizing flows and optimal transport. The key idea is to train a neurally-parameterized velocity field that represents the motion between keyframes. Trajectories are then computed by advecting keyframes through the velocity field. We solve an additional Wasserstein barycenter interpolation problem to guarantee strict adherence to keyframes. Our tool can stylize trajectories through a variety of PDE-based regularizers to create different visual effects. We demonstrate our tool on various keyframe interpolation problems to produce temporally-coherent animations without meshing or rigging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Engineering↗
DOE OSTI2020

Toward a Theory of Superdense Time in Simulation Models

In this work, we develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. Furthermore, the sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed in terms of a mathematical system, constitutes a state transition function. Several forms of superdense time, both known and novel, are shown to satisfy the axioms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tackling Climate Change with Machine Learning

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Virtual Time III, Part 1: Unified Virtual Time Synchronization for Parallel Discrete Event Simulation

Algorithms for synchronization of parallel discrete event simulation have historically been divided between conservative methods that require lookahead but not rollback, and optimistic methods that require rollback but not lookahead. In this paper we present a new approach in the form of a framework called Unified Virtual Time (UVT) that unifies the two approaches, combining the advantages of both within a single synchronization theory. Whenever timely lookahead information is available, a logical process (LP) executes conservatively using an irreversible event handler. When lookahead information is not available the LP does not block, as it would in a classical conservative execution, but instead executes optimistically using a reversible event handler. The switch from conservative to optimistic synchronization and back is decided on an event-by-event basis by the simulator, transparently to the model code. UVT treats conservative synchronization algorithms as optional accelerators for an underlying optimistic synchronization algorithm, enabling the speed of conservative execution whenever it is applicable, but otherwise falling back on the generality of optimistic execution. We describe UVT in a novel way, based on fundamental invariants, monotonicity requirements, and synchronization rules. UVT permits zero-delay messages and pays careful attention to tie-handling using superposition. We prove that under fairly general conditions a UVT simulation always makes progress in virtual time. This is Part 1 of a trio of papers describing the UVT framework for PDES, mixing conservative and optimistic synchronization and integrating throttling control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Virtual Time III, Part 2: Combining Conservative and Optimistic Synchronization

This is Part 2 of a trio of works intended to provide a unifying framework in which conservative and optimistic synchronization for parallel discrete event simulations can be freely and transparently combined in the same logical process on an event-by-event basis. Here, in this article, we continue the outline of an approach called Unified Virtual Time (UVT) that was introduced in Part 1, showing in detail via two extended examples how conservative synchronization can be refactored and combined with optimistic synchronization in the UVT framework. We describe UVT versions of both a basic time windowing algorithm called Unified Simple Time Windows and a refactored version of the Chandy-Misra-Bryant Null Message algorithm called Unified CMB.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximate best-response for the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Readiness for AI: A 360-Degree Survey

Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Generative Vulnerability Assessment for Cyber-Physical Systems

Cyber-physical systems (CPS) are highly susceptible to malicious attacks due to their complex dynamics and interconnectivity. A comprehensive understanding of their vulnerabilities is essential for designing effective resilience measures. This paper presents a data-driven attack generative system for evaluating the vulnerability of CPS. The proposed approach formulates the vulnerability assessment problem as determining the feasibility of a specific attack set based on two boundary functions that represent the effectiveness and stealthiness of attacks. The attack generative model is trained using a custom loss function, with two universal approximators designed to learn the effectiveness and stealthiness functions simultaneously. Theoretical results for successful generation and asymptotic convergence of the resulting training algorithm are given. As a result, the proposed approach is evaluated via numerical simulation of an IEEE 14-bus system and gas pipeline systems, demonstrating its viability in learning how to attack nonlinear CPS and identify potential vulnerabilities.

Computer systems organization↗
DOE OSTI2014

Low-Cost Spectral Sensor Development Description

Solar spectral data for all parts of the US is limited due in part to the high cost of commercial spectrometers. Solar spectral information is necessary for accurate photovoltaic (PV) performance forecasting, especially for large utility-scale PV installations. A low-cost solar spectral sensor would address the obstacles and needs. In this report, a novel low-cost, discrete-band sensor device, comprised of five narrow-band sensors, is described. The hardware is comprised of commercial-off-the-shelf components to keep the cost low. Data processing algorithms were developed and are being refined for robustness. PV module short-circuit current ($I_{sc}$) prediction methods were developed based on interaction-terms regression methodology and spectrum reconstruction methodology for computing $I_{sc}$. The results suggest the computed spectrum using the reconstruction method agreed well with the measured spectrum from the wide-band spectrometer (RMS error of 38.2 W/m 2 -nm). Further analysis of computed $I_{sc}$ found a close correspondence of 0.05 A RMS error. The goal is for ubiquitous adoption of the low-cost spectral sensor in solar PV and other applications such as weather forecasting.

Armijo, Kenneth Miguel↗
DOE OSTI2013

Dynamics and Stability of Rolling Viscoelastic Tires

Current steady state rolling tire calculations often do not include treads because treads destroy the rotational symmetry of the tire. We describe two methodologies to compute time periodic solutions of a two-dimensional viscoelastic tire with treads: solving a minimization problem and solving a system of equations. We also expand on work by Oden and Lin on free spinning rolling elastic tires in which they disovered a hierachy of N-peak steady state standing wave solutions. In addition to discovering a two-dimensional hierarchy of standing wave solutions that includes their N-peak hiearchy, we consider the eects of viscoelasticity on the standing wave solutions. Finally, a commonplace model of viscoelasticity used in our numerical experiments led to non-physical elastic energy growth for large tire speeds. We show that a viscoelastic model of Govindjee and Reese remedies the problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Deep Borehole Emplacement Mode Hazard Analysis (Rev. 0)

This letter report outlines a methodology and provides resource information for the Deep Borehole Emplacement Mode Hazard Analysis (DBEMHA). The main purpose is identify the accident hazards and accident event sequences associated with the two emplacement mode options (wireline or drillstring), to outline a methodology for computing accident probabilities and frequencies, and to point to available databases on the nature and frequency of accidents typically associated with standard borehole drilling and nuclear handling operations. Risk mitigation and prevention measures, which have been incorporated into the two emplacement designs (see Cochran and Hardin 2015), are also discussed. A key intent of this report is to provide background information to brief subject matter experts involved in the Emplacement Mode Design Study. [Note: Revision 0 of this report is concentrated more on the wireline emplacement mode. It is expected that Revision 1 will contain further development of the preliminary fault and event trees for the drill string emplacement mode.]

42 ENGINEERING↗
DOE OSTI2015

On a Thermodynamic Approach to Material Selection for Service in Aggressive Multi-Component Gaseous and/or Vapor Environments

This report fulfills the M4 milestone, M4FT-15IN08020110 UNF Analysis Support, under Work Package Number FT-15IN080201. The issue of materials selection for many engineering applications represents an important problem, particularly in cases where material failure is possible as a result of corrosive environments. For example, 304 dual purpose or 316 stainless steel is used in the construction of many used nuclear fuel storage canisters. Deployed all over the world, these canisters are housed inside shielded enclosures and cooled passively by convective airflow. When located along seaboards or particular industrial areas, salt, other corrosive chemicals, and moisture can become entrained in the air that cools the canisters. It is important to develop an understanding of what impact, if any, that chemical environment will have on those canisters. In many cases of corrosion in aggressive gaseous environments, the material selection process is based on some general recommendations, anecdotal evidence, and/or the past experience of that particular project’s participants. For gaseous mixtures, the theoretical basis is practically limited to the construction of the so-called “Ellingham diagrams” for pure metals. These plots predict the equilibrium temperature between different individual metals, their respective oxides, and oxygen gas. Similar diagrams can be constructed for the reactions with sulfur, nitrogen, carbon, etc. In the generalization of this approach by Richardson and Jeffes, additional scales can be superimposed upon an Ellingham diagram that would correspond to different gaseous mixtures, e.g. CO/CO 2 , or H 2 /H 2 O. However, while the general approach to predicting the stability of a multi-component heterogeneous alloy (e.g., steel or a superalloy) in a multi-component aggressive gaseous environment was developed in very general form, actual examples of its applications to concrete real-life problems are practically absent. This is related to alloy design, corrosion protection, and material selection for different applications. In this work, an effort was made to advance in that direction using modern computational thermodynamics methodology, software, and databases by Thermo-Calc Inc. The developed methodology is illustrated by the case study – a process of nuclear waste immobilization using a chemical engineering approach described below. The developed methodology can be considered a practical illustration of the Ellingham approach generalization and could be used for obtaining thermodynamic guidance on a given process’ feasibility using equipment/sensors made of a particular multicomponent heterogeneous metallic alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Deep Borehole Emplacement Mode Hazard Analysis (Rev. 1)

This report outlines a methodology and provides resource information for the Deep Borehole Emplacement Mode Hazard Analysis (DBEMHA). The main purpose is identify the accident hazards and accident event sequences associated with the two emplacement mode options (wireline or drillstring), to outline a methodology for computing accident probabilities and frequencies, and to point to available databases on the nature and frequency of accidents typically associated with standard borehole drilling and nuclear handling operations.

42 ENGINEERING↗
DOE OSTI2025

Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware

Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-based approaches often have delays that are too long to be applied in practice. It is paramount to have systems with extremely low delays to quickly and with high fidelity adjust quantum hardware settings, where fidelity is defined as overlap with a target quantum state. Here, we utilize machine learning (ML) models to determine control-pulse parameters for preparing Selective Number-dependent Arbitrary Phase (SNAP) gates in microwave cavity qudits, which are multi-level quantum systems that serve as elementary computation units for quantum computing. The methodology involves data generation using classical optimization techniques, ML model development, design space exploration, and quantization for hardware implementation. Our results demonstrate the efficacy of the proposed approach, with optimized models achieving low gate trace infidelity near $10^{-3}$ and efficient utilization of programmable logic resources.

Sanders, Flor [Columbia U.]↗
DOE OSTI2026

Ab Initio Many Body Quantum Embedding and Local Correlation in Crystalline Materials using Interpolative Separable Density Fitting

We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit.

Chemical Physics (physics.chem-ph)↗