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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting microstructurally sensitive fatigue‐crack path in WE43 magnesium using high‐fidelity numerical modeling and three‐dimensional experimental characterization

Abstract Microstructurally small fatigue‐crack growth in polycrystalline materials is highly three‐dimensional due to sensitivity to local microstructural features (e.g., grains). One requirement for modeling microstructurally sensitive crack propagation is establishing the criteria that govern crack evolution, including crack deflection. Here, a high‐fidelity finite‐element modeling framework is used to assess the performance and validity of various crack‐growth criteria, including slip‐based metrics (e.g., fatigue‐indicator parameters), as potential criteria for predicting three‐dimensional crack paths in polycrystalline materials. The modeling framework represents cracks as geometrically explicit discontinuities and involves voxel‐based remeshing, mesh‐gradation control, and a crystal‐plasticity constitutive model. The predictions are compared to experimental measurements of WE43 magnesium samples subject to fatigue loading, for which three‐dimensional grain structures and fatigue‐crack surfaces were measured post‐mortem using near‐field high‐energy x‐ray diffraction microscopy and x‐ray computed tomography. Findings from this work are expected to improve the predictive capabilities of simulations involving microstructurally small fatigue‐crack growth in polycrystalline materials.

Engineering↗
DOE OSTI2022

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scales at O(n log 2 n). We show that this approximation’s Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A High-Fidelity Modeling Tool to Support the Design of Oxy-Combustors for Direct-Fired Supercritical CO2 Cycles

The challenge in the design of oxy-combustors for direct-fired supercritical CO 2 (sCO 2 ) cycles is in addressing disparate performance metrics and objectives. Key design parameters to consider include, among others, injector design for mixing and flame stability, split of recycled CO 2 diluent between injectors and cooling films, target flame temperatures to control noncondensable products, and strategies to inject the diluent CO 2 for film cooling and thermal control. In order to support novel oxy-combustor designs, a high-fidelity yet numerically efficient modeling framework based on the CRUNCH CFD® flow solver is presented, featuring key physics-based submodels relevant in this regime. For computational efficiency in modeling large kinetic sets, a flamelet/progress variable (FPV) based tabulated-chemistry approach is utilized featuring a three-stream extension to allow for the simulation of the CO 2 film cooling stream in addition to the fuel and oxidizer streams. Finite rate chemistry effects are modeled in terms of multiple progress variables for the primary flame as well as for slower-evolving chemical species such as NO x and SO x contaminants. Real fluid effects are modeled using advanced equations of states. The predictive capabilities of this computationally tractable design support tool are demonstrated on a conceptual injector design for an oxy-combustor operating near 30 MPa. Simulations results provide quantitative feedback on the effectiveness of the film cooling as well as the level of contaminants (CO, NO, and N) in the exhaust due to impurities entering from the injectors. These results indicate that this framework would be a useful tool for refining and optimizing the oxy-combustor designs as well as risk mitigation analyses.

Engineering↗
DOE OSTI2022

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

Abstract Unsteadiness, in the form of both broadband background disturbances and discrete coherent wakes, can have a strong effect on the performance of turbomachinery blades. The influence of the incoming flow has received much interest as it inevitably affects the blade boundary layers and develops as it passes through the machine. In the present work, we investigate the effect of unsteady flow on high-pressure turbines (HPTs), using high-fidelity datasets produced by wall resolved large-eddy simulation of an HPT stage. The effects of incident wakes from an upstream stator, compounded by the presence of freestream turbulence, on the downstream rotor are investigated. Based on analyzing cases with different turbulence intensities and length scales prescribed at the inlet, we show that changing the freestream turbulence characteristics has a direct effect on the unsteady behavior of the stator wakes. As a result, the performance of the rotor is also significantly affected. By detailing the influence of the wake–turbulence interaction, we aim to distinguish driving forces on rotor performance, be it changes in the incident wakes or direct influence from the freestream turbulence. Furthermore, the aerothermal behaviors of the rotor blades have been extensively investigated, showing that the blade boundary layers on the suction and pressure sides respond differently to external disturbances. The insights gained can provide designers with guidelines in understanding the unsteady flow effects of a given flow state, and how the unsteadiness present, either broadband or deterministic, will affect the performance of downstream blades.

Engineering↗
DOE OSTI2023

High-Fidelity Energy Deposition Ignition Model Coupled with Flame Propagation Models at Engine-like Flow Conditions

With the heightened pressure on car manufacturers to increase the efficiency and reduce the carbon emissions of their fleets, more challenging engine operation has become a viable option. Highly dilute, boosted, and stratified charge, among others, promise engine efficiency gains and emissions reductions. At such demanding engine conditions, the spark-ignition process is a key factor for the flame initiation propagation and the combustion event. From a computational standpoint, there exist multiple spark-ignition models that perform well under conventional conditions but are not truly predictive under strenuous engine operation modes, where the underlying physics needs to be expanded. In this paper, a hybrid Lagrangian-Eulerian spark-ignition (LESI) model is coupled with different turbulence models, grid sizes, and combustion models. The ignition model, previously developed, relies on coupling Eulerian energy deposition with a Lagrangian particle evolution of the spark channel, at every time-step. The spark channel is attached to the electrodes and allowed to elongate at a speed derived from the flow velocity. The LESI model is used to simulate spark ignition in a nonquiescent crossflow environment at engine-like conditions, using converge commercial computational fluid dynamics (CFD) solver. The results highlight the consistency, robustness, and versatility of the model in a range of engine-like setups, from typical with Reynolds-averaged Navier-Stokes (RANS) and a larger grid size to high fidelity with large-eddy simulation (LES) and a finer grid size. The flame kernel growth is then evaluated against Schlieren images from an optical constant volume ignition chamber with a focus on the performance of flame propagation models, such as G-equation and thickened flame model, versus the baseline well-stirred reactor model. Finally, future development details are discussed.

Advanced ignition modeling↗
DOE OSTI2024

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling.

The recent growth in multifidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to provide variance reduction for estimates of the statistics of a high-fidelity model. In this paper we provide two contributions: (1) we utilize an ensemble estimator to account for uncertainties in the optimal weights of approximate control variate (ACV) approaches and derive lower bounds on the number of samples required to guarantee variance reduction; and (2) we extend an existing multifidelity importance sampling (MFIS) scheme to leverage control variates. Our approach directly addresses a limitation of many multifidelity sampling strategies that require the usage of pilot samples to estimate covariances. As such we make significant progress towards both increasing the practicality of approximate control variates—for instance, by accounting for the effect of pilot samples—and using multifidelity approaches more effectively for estimating low-probability events. The numerical results indicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in variance reduction over the existing state-of-the-art MFIS estimator, which had already shown an outstanding convergence rate compared to the Monte Carlo method, on several problems of computational mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Towards a High Fidelity Training Environment for Autonomous Cyber Defense Agents

Cyber defenders are overwhelmed by the frequency and scale of attacks against their networks. This problem will only be exacerbated as attackers leverage AI to automate their workflows. Autonomous cyber defense capabilities could aid defenders by automating operations and adapting dynamically to novel threats. However, existing training environments fall short in areas such as generalization, explainability, scalability, and transferability, making it intractable to train agents that will be effective in real networks. In this paper we take an important step towards creating autonomous cyber defense agents — we present a high fidelity training environment called Cyberwheel that includes both simulation and emulation capabilities. Cyberwheel simplifies customization of the training network and easily allows redefining the agent’s reward function, observation space, and action space to support rapid experimentation of novel approaches to agent design. It also provides visibility into agent behaviors necessary for agent evaluation and sufficient documentation / examples to lower the barrier to entry. As an example use case of Cyberwheel, we present initial results training an autonomous agent to deploy cyber deception strategies in simulation.

Oesch, T↗
DOE OSTI2025

A Scalable Multi-Modal Framework for High-Fidelity Distributed Human Mobility Simulations

The development of data-driven models for human mobility in urban settings requires access to substantial and diverse real-world data. However, existing historical data often presents challenges such as limited volume, variety, and veracity, as well as missing data and privacy preservation concerns. Also, urban mobility modeling is inherently time-variant, complex, and multi-modal, encompassing everything from individual walking and running to private road travel and large-scale public transportation. These challenges call for innovative solutions to overcome data limitations and compute needs to model mobility behaviors accurately. To address these challenges, we propose a distributed, co-simulation-based architecture DURMOSim that integrates real-world data with scalable, high-fidelity simulations, demonstrating distributed co-simulation feasibility with existing mobility models. DURMOSim underpins a modular integration that would enable using any available mobility simulators for greater extensibility and scalability in performing various urban scenarios. In this paper, we present the design, implementation, and performance evaluation of DURMOSim, highlighting its capability to model population-scale mobility patterns. Our initial results show its ability to dynamically synchronize multiple simulation models at runtime with negligible computational overhead. We believe DURMOSim could be a robust tool for advancing urban mobility research and intelligent transportation systems.

Yoginath, Srikanth [ORNL] (ORCID:0000000184236050)↗
DOE OSTI2026

QEC-fidelity

Code for paper "Universal Optimization and Tighter Fidelity Bounds for Approximate Quantum Error Correction" https://doi.org/10.48550/arXiv.2607.24968

Wu, Jing [Fermi National Accelerator Laboratory (F↗
DOE OSTI2021

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science

We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of the Whole Device Modeling Application (WDMApp) in the Exascale Computing Project. EFFIS consists of a library, command line utilities, and a collection of run-time daemons. Together, these software products enable users to easily compose and execute workflows that include: strong or weak coupling, in situ (or offline) analysis/visualization/monitoring, command-and-control actions, remote dashboard integration, and more. We describe WDMApp physics coupling cases and computer science requirements that motivate the design of the EFFIS framework. Furthermore, we explain the essential enabling technology that EFFIS leverages: ADIOS for performant data movement, PerfStubs/TAU for performance monitoring, and an advanced COUPLER for transforming coupling data from its native format to the representation needed by another application. Finally, we demonstrate EFFIS using coupled multi-simulation WDMApp workflows and exemplify how the framework supports the project’s needs. We show that EFFIS and its associated services for data movement, visualization, and performance collection does not introduce appreciable overhead to the WDMApp workflow and that the resource-dominant application’s idle time while waiting for data is minimal.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2021

Effects of as-received defects on ceramic matrix composites properties using high-fidelity microstructures with periodic boundary conditions

The presence of microstructural defects in as-received specimens of ceramic matrix composites (CMCs) significantly influences their constitutive response and damage, highlighting the importance of characterization and quantification of these defects for accurate assessment of damage and failure in the service environment. In a recent effort, the authors developed an algorithm to generate stochastic representative volume elements (SRVEs) of Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs based on extensive multiscale material and defect characterization data. This paper implements this algorithm within a commercial finite element solver with periodic boundary conditions (PBCs) for high-fidelity micromechanics analysis and investigation of macroscopic material behavior of C/SiNC composites. Different loading directions are used to predict the global mechanical properties, and the results are in excellent agreement with theoretical (rule of mixture) predictions. Subsequently, the effects of as-received defects on the global and local responses are investigated. The results show that intratow porosity has pronounced degradation effects on the global elastic properties and results in complex stress localization patterns, which can be attributed to potential damage initiation sites.

Khafagy, Khaled H.↗
DOE OSTI2021

Data from PNA-Assisted DNAzymes to Cleave Double-Stranded DNA for Genetic Engineering with High Sequence Fidelity

DNAzymes have been widely used in many sensing and imaging applications but have rarely been used for genetic engineering since their discovery in 1994, because their substrate scope is mostly limited to single-stranded DNA or RNA, whereas genetic information is stored mostly in double-stranded DNA (dsDNA). To overcome this major limitation, we herein report peptide nucleic acid (PNA)-assisted double-stranded DNA nicking by DNAzymes (PANDA) as the first example to expand DNAzyme activity toward dsDNA. We show that PANDA is programmable in efficiently nicking or causing double strand breaks on target dsDNA, which mimics protein nucleases and can act as restriction enzymes in molecular cloning. In addition to being much smaller than protein enzymes, PANDA has a higher sequence fidelity compared with CRISPR/Cas under the condition we tested, demonstrating its potential as a novel alternative tool for genetic engineering and other biochemical applications.

Conversion↗
DOE OSTI2022

Development of a high-fidelity multi-cycle model of the NuScale small modular reactor using VERA

With growing renewables penetration, there is increased interest in flexible power operation for nuclear reactors. For multi-unit SMRs, in particular the NuScale SMR, which is an integral pressurized water reactor, there are opportunities to optimize flexible power operation across multiple units to limit the degradation of structural and control components. Here, we focus on degradation of in-core components, specifically the control rods and reactor pressure vessel. To perform these studies a high-fidelity, multi-cycle representation of the NuScale SMR is required, with a detailed representation of the structural and control components. To this end, the NuScale SMR has been modelled using the Virtual Environment for Reactor Applications (VERA) software. The entire transition to equilibrium is simulated, from Cycle 1 through to the equilibrium cycle. The equilibrium cycle model shows a good agreement with the NuScale design certification application (DCA) results, with differences attributable to a combination of using public domain data for the present study, and methodological differences. K-effective, power distributions, reactivity coefficients, and boron letdown curves are compared and all found to closely match. This shows that the VERA model is suitable for further studies. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Scalable photonic integrated circuits for high-fidelity light control

Advances in laser technology have driven discoveries in atomic, molecular, and optical (AMO) physics and emerging applications, from quantum computers with cold atoms or ions, to quantum networks with solid-state color centers. This progress is motivating the development of a new generation of optical control systems that can manipulate the light field with high fidelity at wavelengths relevant for AMO applications. These systems are characterized by criteria: (C1) operation at a design wavelength of choice in the visible (VIS) or near-infrared (IR) spectrum, (C2) a scalable platform that can support large channel counts, (C3) high-intensity modulation extinction and (C4) repeatability compatible with low gate errors, and (C5) fast switching times. Here, we provide a pathway to address these challenges by introducing an atom control architecture based on VIS-IR photonic integrated circuit (PIC) technology. Based on a complementary metal–oxide–semiconductor fabrication process, this atom-control PIC (APIC) technology can meet system requirements (C1)–(C5). As a proof of concept, we demonstrate a 16-channel silicon-nitride-based APIC with (5.8±0.4)ns response times and >30dB extinction ratio at a wavelength of 780 nm.

Menssen, Adrian J.↗
DOE OSTI2024

Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models

Multifidelity (MF) uncertainty quantification (UQ) seeks to leverage and fuse information from a collection of models to achieve greater statistical accuracy with respect to a single-fidelity counterpart, while maintaining an efficient use of computational resources. Despite many recent advancements in MF UQ, several challenges remain and these often limit its practical impact in certain application areas. In this manuscript, we focus on the challenges introduced by nondeterministic models to sampling MF UQ estimators. Nondeterministic models produce different responses for the same inputs, which means their outputs are effectively noisy. MF UQ is complicated by this noise since many state-of-the-art approaches rely on statistics, e.g., the correlation among models, to optimally fuse information and allocate computational resources. Here, we demonstrate how the statistics of the quantities of interest, which impact the design, effectiveness, and use of existing MF UQ techniques, change as functions of the noise. With this in hand, we extend the unifying approximate control variate framework to account for nondeterminism, providing for the first time a rigorous means of comparing the effect of nondeterminism on different multifidelity estimators and analyzing their performance with respect to one another. Numerical examples are presented throughout the manuscript to illustrate and discuss the consequences of the presented theoretical results.

97 MATHEMATICS AND COMPUTING↗