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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Doses and fluxes in the proton-irradiated Big Target: A MARS15 analysis

This work is aimed at determination of secondary particle fluxes, prompt and residual doses as well as energy deposition in and around the Big Target, a 20-tonnes cylindrical target made of natural uranium with the lead core and a stainless-steel yoke, which is being considered to be irradiated by a 0.16-μA 600-MeV proton beam of the JINR Phasotron accelerator. The simulations were performed with the use of the MARS15 particle transport code employing the LAQGSM and CEM generators and based on the ENDFB-VIII neutron cross section library. The calculations for 12-hour, 100-hour, and 3-day irradiations with the aforementioned conditions indicate that temperature rise due to the beam does not require target cooling, the target can be safely handled soon after the end of irradiation, and its decommissioning is possible three years past the irradiation. Doses

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
DOE OSTI2015

First-Principles Petascale Simulations for Predicting Deflagration to Detonation Transition in Hydrogen-Oxygen Mixtures

Hydrogen has emerged as an important fuel across a range of industries as a means of achieving energy independence and to reduce emissions. DDT and the resulting detonation waves in hydrogen-oxygen can have especially catastrophic consequences in a variety of industrial and energy producing settings related to hydrogen. First-principles numerical simulations of flame acceleration and DDT are required for an in-depth understanding of the phenomena and facilitating design of safe hydrogen systems. The goals of this project were (1) to develop first-principles petascale reactive flow Navier-Stokes simulation code for predicting gaseous high-speed combustion and detonation (HSCD) phenomena and (2) demonstrate feasibility of first-principles simulations of rapid flame acceleration and deflagration-to-detonation transition (DDT) in stoichiometric hydrogen-oxygen mixture (2H 2 + O 2 ). The goals of the project have been accomplished. We have developed a novel numerical simulation code, named HSCD, for performing first-principles direct numerical simulations of high-speed hydrogen combustion. We carried out a series of validating numerical simulations of inert and reactive shock reflection experiments in shock tubes. We then performed a pilot numerical simulation of flame acceleration in a long pipe. The simulation showed the transition of the rapidly accelerating flame into a detonation. The DDT simulations were performed using BG/Q Mira at the Argonne National Laboratory, currently the fourth fastest super-computer in the world.

08 HYDROGEN↗
DOE OSTI2018

Microstructural characterization of laser metal powder deposited Alloy 718

Highlights: • In as-deposited condition γ′ and γ″ were found in the interdendritic regions. • JMatPro simulations confirmed accelerated precipitation in interdendritic regions. • Depletion of Niobium was still found in the dendritic core after heat treatment. • The depletion of Niobium were more noticeable when using a high heat input. • γ″ particles were less well-developed in the regions depleted of Niobium. - Abstract: A microstructural study of Laser Metal Powder Deposition (LMPD) of Alloy 718, using a low (40 J/mm) and high (100 J/mm) heat inputs (HIs) was performed. The microstructure was characterized in as-deposited condition as well as after a standard heat-treatment, using optical microscope (OM), scanning electron microscope (SEM) and Transmission Electron Microscope (TEM). Laves, MC-carbides, γ′ and γ″ are observed in the interdendritic areas of both conditions. However, the dendritic core only consists of γ-matrix. The high HI condition shows a slightly larger Primary Dendrite Arm Spacing (PDAS) as compared to the low HI condition. Additionally, the particle size of the Nb-rich constituents in the interdendritic regions (Laves-phase and Niobium carbide) is larger in the high HI sample. After heat-treatment, the Laves phase dissolves and is replaced by δ-phase in the interdendritic regions, while γ′, γ″ and MC-carbide remain in the interdendritic regions. However, the γ″ precipitates seems to be less developed in the dendritic core as compared to the interdendritic regions, especially in the high HI sample. This can be attributed to a heterogeneous distribution of Nb in the microstructure, with a lower Nb content in the dendritic core as compared to close to the interdendritic regions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ancilla-entangling Floquet kicks for accelerating quantum algorithms

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and thus places an annealing rate bottleneck for high success probability. Here, in this study, we accelerate quantum simulation using digital multiqubit gates that entangle primary system qubits with ancillary qubits. The practical benefits originate from tuning the ancillary gauge degrees of freedom to enhance the quantum algorithm's original functionality in the system registry. For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent but with higher accuracy through exact state-vector numerical simulation in a digital-analog setting. The findings are further supported by time-averaged Hamiltonian theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Comparison of FAST Experiments to BISON Simulations

The Fission Accelerated Steady-State Test (FAST) experiments utilize the effects of scaling to accelerate the burnup experienced by the fuel. This experimental technique offers a unique opportunity to evaluate the effects of scaling on fuel and cladding material modelling. Currently, models used in the BISON fuel performance code for metallic fuel performance are calibrated for EBR-II conditions and geometries. Comparing BISON simulations to FAST experimental data will allow for the identification of shortcomings in the models for conditions outside the EBR-II paradigm. FAST experiments have been performed on U-Zr and U-10Mo fuel types with HT9 cladding. Initial comparisons of simulated cladding strains to experimental results show that the BISON simulations consistently overpredict cladding strain in the fuel region but underpredict the cladding strain in the plenum region. This indicates potential issues with the simulation of swelling and gas pressure at smaller geometries. Further comparison with fission gas release measurements is planned as data becomes available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Species-specific transpiration responses to intermediate disturbance in a northern hardwood forest: Transpiration response to disturbance

Intermediate disturbances shape forest structure and composition, which may in turn alter carbon, nitrogen, and water cycling. We used a large-scale experiment in a forest in northern lower Michigan where we prescribed an intermediate disturbance by stem girdling all canopy-dominant early successional trees to simulate an accelerated age-related senescence associated with natural succession.Using 3 years of eddy covariance and sap flux measurements in the disturbed area and an adjacent control plot, we analyzed disturbance-induced changes to plot level and species-specific transpiration and stomatal conductance. We found transpiration to be ~15% lower in disturbed plots than in unmanipulated control plots. However, species-specific responses to changes in microclimate varied. While red oak and white pine showed increases in stomatal conductance during post disturbance (62.5 and 132.2%, respectively), redmaple reduced stomatal conductance by 36.8%. We used the hysteresis between sap flux and vapor pressure deficit to quantify diurnal hydraulic stress incurred by each species in both plots. Red oak, a ring porousanisohydric species, demonstrated the largest mean relative hysteresis, while red maple, bigtooth aspen, andpaper birch, all diffuse porous species, had the lowest relative hysteresis. We employed the Penman-Monteithmodel for LE to demonstrate that these species-specific responses to disturbance are not well captured using current modeling strategies and that accounting for changes to leaf area index and plot microclimate are insufffcient to fully describe the effects of disturbance on transpiration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning for accuracy in density functional approximations

Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. In this paper, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2018

OpenMP and MPI implementations of an elasto-viscoplastic fast Fourier transform-based micromechanical solver for fast crystal plasticity modeling

Here, we explore several parallel implementations of an elasto-viscoplastic fast Fourier transform (EVPFFT) model using Message Passing Interface (MPI), OpenMP, and a hybrid of MPI and OpenMP to efficiently predict micromechanical response of polycrystals. Performance studies using EVPFFT are performed based on domain decomposition over voxels of a periodic cell, which is a representative volume element (RVE) of polycrystalline copper. We begin by parallelizing the computationally intensive Newton–Raphson (NR) single crystal solver within EVPFFT. Next, we compare the performance of the serial and parallel FFTW (Fastest Fourier Transform in the West) using OpenMP (OpenMP-FFTW) and MPI (MPI-FFTW) with the original Numerical Recipes-based FOURN routine within EVPFFT. In the parallel environment, we find that the FFT calculations are best performed using the MPI version of FFTW. Finally, the remainder of the code, except read/write subroutines, is parallelized. Significant speedups of the original EVPFFT model are achieved using MPI on shared memory multicore workstations. Furthermore, results achieved on a distributed memory Cray supercomputer show promising strong and weak scalability and in some cases even super scalability for the single crystal NR solver in EVPFFT. MPI-FFTW also scales perfectly for microstructure RVEs larger than 64 3 FFT voxels. For example, the MPI-EVPFFT parallel version of the code accelerates the simulations for approximately two orders of magnitude using 64 cores over the old serial code for an RVE size of 128 3 . Finally, the parallel EVPFFT code developed in this work can run massive voxel-based microstructural RVEs taking the advantages of thousands of logical cores provided by more advanced clusters.

42 ENGINEERING↗
DOE OSTI2020

A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD

The three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm is a very efficient method for modeling short-pulse laser or relativistic charged particle beam–plasma interactions. In this algorithm, the plasma response, i.e., plasma wave wake, to a non-evolving laser or particle beam is calculated using a set of Maxwell’s equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. Additionally, at the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the algorithmic complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. This hybrid algorithm uses a PIC description in r–z and a gridless description in . We describe a novel method that combines the quasi-static and hybrid PIC methods. This algorithm expands the fields, charge and current density into azimuthal harmonics. A set of the quasi-static field equations is derived for each harmonic. The complex amplitudes of the fields are then solved using the finite difference method. The beam and plasma particles are advanced in Cartesian coordinates using the total fields. Details on how this algorithm was implemented using a similar workflow to an existing quasi-static code, QuickPIC, are presented. The new code is called QPAD for QuickPIC with Azimuthal Decomposition. Benchmarks and comparisons between a fully 3D explicit PIC code (OSIRIS), a full 3D quasi-static code (QuickPIC), and the new quasi-static PIC code with azimuthal decomposition (QPAD) are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
DOE OSTI2018

Additive manufacturing of functionally graded transition joints between ferritic and austenitic alloys

Dissimilar metal joints between ferritic and austenitic alloys are susceptible to premature failure due to diffusive carbon loss from the ferritic alloy driven by abrupt changes in carbon chemical potential. Compositional grading of transition joints fabricated using laser-based directed energy deposition additive manufacturing offers a means for limiting carbon diffusion. In this report we fabricate functionally graded transition joints between a ferritic and austenitic alloy, characterize spatial variations of chemical composition, microstructure and hardness, and test their effectiveness to limit carbon loss from the ferritic alloy. Microstructural studies and carbon potential variations in the functionally graded material showed that the length of the joints can be shorter, and there is no benefit to continue compositional grading once the microstructure becomes fully austenitic. Since dissimilar joints have an expected lifetime of several decades, long service times were simulated through accelerated heat treatment experiments at elevated temperatures for both a dissimilar metal weld and a functionally graded transition joint. While the dissimilar weld showed pronounced carbon loss from the ferritic side, there was insignificant change in the carbon concentration profile in the functionally graded joint indicating effectiveness of the graded joints to perform under service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Interpreting experimental measurements of helium bubbles using stochastic cluster dynamics models of heterogeneous nucleation and growth in irradiated ferritic alloys

Among a number of other advantageous features, ferritic/martensitic steels are being considered as fusion reactor structural materials due to their low intrinsic swelling under irradiation. However, under high-energy neutron irradiation, He produced through (n, α) reactions stabilizes vacancy clusters, which then act as seeds for bubble formation and growth, negating the intrinsic swelling resistance of these alloys. Standard models of irradiation damage accumulation and microstructural evolution consider homogeneous nucleation as the basis for bubble formation and growth. However, this generally does not explain the large bubble densities and sizes observed experimentally under a number of different conditions. Here, we propose a more realistic physical model of bubble nucleation, formation, and growth designed to capture recent experimental measurements of He-bubble formation and evolution during co-implantation of He and Fe ions in model ferritic alloys. We find that experimental results are explained only when the following three features are considered simultaneously: (i) heterogeneous nucleation of He-vacancy bubbles at defect sinks (e.g., dislocations, grain boundaries, and second-phase precipitates), (ii) nucleation and growth of bubbles via the ‘trap mutation’ mechanism (i.e., spontaneous production of Frenkel pairs due to absorption of He atoms), and (iii) transition from a growth-limited, He-stabilized bubble regime to a ‘runaway’ void-type growth scenario. The model is implemented into a stochastic cluster dynamics framework that takes advantage of cluster size grouping methods to accelerate the simulations, allowing us to reach 10 dpa of simulated irradiated dose, and to capture cluster sizes in excess of 20 nm. Further, a careful extrapolation exercise conducted assuming classical nucleation theory leads to excellent agreement with the experimental measurements at 50 dpa of irradiation.

36 MATERIALS SCIENCE↗