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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ChatBLAS: The First AI-Generated and Portable BLAS Library

We present ChatBLAS, the first AI-generated and portable Basic Linear Algebra Subprograms (BLAS) library on different CPU/GPU configurations. The purpose of this study is (i) to evaluate the capabilities of current large language models (LLMs) to generate a portable and HPC library for BLAS operations and (ii) to define the fundamental practices and criteria to interact with LLMs for HPC targets to elevate the trustworthiness and performance levels of the AI-generated HPC codes. The generated C/C++ codes must be highly optimized using device-specific solutions to reach high levels of performance. Additionally, these codes are very algorithm-dependent, thereby adding an extra dimension of complexity to this study. We used OpenAI’s LLM ChatGPT and focused on vector-vector BLAS level-1 operations. ChatBLAS can generate functional and correct codes, achieving high-trustworthiness levels, and can compete or even provide better performance against vendor libraries.

Valero Lara, Pedro↗
DOE OSTI2022

Generative Design for Resilience of Interdependent Network Systems

Abstract Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria while the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on synthetic supply chain networks and power systems from the IEEE dataset have illustrated the applicability of the developed method for designing resilient interdependent network systems.

Engineering↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Generative models for simulation of KamLAND-Zen

Abstract The next generation of searches for neutrinoless double beta decay ($$0 \nu \beta \beta $$ 0 ν β β ) are poised to answer deep questions on the nature of neutrinos and the source of the Universe’s matter–antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic$$0 \nu \beta \beta $$ 0 ν β β is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. We present their current ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.

Physics↗
DOE OSTI2020

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads

The Message Passing Interface (MPI) standard plays a crucial role in enabling scientific applications for parallel computing and is an essential component in high-performance computing (HPC). However, implementing MPI code manually—especially applying a proper domain decomposition and communication pattern—is a challenging and error-prone task. We present ChatMPI, an AI assistant for MPI parallelization of sequential C codes. In our analysis, we focus on testing six essential HPC workloads, which are based on Basic Linear Algebra Subprograms levels 1, 2, and 3 as well as sparse, stencil, and iterative operations. We analyze the process of creating ChatMPI by using the ChatHPC library. This lightweight large language model (LLM)–based infrastructure enables HPC experts to efficiently create and supervise trustworthy AI capabilities for critical HPC software tasks. We study the data required for training (fine-tuning) ChatMPI to generate parallel codes that not only use MPI syntax correctly but also apply HPC techniques to reduce memory communication and maximize performance by using proper work decomposition. With a relatively small training dataset composed of a few dozen prompts and fewer than 15 minutes of fine-tuning on one node equipped with two NVIDIA H100 GPUs, ChatMPI elevates trustworthiness for MPI code generation of current LLMs (e.g., Code Llama, ChatGPT-4o and ChatGPT 5). Additionally, we evaluate the performance of the MPI codes generated by ChatMPI in comparison with the ones generated by ChatGPT-4o and ChatGPT-5. The codes generated by ChatMPI provide up to a 4 × boost in performance by using better problem decomposition, communication patterns, and HPC techniques (e.g., communication avoiding).

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2021

Effect of Temperature and FEC on Silicon Anode Heat Generation Measured by Isothermal Microcalorimetry

Isothermal microcalorimetry (IMC) was used to better understand parasitic reactions and heat generation from Si electrodes in the first 10 cycles using Li/Si half cells. Heat generation from cell polarization (ohmic heat), entropy changes (reversible heat), and parasitic reactions (parasitic heat) are separated and quantified. The effect of temperature and fluoroethylene carbonate (FEC) as an electrolyte additive are also explored. Our results show that at the C/10 cycling rate used here, ohmic heat makes the largest contribution to overall heat generation while reversible heat is the smallest. Ohmic heat generation increases with cycle number due to increasing internal resistance, though the effect is smaller for cells with FEC. Interestingly, capacity-normalized parasitic heat generation is largely unaffected by changes in temperature despite differing reaction kinetics. We show that this is caused by a decrease in average parasitic reaction enthalpy as temperature is increased. Further, cells with FEC display higher average parasitic reaction enthalpy than cells without. The average parasitic reaction enthalpies for all the Si electrodes we tested were lower than previously reported values for graphite, indicating that the SEI formed on Si is less stable.

25 ENERGY STORAGE↗
DOE OSTI2020

Heat Generation versus Heat Dissipation Maps for Batteries (Battery Safety Maps) [SWR-21-27]

NREL's Battery Safety Map (Heat Generation versus Heat Dissipation Maps for Batteries) generates a response surface that is grouped into regions where the battery is deemed safe from the propagation of thermal runaway, those where an engineering solution would render the battery safe, and finally instances where thermal runaway is inevitable. The demarcation for the different regions is provided by specifications or constraints imposed by the end user. NREL's Battery Safety Map is a user-defined subroutine that sweeps through a set of parameters describing the operating conditions and short-circuit scenarios for battery safety simulations. For each run, the electrochemical-thermal model implementing reaction kinetics and gas generation are run to evaluate the amount of heat generated. A thermal model then captures the amount of heat dissipation based on boundary conditions defined (e.g., forced convection, constant temperature at the surface, etc.). Based on the rate of heat transfer and the amount of heat generated, each run is classified into "safe," "mitigation possible," or "unsafe" zones. These categories are defined based on constraints or tolerance specifications provided by the end user.

Santhanagopalan, Shriram↗
DOE OSTI2023

Code for the manuscript titled "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces"

We would like to disclose two scripts, written in Jupyter notebook, in which we implement the "Blackout Diffusion Process" described in the manuscript "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces" (LA-UR-23-20509), to be submitted to the International Conference of Machine Learning (ICML). The abstract of the manuscript is append below. == Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, which occur in many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. We relate the theory to the existing continuous-state Gaussian diffusion and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce "Blackout Diffusion", which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10 dataset confirm the feasibility of generative diffusion modeling in a discrete space. Generalizing from specific (Gaussian) forward processes to a more general framework also sheds light on how to interpret generative diffusion models and their mathematical structure, which we comment on.

Lin, Yen Ting↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Maximal nighttime electrical power generation via optimal radiative cooling

We present a systematic optimization of nighttime thermoelectric power generation system utilizing radiative cooling. We show that an electrical power density >2 W/m 2 , two orders of magnitude higher than the previously reported experimental result, is achievable using existing technologies. This system combines radiative cooling and thermoelectric power generation and operates at night when solar energy harvesting is unavailable. The thermoelectric power generator (TEG) itself covers less than 1 percent of the system footprint area when achieving this optimal power generation, showing economic feasibility. We study the influence of emissivity spectra, thermal convection, thermoelectric figure of merit and the area ratio between the TEG and the radiative cooler on the power generation performance. We optimize the thermal radiation emitter attached to the cold side and propose practical material implementation. The importance of the optimal emitter is elucidated by the gain of 153% in power density compared to regular blackbody emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Constructing the infrared conformal generators on the fuzzy sphere

We investigate the conformal algebra on the fuzzy sphere, and in particular the generators of translations and special conformal transformations which are emergent symmetries in the infinite IR but are broken along the RG flow. We show how to extract these generators using the energy momentum tensor, which is complicated by the fact that one does not have a priori access to the energy momentum tensor of the CFT limit but rather must construct it numerically. We discuss and quantitatively analyze the main sources of corrections to the conformal generators due to the breaking of scale-invariance at finite energy, and develop efficient methods for removing these corrections. The resulting generators have matrix elements that match CFT predictions with accuracy varying from sub-percent level for the lowest-lying states up to several percent accuracy for states with dimension \sim 5 ∼ 5 with N=16 N = 16 fermions. We show that the generators can be used to accurately identify primary operators vs descendant operators in energy ranges where the spectrum is too dense to do the identification solely based on the approximate integer spacing within conformal multiplets.

Fardelli, Giulia (ORCID:0000000217998124)↗
DOE OSTI2022

BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators

The parameters in Monte Carlo (MC) event generators are tuned on experimental measurements by evaluating the goodness of fit between the data and the MC predictions. The relative importance of each measurement is adjusted manually in an often time-consuming, iterative process to meet different experimental needs. In this work, we introduce several optimization formulations and algorithms with new decision criteria for streamlining and automating this process. These algorithms are designed for two formulations: bilevel optimization and robust optimization. Both formulations are applied to the datasets used in the ATLAS A14 tune and to the dedicated hadronization datasets generated by the SHERPA generator, respectively. The corresponding tuned generator parameters are compared using three metrics. We compare the quality of our automatic tunes to the published ATLAS A14 tune. Moreover, we analyze the impact of a pre-processing step that excludes data that cannot be described by the physics models used in the MC event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Seismo-Acoustic Wave Simulation for Earthquake-Generated Infrasound

It is well known that underground explosions or earthquakes can generate infrasound in the atmosphere. Seismoacoustic coupling between the ground motions and atmosphere is a key mechanism for infrasound generation. Epicentral infrasound which is direct response to epicentral ground motions are extensively studied for event detection and discrimination. Diffracted infrasound generated from Rayleigh waves are often observed and reported. Recent studies suggested the surface topography is also important for infrasound generation, and mountains or topographic peaks can act as effective secondary infrasound sources. In this study, we perform full 3-D seismoacoustic simulations to understand a coupled seismic and acoustic wave generation by an earthquake and energy partitioning across the solid Earthocean-atmosphere system. By including the effects of realistic topography, bathymetry, and earthquake focal mechanism, we provide quantitative information about seismoacoustic wavefields and secondary infrasound sources across the solid-water, solid-air, and water-air interfaces. Acoustic wave transmission across water-air interface is also investigated by using synthetic waveforms. Our study suggests that shallow seas near the coastline or over elevated seafloors can transmit significant amount of energy from acoustic waves in water and can be effective secondary infrasound sources.

58 GEOSCIENCES↗
DOE OSTI2021

Measurement of radiolytic hydrogen generation and impact of drying treatments on reactor exposed and surrogate aluminum materials

Technical challenges associated with dry storage of Aluminum-clad Spent Nuclear Fuel (ASNF) include a need to better understand the potential extent of gaseous molecular hydrogen (H 2 ) production through radiolytic degradation of the aluminum (oxy)hydroxide films present on ASNF. The characterization of radiolytic gas generation from ASNF (oxy)hydroxide layers has been identified as a key knowledge gap which poses a technical challenge to the long-term storage of ASNF. Task 2 of the action plan for the extended (>50 years) dry storage of ASNF addresses this gap. Previous radiolysis studies performed under Task 2 established baseline estimates of H 2 generation rates from the attendant hydrated oxides. The results confirmed that net radiolytic H 2 production has a dependency on absorbed gamma dose, as well as relative humidity and cover gas composition (air, nitrogen, and argon). Further experiments revealed that the physisorbed water on the samples may significantly impact the radiolytic H 2 yield. This phenomenon complicates the determination of H 2 generation rates for hydrated oxides, which may lead to inaccurate modeling predictions of the long-term H 2 yields in sealed storage systems containing ASNF, particularly when compared to spent fuel casks which have undergone some drying process intended to remove physically- and chemically-bound water. This report describes the testing methods utilized and the hydrogen generation results obtained in an investigation of the effects of gamma irradiation on aluminum materials for a variety of (oxy)hydroxide surface compositions and drying conditions. The testing methods included small-area aluminum material testing in ampules, and large-area aluminum material testing in steel vessels. Test material preparation, irradiation, and radiolytic H 2 measurement methods are summarized. The measured H 2 concentrations, which reflect the variable initial hydrated inventory and drying treatments, are compared to one another as well as to previously published data, to identify the primary factors affecting radiolytic H 2 generation rates and potential equilibrium H 2 concentrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cross Section Generation Capability in Griffin

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code reads multigroup cross sections in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. The implementation of the cross section generation capability in Griffin was initiated last year by plugging in the cross section application programming interface (CSAPI) and reviewing the methodologies for treating particulate fuels. The focus this year was on improving the CSAPI integration and implementing advanced self-shielding methods for applications to advanced reactor problems with TRISO fuels. First, the process for cross section library generation was updated to accurately and rigorously produce isotopic cross section data. Second, the equivalent Dancoff factor cell method performing slowing down calculations on the fly for the resonance treatment was implemented in CSAPI to improve the accuracy of effective multigroup cross sections in the resonance energy range. Third, the iterative local spatial self-shielding method was implemented under the calculation framework of the equivalent Dancoff factor cell method to accurately deal with the double heterogeneity effect of particulate fuel. The updated CSAPI with the advanced self-shielding methods, together with the cross section libraries generated based on the improved process, were tested for pin-cell, unit-cell, and fuel assembly problems with various resonance self-shielding conditions based on very high temperature reactor, high temperature test reactor, and Empire benchmark cores, indicating that the updated CSAPI in Griffin is able to produce multigroup cross sections accurately and efficiently. We also showed that the methodology worked well for pebble bed fuel from HTR-10, but the capability still needs to be fully integrated into CSAPI. In the future, further benchmark tests will be performed for various thermal reactor core problems, including particulate fuel-based pebble bed reactors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fourth Generation MPDV and MBR at DARHT

A new experimental setup for diagnosing hydrotest performance was designed, built, and implemented at DARHT. The setup consists of 128 points of fourth generation (Gen4) multiplexed photonic Doppler velocimetry (MPDV) and 128 separate points of modulation based ranging (MBR) to serve as a direct complement to the MPDV. The diagnostics were designed to overcome limitations in the third generation of the MPDV diagnostic that limited laser launch power to ~10 mW per point. As the main failure mechanism identified in the third generation MPDV system arose from stimulated Brillouin scattering, a nonlinear optical phenomenon inherent in standard single mode fiber, the fourth generation MPDV system reduced the number of time-multiplexed channels by a factor of two. This has allowed laser launch power to be increased to as much as 200 mW per channel which is a 20 times increase in the signal to noise ratio (SNR) as compared with the third generation system. This document will first discuss the previous limitations and describe the design of the system. This will be followed by the results of a simple flyer plate experiment used to demonstrate the improved SNR as well as calibrate the phase errors in the system that MBR is sensitive to.

42 ENGINEERING↗
DOE OSTI2022

INGRID: An interactive grid generator for 2D edge plasma modeling

A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces where the geometry is often complicated by the presence of one or more X-points; and modeling boundary plasmas usually relies on computational grids that account for the magnetic field geometry. The new grid generator INGRID (Interactive Grid Generator) presented here is a Python-based code for calculating grids for fusion boundary plasma modeling, for a variety of configurations with one or two X-points in the domain. INGRID first performs partitioning over the domain consisting of a small number of patches conforming to the magnetic field and wall geometry; then it generates a subgrid on each of the patches and joins them into a global grid. This domain partitioning strategy makes possible a uniform treatment of various configurations with one or two X-points in the domain. This includes single-null, double-null, and other configurations with two X-points in the domain. The INGRID design allows generating grids either interactively, via a parameterfile driven GUI, or using a non-interactive script-controlled workflow. Results of testing demonstrate that INGRID is a flexible, robust, and user-friendly grid-generation tool for fusion boundary-plasma modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗