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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Insights from Optimizing HPL Performance on Exascale Systems: A Comparative Analysis of Panel Factorization

High performance LINPACK (HPL) remains the primary benchmark for evaluating supercomputing performance. It includes many parts with substantial internal complexity, and its performance is affected by a large number of parameters that interact in ways that are difficult to predict on large-scale heterogeneous supercomputer systems. We present a comprehensive performance analysis of HPL on Frontier, the world’s first exascale supercomputer, which achieved HPL performance of 1.35 exaflops. Through empirical parameter tuning, detailed modeling, and comparative evaluation, we uncover critical performance insights, share lessons learned, and outline best practices for effective parameter tuning on exascale systems. We introduce and evaluate two novel PDFACT strategies: a dedicated-thread (DT) variant and a GPU-based variant (GPUPDFACT) implementation using HIP cooperative groups, demonstrating that GPU-based factorization outperforms conventional CPU-based PDFACT on Frontier’s architecture. Our findings establish key performance factors for HPL on exascale systems and offer valuable guidance for future high-performance computing and benchmarking efforts.

Lu, Hao [ORNL] (ORCID:000000018941870X)↗
DOE OSTI2024

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources: A Case Study on Federated Fine-Tuning of LLaMA 2

Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. Here, in this article, we elaborate on the design of our Advanced Privacy-Preserving Federated Learning (APPFL) framework, which streamlines end-to-end secure and reliable federated learning experiments across cloud computing facilities and high-performance computing resources by leveraging Globus Compute, a distributed function as a service platform, and Amazon Web Services. We further demonstrate the use case of APPFL in fine-tuning an LLaMA 2 7B model using several cloud resources and supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enabling 6C Fast Charging of Li–Ion Batteries with Graphite/Hard Carbon Hybrid Anodes

Li-ion batteries that can simultaneously achieve high-energy density and fast charging are essential for electric vehicles. Graphite anodes enable a high-energy density, but suffer from an inhomogeneous reaction current and irreversible Li plating during fast charging. In contrast, hard carbon exhibits superior rate performance but lower energy density owing to its lower initial coulombic efficiency and higher average voltage. In this work, these tradeoffs are overcome by fabricating hybrid anodes with uniform mixtures of graphite and hard carbon, using industrially-relevant multi-layer pouch cells (>1 Ah) and electrode loadings (3 mAh cm –2 ). By controlling the graphite/hard carbon ratio, this study shows that battery performance can be systematically tuned to achieve both high-energy density and efficient fast charging. Pouch cells with optimized hybrid anodes retain 87% and 82% of their initial specific energy after 500 cycles of 4C and 6C fast-charge cycling, respectively. This is significantly higher than the 61% and 48% specific energy retention with graphite anodes under the same conditions. The enhanced performance is attributed to improved homogeneity of the reaction current throughout the hybrid anode, which is supported by continuum-scale modeling. Furthermore, this process is directly compatible with existing roll-to-roll battery manufacturing, representing a scalable pathway to fast charging.

25 ENERGY STORAGE↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2026

Water-stable direct air capture of CO 2 with microcapsules of task-specific ionic liquid and their electrothermal regeneration

Microcapsules of the task specific ionic liquid (TSIL) 1-ethyl-3-methylimidazolium 2-cyanopyrrolide [EMIM][2CNpyr] with composite polydimethylsiloxane (PDMS) shells were fabricated for use in CO 2 direct air capture (DAC) conditions. The TSIL was encapsulated using an oil-in-oil emulsion as a templating procedure through two different approaches. In the first approach, a PDMS-polyurea (PU) shell was constructed by interfacial polymerization, while in the second approach, a graphene oxide (GO)-PDMS shell was constructed by cross-linking GO sheets. The composition and structure of both capsule types were fully characterized, and their CO 2 DAC performance was evaluated by gravimetric and breakthrough analysis. Both capsules exhibited competitive capacities, with the PDMS-PU capsules and the GO-PDMS capsules reaching 0.75 mol kg −1 and 0.66 mol kg −1 , respectively. We further demonstrate that both capsule systems can be regenerated with complementary electrothermal methods. Microwave (MW) regeneration was used for the PDMS-PU capsules, effectively releasing absorbed CO 2 in less than an hour. Owing to the electrical conductivity of GO, GO-PDMS capsules were regenerated via radio frequency heating (RF). This work highlights the importance and opportunity of tuning solid–liquid composite performance for advanced applications, including direct air capture of carbon dioxide.

Al-Mahbobi, Luma [Texas A&M University, College St↗
DOE OSTI2024

Microwave-assisted synthesis of mixed-linker covalent organic frameworks enabling tunable and ultrahigh iodine capture

The use of covalent organic frameworks (COFs) for hazardous radioiodine capture has been highly sought after recently. However, the synthesis of high-performance COF adsorbents while circumventing the limitations of traditional solvothermal methods remains largely unexplored. Herein, we for the first time combine microwave-assisted synthesis and mixed-linker strategy to fabricate multivariate COF adsorbents (X% OMe-TFB-BD COFs, X% = 0, 33, 50, 67, and 100 mol%) with varying ratios of benzidine (BD) and 3,3'-dimethoxylbenzidine (BD-OMe) linkers in a rapid and facile manner. Adjusting the BD-OMe/BD mole ratios has led to distinct variations in density, crystallinity, porosity, morphology, and thermal/chemical stability of the resultant COFs, which empowered fine-tuning of the adsorption performance towards static iodine vapor. Remarkably, the 50% OMe-TFB-BD COF exhibited an ultrahigh iodine adsorption capability of 8.2 g g –1 , surpassing those of single-component COFs, mixed-linker COFs with other methoxy content, physically blended mixtures, and most existing COF adsorbents. Moreover, 50% OMe-TFB-BD COF was recyclable seven times without obvious loss in its adsorption capacity. Finally, this work underscores the substantial potential of microwave-assisted mixed-linker strategy as a viable approach for developing multivariate COFs with shortened reaction times, precisely tailored pore environment, and tunable sorption properties, which are of considerable promise for environmental remediation and other niche applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optimization and understanding of ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalyst for enhanced CO 2 -methanol conversion at low temperature and pressure

Cu-ZnO-Al 2 O 3 is the most widely applied catalyst for CO 2 hydrogenation to methanol. However, it is still a challenge to produce methanol using this catalyst under low-temperature (<250 °C) and low-pressure (<10 bar) conditions with desirable yield and selectivity. In this work, by tuning the experimental processing parameters such as solvent, loading amount, and annealing temperature, highly improved ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalysts have been successfully demonstrated. Here, by using organic solvent (N,N-dimethylformamide (DMF), acetone, or isopropanol) for dip-coating loading process instead of deionized (DI) water, Cu-ZnO-Al 2 O 3 nanocatalysts was comparatively better dispersed on the nanorod array support with populated and abundant active sites, thus enhancing the methanol yield. With the control of the loading amount and annealing temperature, finely distributed Cu nanoparticles were obtained on the ZnO nanorod surfaces to enhance the interactions between Cu and ZnO nanorod surfaces. Further improvement of the catalyst performance is demonstrated by tuning the reaction space velocity. At 200 °C and 10 bar conditions, the optimized catalyst achieved a methanol yield of 6.46 mol h –1 kg –1 with 100 % selectivity. The good stability after prolonged testing of the catalysts demonstrates the potential practical implementation. The in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements under the 1 bar reveal that the CO 2 hydrogenation to methanol on the ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalyst follows the CO reaction pathway, due to the surface oxygen vacancies on ZnO nanorods which facilitate CO 2 dissociation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

A multi-backend autotuning study of feature selection on GPUs

Abstract Feature selection is an important step in machine learning that can benefit from GPU acceleration. As the number of GPU vendors increases, it is imperative to adapt algorithms such as the minimum Redundancy Maximum Relevance (mRMR) feature selection method to different backends that support several GPU architectures. This work presents a multi-backend implementation of mRMR across CUDA, HIP, and SYCL, and studies its performance when combined with Bayesian optimization and transfer learning to automatically tune execution parameters for different platforms and datasets. Our experimental results show that when tuned, CUDA and HIP achieve comparable performance on NVIDIA architectures, while SYCL exhibits a moderate performance gap. Overall, this work highlights the impact of backend choice and autotuning on GPU-accelerated feature selection and provides insights into deploying mRMR across heterogeneous environments.

Beceiro, Bieito (ORCID:0000000333014890)↗
DOE OSTI2023

Optimization and Portability of a Fusion OpenACC-based FORTRAN HPC Code from NVIDIA to AMD GPUs

NVIDIA has been the main provider of GPU hardware in HPC systems for over a decade. Most applications that benefit from GPUs have thus been developed and optimized for the NVIDIA software stack. Recent exascale HPC systems are, however, introducing GPUs from other vendors, e.g. with the AMD GPU-based OLCF Frontier system just becoming available. AMD GPUs cannot be directly accessed using the NVIDIA software stack, and require a porting effort by the application developers. This paper provides an overview of our experience porting and optimizing the CGYRO code, a widely-used fusion simulation tool based on FORTRAN with OpenACC-based GPU acceleration. While the porting from the NVIDIA compilers was relatively straightforward using the CRAY compilers on the AMD systems, the performance optimization required more fine-tuning. In the optimization effort, we uncovered code sections that had performed well on NVIDIA GPUs, but were unexpectedly slow on AMD GPUs. After AMD-targeted code optimizations, performance on AMD GPUs has increased to meet our expectations. Modest speed improvements were also seen on NVIDIA GPUs, which was an unexpected benefit of this exercise.

Sfiligoi, Igor↗
DOE OSTI2026

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments

Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms.

FOS: Computer and information sciences↗
DOE OSTI2021

Cu-Local Structures and Their Relation with Nanoscale Electrical Performance in CdTe

For decades, copper has been introduced in CdTe devices to improve overall performance (open circuit voltage, fill factor, and series resistance). While multiple articles have reported on Cu-based defects, very little is known about how the local structure around the copper atom affects electrical performance. Using X-ray Absorption Near Edge Structure (XANES) coupled with X-ray microscopy we investigate good and poor performing region in Cu-doped CdTe devices. Our XANES coupled with theoretical standards by FEFF9 suggest that CU 2 Te phase and Cu Cd may be responsible for the high electrical performance of the regions under study. This correlation of structure-performance at the nanoscale offers a unique framework to understand and tune processes with deep implications to the overall electrical performance of the solar cell.

CdTe↗
DOE OSTI2024

Anderson acceleration with approximate calculations: Applications to scientific computing

Here we provide rigorous theoretical bounds for Anderson acceleration (AA) that allow for approximate calculations when applied to solve linear problems. We show that, when the approximate calculations satisfy the provided error bounds, the convergence of AA is maintained while the computational time could be reduced. We also provide computable heuristic quantities, guided by the theoretical error bounds, which can be used to automate the tuning of accuracy while performing approximate calculations. For linear problems, the use of heuristics to monitor the error introduced by approximate calculations, combined with the check on monotonicity of the residual, ensures the convergence of the numerical scheme within a prescribed residual tolerance. Motivated by the theoretical studies, we propose a reduced variant of AA, which consists in projecting the least-squares used to compute the Anderson mixing onto a subspace of reduced dimension. The dimensionality of this subspace adapts dynamically at each iteration as prescribed by the computable heuristic quantities. We numerically show and assess the performance of AA with approximate calculations on: (i) linear deterministic fixed-point iterations arising from the Richardson's scheme to solve linear systems with open-source benchmark matrices with various preconditioners and (ii) non-linear deterministic fixed-point iterations arising from non-linear time-dependent Boltzmann equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Methods for a blind analysis of isobar data collected by the STAR collaboration

In 2018, the STAR collaboration collected data from $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ at $\sqrt{s_\text {NN}}=200$ GeV to search for the presence of the chiral magnetic effect in collisions of nuclei. The isobar collision species alternated frequently between $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ . In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data, STAR developed a three-step blind analysis procedure. Analysts are initially provided a “reference sample” of data, comprised of a mix of events from the two species, the order of which respects time-dependent changes in run conditions. After tuning analysis codes and performing time-dependent quality assurance on the reference sample, analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual $\approx 30$-min data-taking runs. For this sample, species-specific information is disguised, but individual output files contain data from a single isobar species. Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage. Following these modifications, the “frozen” code is passed over the fully un-blind data, completing the blind analysis. As a check of the feasibility of the blind analysis procedure, analysts completed a “mock data challenge,” analyzing data from Au + Au collisions at $\sqrt{s_\text {NN}}=27$ GeV, collected in 2018. The Au + Au data were prepared in the same manner intended for the isobar blind data. Finally, the details of the blind analysis procedure and results from the mock data challenge are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗