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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2019

Towards Resolving the Spatial Metabolome with Unambiguous Molecular Annotations in Complex Biological Systems by Coupling Mass Spectrometry Imaging with Structures for Lossless Ion Manipulations

We demonstrate the coupling of liquid extraction surface analysis (LESA) to structures for lossless ion manipulations in conjunction with serpentine ultralong path with extending routing (SLIM SUPER) ion mobility-mass spectrometry (IM-MS) for the unambiguous annotation of important isomeric glycoforms in carbon-fixing communities. A major bottleneck of current mass spectrometry imaging (MSI) methods is their inability to accurately and confidently delineate amongst isomers within a single probing area. Our methodology provides gains in both sensitivity and IM resolution, while simultaneously gaining insight on the spatial distribution of these biological isomers in a mixed organism composed of moss, cyanobacteria and fungus. We envision this methodology will be broadly applicable to other mass spectrometry imaging modalities and invaluable in multiplexed, untargeted, analyses of a single probing location.

Nagy, Gabe↗
DOE OSTI2016

Engineering Bacteria to Catabolize the Carbonaceous Component of Sarin: Teaching E. coli to Eat Isopropanol

In this paper, we report an engineered strain of Escherichia coli that catabolizes the carbonaceous component of the extremely toxic chemical warfare agent sarin. Enzymatic decomposition of sarin generates isopropanol waste that, with this engineered strain, is then transformed into acetyl-CoA by enzymatic conversion with a key reaction performed by the acetone carboxylase complex (ACX). We engineered the heterologous expression of the ACX complex from Xanthobacter autotrophicus PY2 to match the naturally occurring subunit stoichiometry and purified the recombinant complex from E. coli for biochemical analysis. Incorporating this ACX complex and enzymes from diverse organisms, we introduced an isopropanol degradation pathway in E. coli, optimized induction conditions, and decoupled enzyme expression to probe pathway bottlenecks. Our engineered E. coli consumed 65% of isopropanol compared to no-cell controls and was able to grow on isopropanol as a sole carbon source. Finally, in the process, reconstitution of this large ACX complex (370 kDa) in a system naïve to its structural and mechanistic requirements allowed us to study this otherwise cryptic enzyme in more detail than would have been possible in the less genetically tractable native Xanthobacter system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
NASA NTRS1981

Operations analysis of gravity assisted rapid transit

Gravity assisted rapid transit (GART) with 6 percent grades before and after each station are compared with conventional systems in terms of energy consumption, run time, line capacity and schedule stability under abnormal circumstances. Parametric analyses of run times and energy consumption include the impact of alternate accelerating and braking levels. The capcity analysis uses a network simulation program to determine the location and severity of all signal delays. Based on results of initial simulations, the block design was revised to eliminate bottlenecks in normal operations. The systems are then compared at headways of 80 to 180 seconds. One month of incidence reports of a modern operating transit system are reviewed to determine the failures to be simulated. The impact of failures resulting in station delays (30 to 360 seconds), speed limit reduction (20 mph and 30 mph to one or more trains), vehicle performance (75 percent acceleration) are compared at scheduled headway of 90 to 180 seconds.

Source record↗
DOE OSTI2024

Darshan for HEP applications

Modern HEP workflows must manage increasingly large and complex data collections. HPC facilities may be employed to help meet these workflows’ growing data processing needs. However, a better understanding of the I/O patterns and underlying bottlenecks of these workflows is necessary to meet the performance expectations of HPC systems.Darshan is a lightweight I/O characterization tool that captures concise views of HPC application I/O behavior. It intercepts application I/O calls at runtime, records file access statistics for each process, and generates log files detailing application I/O access patterns.Typical HEP workflows include event generation, detector simulation, event reconstruction, and subsequent analysis stages. A study of the I/O behavior of the ATLAS simulation and filtering stage, and the CMS simulation workflow using Darshan is presented, including insights into the I/O operations and data access size.

Wang, Rui↗
DOE OSTI2023

maranasgroup/scRBA

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (kapp) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

Dinh, Hoang↗
DOE OSTI2016

Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling

The increasing complexity of both scientific simulations and high-performance computing system architectures are driving the need for adaptive workflows, in which the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state of the simulation itself. Consider, for example, the frequency of data storage. Critical phases of the simulation should be captured with high frequency and with high fidelity for postanalysis; however, we cannot afford to retain the same frequency for the full simulation due to the high cost of data movement. We can instead look for triggers, indicators that the simulation will be entering a critical phase, and adapt the workflow accordingly. In this paper, we present a methodology for detecting triggers and demonstrate its use in the context of direct numerical simulations of turbulent combustion using S3D. We show that chemical explosive mode analysis (CEMA) can be used to devise a noise-tolerant indicator for rapid increase in heat release. However, exhaustive computation of CEMA values dominates the total simulation, and thus is prohibitively expensive. To overcome this computational bottleneck, we propose a quantile sampling approach. Our sampling-based algorithm comes with provable error/confidence bounds, as a function of the number of samples. Most importantly, the number of samples is independent of the problem size, and thus our proposed sampling algorithm offers perfect scalability. Our experiments on homogeneous charge compression ignition and reactivity controlled compression ignition simulations show that the proposed method can detect rapid increases in heat release, and its computational overhead is negligible. Our results will be used to make dynamic workflow decisions regarding data storage and mesh resolution in future combustion simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Network performance analysis for HPC datacenters (net_perf) v1.0

The software has two main features: (1) identify data movement trends in HPC data centers that use network flow monitoring (2) analyze the performance of individual data flows under the existing data movement management strategy and identify performance bottlenecks that impede timely data availability for science workflows. Its main advantage is that it is tailored for HPC network traffic by considering HPC data movement management intricacies.

Giannakou, Anna↗
DOE OSTI2019

Future Aviation Biofuel Analysis Using the Biomass Scenario Model

This presentation summarizes the findings from two articles that use the Biomass Scenario Model (BSM) to explore potential future aviation biofuels scenarios. Newes et al. (2017) concluded that construction/build out capabilities and development of the feedstock market are key bottlenecks in the initial years. Displacement of jet fuel by 30% with biofuels by 2030 is possible, but several factors related to policy design-in the absence of high oil prices or policy uncertainty-contribute to the timing and magnitude of aviation biofuels production. First, incentives targeted toward jet fuel production, such as financial incentives (e.g., a producer tax credit or a CO2 tax) could be sufficient to reach six billion gallons. Second, R&D investment in pre-commercial technologies is needed to reduce the cost of production through learning-by-doing. Third, reduction of investment risk through loan guarantees and offtake agreements may allow production to ramp up more quickly through accelerating industry learning. Lewis et al. (2018) showed that both BSM and the Freight and Fuel Transportation Optimization Tool suggest that 200 million to 1 billion gallons per year of alternative jet fuel production are possible by 2030, given multiple incentives and a favorable investment climate. However, different capital costs and technology maturation rates in the two models will affect deployment of different fuel production technologies and therefore the feedstocks needed. Further collaboration on these modeling approaches would reduce methodological blind spots while providing insights into future industry trajectories.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Neural network acceleration of large-scale structure theory calculations

Here, we make use of neural networks to accelerate the calculation of power spectra required for the analysis of galaxy clustering and weak gravitational lensing data. For modern perturbation theory codes, evaluation time for a single cosmology and redshift can take on the order of two seconds. In combination with the comparable time required to compute linear predictions using a Boltzmann solver, these calculations are the bottleneck for many contemporary large-scale structure analyses. Here, we construct neural network-based surrogate models for Lagrangian perturbation theory (LPT) predictions of matter power spectra, real and redshift space galaxy power spectra, and galaxy-matter cross power spectra that attain ~ 0.1% (at one sigma) accuracy over a broad range of scales in a ωCDM parameter space. The neural network surrogates can be evaluated in approximately one millisecond, a factor of 1000 times faster than the full Boltzmann code and LPT computations. In a simulated full-shape redshift space galaxy power spectrum analysis, we demonstrate that the posteriors obtained using our surrogates are accurate compared to those obtained using the full LPT model. We make our surrogate models public at https://github.com/sfschen/EmulateLSS, so that others may take advantage of the speed gains they provide to enable rapid iteration on analysis settings, something that is essential in complex contemporary large-scale structure analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Discovery of FoTO1 and Taxol genes enables biosynthesis of baccatin III

Abstract Plants make complex and potent therapeutic molecules 1,2 , but sourcing these molecules from natural producers or through chemical synthesis is difficult, which limits their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (sold under the brand name Taxol), which is derived from yew trees (Taxusspecies) 3 . Identifying the full paclitaxel biosynthetic pathway would enable heterologous production of the drug, but this has yet to be achieved despite half a century of research 4 . WithinTaxus’ large, enzyme-rich genome 5 , we suspected that the paclitaxel pathway would be difficult to resolve using conventional RNA-sequencing and co-expression analyses. Here, to improve the resolution of transcriptional analysis for pathway identification, we developed a strategy we term multiplexed perturbation × single nuclei (mpXsn) to transcriptionally profile cell states spanning tissues, cell types, developmental stages and elicitation conditions. Our data show that paclitaxel biosynthetic genes segregate into distinct expression modules that suggest consecutive subpathways. These modules resolved seven new genes, allowing a de novo 17-gene biosynthesis and isolation of baccatin III, the industrial precursor to Taxol, inNicotiana benthamianaleaves, at levels comparable with the natural abundance inTaxusneedles. Notably, we found that a nuclear transport factor 2 (NTF2)-like protein, FoTO1, is crucial for promoting the formation of the desired product during the first oxidation, resolving a long-standing bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA ligase, the eight genes discovered here enable the de novo biosynthesis of 3’-N-debenzoyl-2’-deoxypaclitaxel. More broadly, we establish a generalizable approach to efficiently scale the power of co-expression analysis to match the complexity of large, uncharacterized genomes, facilitating the discovery of high-value gene sets.

Science & Technology - Other Topics↗
DOE OSTI2018

In‐memory integration of existing software components for parallel adaptive unstructured mesh workflows

Summary Reliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

Smith, Cameron W.↗
DOE OSTI2026

Identification of pressure points in modern power systems using transfer entropy

Power shortages disrupt daily life, economic activity, and essential services. In modern power systems, weather is an increasingly important driver of reliability: high temperatures raise demand and limit transmission capacity, and calm or cloudy periods reduce wind and solar supply. Using a data-driven analysis, this study identifies grid infrastructure whose operating patterns help predict power shortages. The results show that reliability risks often emerge from interacting stresses across generation, transmission, and demand, rather than from single bottlenecks. By clarifying how system stress propagates through the grid, this diagnostic perspective helps explain why shortages occur under specific conditions and can complement traditional planning and operational tools to support adaptive reliability strategies, targeted monitoring, and coordinated infrastructure investments.

power systems↗
DOE OSTI2019

Exploring GlideinWMS and HTCondor scalability frontiers for an expanding CMS Global Pool

The CMS Submission Infrastructure Global Pool, built on Glidein-WMS andHTCondor, is a worldwide distributed dynamic pool responsible for the allocation of resources for all CMS computing workloads. Matching the continuously increasing demand for computing resources by CMS requires the anticipated assessment of its scalability limitations. In addition, the Global Plmust be able to expand in a more heterogeneous environment, in terms of resource provisioning (combining Grid, HPC and Cloud) and workload submissi.A dedicated testbed has been set up to simulate such conditions with the purpose of finding potential bottlenecks in the software or its configuration. This report provides a thorough description of the various scalabilitydimensions in size and complexity that are being explored for the future Global Pool, along with the analysis and solutions to the limitations proposed with the support of the GlideinWMS and HTCondor developer teams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

NEthing v0.1

The program extends the analysis capabilities of existing profilers through additional analysis to provide insights about communication computation overlap, communication modeling, and broad performance summaries of CPU, GPU and memory. This allows users to understand how their applications are spending time and where performance bottlenecks are.

Groves, Taylor↗
DOE OSTI2019

Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa Workshop Report

In the last few decades, high-throughput technologies using various “omics” have enabled unprecedented views of biological systems at the molecular level. In parallel, the integration of omic datasets using computational modeling has provided new understanding of biological processes in organisms relevant to the U.S. Department of Energy’s (DOE) missions in energy and the environment. Collectively, these developments have spearheaded the advancement of systems biology, which can be viewed as a holistic approach for deciphering the complexity of biological systems. However, as high-throughput omic technologies and integrative systems biology efforts have improved our understanding of some biological systems, analyzing and finding meaningful answers within these massive datasets remain extremely challenging, in large part due to the lack of fundamental knowledge of gene function. Indeed, all sequenced genomes, both microbes and plants, contain large numbers of genes of “unknown function” that significantly limit scientists’ ability to model, predict, and engineer organisms with enhanced functions relevant to DOE. Current methodologies can be employed to decipher gene function, but they are typically slow, laborious, inefficient, and not scalable. This “bottleneck” in genome understanding could be broken with new, innovative, and transformative experimental tools, datasets, and computation that can define gene function on a massive and high-throughput scale compatible with the pace of DNA sequencing. In light of this grand challenge, DOE’s Office of Biological and Environmental Research (BER) convened the Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa workshop on November 1–2, 2018. This workshop brought together leaders in microbiology, plant sciences, technology, and computation, who collectively identified the experimental and data analysis gaps preventing large-scale gene function determination as well as opportunities for overcoming these gaps.

59 BASIC BIOLOGICAL SCIENCES↗