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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 145 records · Page 8

Practical considerations for amino acid isotope analysis

Over the last few decades, isotopic analysis of amino acids at the compound- and position-specific levels has been rapidly advancing across diverse fields. As these techniques progress, evaluation of isotopic fractionation associated with sample workup is essential. This critical review of analytical methods through the lens of isotope geochemistry provides a benchmark for researchers across disciplines seeking to make compound- and position-specific amino acid isotope measurements. We focus on preparation, acid hydrolysis, clean-up, derivatization, separation, and C, H, N, and S isotope measurement. Despite substantial customizability across these steps, the following general recommendations should maximize recovery while minimizing isotopic fractionation. Samples should be freeze-dried and stored anoxically at ≤ –20 °C prior to conventional acid hydrolysis (6N HCl, 110 °C, 20–24 h, anoxic), which suffices for many residues. Both gas and liquid chromatographic (GC and LC, respectively) techniques are well-established and separate about 15 amino acids; LC bypasses the need for derivatization, while GC provides higher sensitivity. Furthermore, when derivatization is needed, n-acetyl and alkoxycarbonyl esters provide the most reproducible C isotope ratios. For compound-specific analyses, online GC–IRMS and LC–IRMS systems offer the easiest workflow, but EA–IRMS enables potential multi-element isotope analysis. Emerging techniques like high-resolution mass spectrometry are also promising for multi-element analysis and recover position-specific isotopic information. Looking forward to the next decade of innovation, isotope geochemists and ecologists can improve amino acid isotope analysis by focusing on streamlining multi-element analysis and standardizing calibration practices across laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

dCache - Keeping up With the Evolution of Science

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.

Mkrtchyan, Tigran↗

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver

Abstract Solving large-scale inverse problems using deep-learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem may require the utilization of high performance computing systems which poses a challenge on the algorithmic design of the inverse problem solver. Most deep learning algorithms require, due to their design, custom parallelization techniques in order to be resource efficient while showing a reasonable convergence. In this paper we introduce a S calable A synchronous G enerative I nverse P roblem S olver (SAGIPS) on high-performance computing systems. We present a workflow that utilizes an asynchronous ring-allreduce algorithm to transfer the gradients of the generator network across multiple GPUs. Experiments with a scientific proxy application demonstrate that SAGIPS shows near linear weak scaling, together with a convergence quality that is comparable to traditional methods. The approach presented here allows leveraging Generative Adverserial Network across multiple GPUs, promising advancements in solving complex inverse problems at scale.

97 MATHEMATICS AND COMPUTING↗

Energy Material Network Data Hubs

In early 2015 the United States Department of Energy conceived of a consortium of collaborative bodies based on shared expertise, data, and resources that could be targeted towards the more difficult problems in energy materials research. The concept of virtual laboratories had been envisioned and discussed earlier in the decade in response to the advent of the Materials Genome Initiative and similar scientific thrusts. To be effective, any virtual laboratory needed a robust method for data management, communication, security, data sharing, dissemination, and demonstration to work efficiently and effectively for groups of remote researchers. With the accessibility of new, easily deployed cloud technology and software frameworks, such individual elements could be integrated, and the required collaboration architecture is now possible. The developers have leveraged open-source software frameworks, customized them, and merged them into a platform to enable collaborative energy materials science, regardless of the geographic dispersal of the people and resources. After five years in operations, the systems are demonstratively an effective platform for enabling research within the Energy Material Networks (EMN). This paper will show the design and development of a secured scientific data sharing platform, the ability to customize the system to support diverse workflows, and examples of the enabled research and results connected with some of the Energy Material Networks.

97 MATHEMATICS AND COMPUTING↗

Adaptive workflow for simulation of RF heaters

Accurate RF (Radio Frequency) simulations of fusion systems like ITER require the definition of high-fidelity analysis geometries that include detailed antenna, reactor wall, and physics regions. Here, this paper will describe a workflow for the execution of adaptive high-performance simulations of RF fusion systems. In this workflow, the simulation input consists of a CAD model attributed with the needed analysis attributes. The analysis mesh is automatically generated and the analysis steps are executed using the time-harmonic Maxwell's equations solved using high-order Nédélec finite elements. A patch recovery-based error estimator is used to drive a conforming mesh adaptation procedure.

97 MATHEMATICS AND COMPUTING↗

Parallel Battery: The Framework and Process for an Intelligent and Ecological Battery System and Related Services

The concept, framework, process methodology and applications of parallel battery were proposed from both virtual and real aspects.The parallel battery was an application of ACP-based parallel intelligence in battery and related energy system areas.The real battery system was running with its equivalent, and the artificial battery system was in a virtual space, in a parallel and interactive manner.The artificial battery system contained the descriptive, predictive, and prescriptive functions on the real battery and related energy systems.There was a closed-loop workflow between the real battery system and the artificial battery system, which iteratively optimizes the battery and related energy systems, leading to a new paradigm of intelligent and ecological parallel battery system management.

ACP approach↗

Workflow for Process Automation of Soil Gas Results from an Automated Soil Gas-Sampling System for Application in Carbon Storage Projects

Conference presentation at Geoconvention, Calgary, Alberta, Canada, May 12–14, 2025. The Energy & Environmental Research Center (EERC) developed an automated workflow for processing soil gas measurements collected from the automated soil gas-sampling systems deployed across the project site. Raw soil gas measurements are collected from each station every 4 hours and automatically uploaded to a cloud database. The workflow begins by writing code to download the data to a workstation automatically, then the data are published to an online dashboard that visualizes the measurements in time-series plots and a process-based decision-making framework. This automated workflow accelerates the time from data acquisition to decision-making. It supports carbon storage project operators by preparing and delivering a live, standardized dataset for quick analysis and source attribution to provide assurance of containment and overall permit compliance.

02 PETROLEUM↗

Workflow for Process Automation of Soil Gas Results from an Automated Soil Gas-Sampling System for Application in Carbon Storage Projects

Extended abstract for Geoconvention, Calgary, Alberta, Canada, May 12–14, 2025. The Energy & Environmental Research Center (EERC) developed an automated workflow for processing soil gas measurements collected from the automated soil gas-sampling systems deployed across the project site. Raw soil gas measurements are collected from each station every 4 hours and automatically uploaded to a cloud database. The workflow begins by writing code to download the data to a workstation automatically, then the data are published to an online dashboard that visualizes the measurements in time-series plots and a process-based decision-making framework. This automated workflow accelerates the time from data acquisition to decision-making. It supports carbon storage project operators by preparing and delivering a live, standardized dataset for quick analysis and source attribution to provide assurance of containment and overall permit compliance.

02 PETROLEUM↗

A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

54 ENVIRONMENTAL SCIENCES↗

Elastic Workflows with PMIx [Slides]

Scientific workflows increasing in complexity. Elastic workflows promise improved application and system performance.

97 MATHEMATICS AND COMPUTING↗

Q-IRIS: The Evolution of the IRIS Task-Based Runtime to Enable Classical-Quantum Workflows

Extreme heterogeneity in emerging HPC systems are starting to include quantum accelerators, motivating runtimes that can coordinate between classical and quantum workloads. We present a proof-of-concept hybrid execution framework integrating the IRIS asynchronous task-based runtime with the XACC quantum programming framework via the Quantum Intermediate Representation Execution Engine (QIR-EE). IRIS orchestrates multiple programs written in the quantum intermediate representation (QIR) across heterogeneous backends (including multiple quantum simulators), enabling concurrent execution of classical and quantum tasks. Although not a performance study, we report measurable outcomes through the successful asynchronous scheduling and execution of multiple quantum workloads. To illustrate practical runtime implications, we decompose a four-qubit circuit into smaller subcircuits through a process known as quantum circuit cutting, reducing per-task quantum simulation load and demonstrating how task granularity can improve simulator throughput and reduce queueing behavior -- effects directly relevant to early quantum hardware environments. We conclude by outlining key challenges for scaling hybrid runtimes, including coordinated scheduling, classical-quantum interaction management, and support for diverse backend resources in heterogeneous systems.

Miniskar, Narasinga Rao [ORNL] (ORCID:000000018259↗

Apparatus and method for safety analysis evaluation with data-driven workflow

An apparatus and method for system safety analysis evaluation is provided, the apparatus including processing circuitry configured for generating a calculation matrix for a system, generating a plurality of models based on the calculation matrix, performing a benchmarking or convolution analysis of the plurality of models, identifying a design envelope based on the benchmarking or convolution analysis, deriving uncertainty models from the benchmarking or convolution analysis, deriving an assessment judgment based on the uncertainty models and acceptance criteria, defining one or more limiting scenarios based on the design envelope, and determining a safety margin in at least one figure-of-merit for the system based on the design envelope and the acceptance criteria.

Martin, Robert P.↗

Apparatus and method for safety analysis evaluation with data-driven workflow

An apparatus and method for system safety analysis evaluation is provided, the apparatus including processing circuitry configured for generating a calculation matrix for a system, generating a plurality of models based on the calculation matrix, performing a benchmarking or convolution analysis of the plurality of models, identifying a design envelope based on the benchmarking or convolution analysis, deriving uncertainty models from the benchmarking or convolution analysis, deriving an assessment judgment based on the uncertainty models and acceptance criteria, defining one or more limiting scenarios based on the design envelope, and determining a safety margin in at least one figure-of-merit for the system based on the design envelope and the acceptance criteria.

Martin, Robert P.↗

Sample IEEE123 Bus system for OEDI SI

Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.

123 bus↗

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows

This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.

97 MATHEMATICS AND COMPUTING↗

Optimizing Data Movement for GPU-Based In-Situ Workflow Using GPUDirect RDMA

The extreme-scale computing landscape is increasingly dominated by GPU-accelerated systems. At the same time, in-situ workflows that employ memory-to-memory inter-application data exchanges have emerged as an effective approach for leveraging these extreme-scale systems. In the case of GPUs, GPUDirect RDMA enables third-party devices, such as network interface cards, to access GPU memory directly and has been adopted for intra-application communications across GPUs. In this paper, we present an interoperable framework for GPU-based in-situ workflows that optimizes data movement using GPUDirect RDMA. Specifically, we analyze the characteristics of the possible data movement pathways between GPUs from an in-situ workflow perspective, and design a strategy that maximizes throughput. Furthermore, we implement this approach as an extension of the DataSpaces data staging service, and experimentally evaluate its performance and scalability on a current leadership GPU cluster. The performance results show that the proposed design reduces data-movement time by up to 53% and 40% for the sender and receiver, respectively, and maintains excellent scalability for up to 256 GPUs.

Zhang, Bo↗

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗