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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
DOE OSTI2023

Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing

We introduce a software suite developed for galaxy cluster cosmological analysis with the Dark Energy Survey Data. Cosmological analyses based on galaxy cluster number counts and weak-lensing measurements need efficient software infrastructure to explore an increasingly large parameter space, and account for various cosmological and astrophysical effects. Our software package is designed to model the cluster observables in a wide-field optical survey, including galaxy cluster counts, their averaged weak-lensing masses, or the cluster's averaged weak-lensing radial signals. To ensure maximum efficiency, this software package is developed in C++ in the CosmoSIS software framework, making use of the CUBA integration library. We also implement a testing and validation scheme to ensure the quality of the package. We demonstrate the effectiveness of this development by applying the software to the Dark Energy Survey Year 1 galaxy cluster cosmological data sets, and acquired cosmological constraints that are consistent with the fiducial Dark Energy Survey analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses

Background: In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 – 5720 nGy in total dose (71–208 nGy/hr), are compared to this “radiation-deprived” treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages. Results: In E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69–114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs “Minus” comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15–178 fold), but one (DNAstar’s DESeq2) resulted in more conservative fold-changes (1.5–3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar’s edgeR while DNAstar-D was more conservative. Conclusions: Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 – 178 fold), but one (DNAstar’s DESeq2) gave more realistic fold-changes (1.5–3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D’s expression results. When another model organism’s (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 (“DNAstar-D”) as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Challenges of and Opportunities for a Large Diverse Software Team

A large software team consisting of members with different expertise, skillsets, personalities, ethnicities, and involving collaboration on a large and complex software product presents many technical and cultural challenges, but also provides unique opportunities. In this article, we discuss the essential issues we faced when successfully transforming a collection of various independently developed software libraries into one large integrated product: the eXtreme-scale scientific Software Development Kit (xSDK). Furthermore, we argue it is just as important to pay attention to cultural challenges, such as establishment of reliable communication channels that considers, among others, differences in personalities and backgrounds as well as overcoming geographical separation and time-zone distribution when collaborating, as technical challenges. Finally, we discuss opportunities stemming from participating in a large diverse software team, such as increased internal expertise, variety of skillsets, broadened connections to external experts, and access to a larger pool of ideas or solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Cast of Thousands: How the IDEAS Productivity Project Has Advanced Software Productivity and Sustainability

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy’s Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities—building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery—mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards Software Bill of Materials in the Nuclear Industry

Large, modern industrial facilities often incorporate thousands of digital assets in their operational technology. Regulated facilities, such as nuclear power plants (NPPs), maintain robust cybersecurity and configuration management programs that often use bills of materials (BOMs) for these assets, including make, model, and version of hardware, firmware, and software. However, these BOMs typically capture only first- or second-tier information provided by the original equipment manufacturer (OEM). Unfortunately, as indicated by the increasing number and sophistication of software supply chain attacks, this level of detail is insufficient for identifying all the potential vulnerabilities and risks in software applications. Software BOMs (SBOMs) provide detailed enumeration of components and dependencies within the product or devices, including firmware. SBOMs can be combined with vulnerability data sources and vendor vulnerability attestations to improve vulnerability management and enable rapid identification of affected components when new software vulnerabilities are discovered. Ideally, SBOMs are created by the OEM prior to installation. However, since this practice is not yet commonplace and since NPPs are typically slow to adopt new technology, most NPPs do not incorporate SBOMs into their asset or configuration management programs. Fortunately, SBOMs can be generated by NPPs on existing digital assets to provide further insight into risk management decisions. This report provides an overview of the current SBOM ecosystem and recommends guidance on how to get started in a “crawl, walk, run” manner to develop and implement a sustainable SBOM program for digital assets in an NPP.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

An Application of a Modified Beta Factor Method for the Analysis of Software Common Cause Failures

This paper presents an approach for modeling software common cause failures (CCFs) within digital instrumentation and control (I&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. This work emphasizes the importance of identifying software-centric attributes related to the coupling mechanisms necessary for simultaneous failures of redundant software components. The groups of components that share coupling mechanisms are called common cause component groups (CCCGs). Most CCF models rely on operational data as the basis for establishing CCCG parameters and predicting CCFs. This work is motivated by two primary concerns: (1) a lack of operational and CCF data for estimating software CCF model parameters; and (2) the need to model single components as part of multiple CCCGs simultaneously. A hybrid approach was developed to account for these concerns by leveraging existing techniques: a modified beta factor model allows single components to be placed within multiple CCCGs, while a second technique provides software-specific model parameters for each CCCG. This hybrid approach provides a means to overcome the limitations of conventional methods while offering support for design decisions under the limited data scenario.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Analyzing Hardware and Software Common Cause Failures in Digital Instrumentation and Control Systems using Dual Error Propagation Method

This paper develops a methodology for quantifying software common cause failures (CCFs) in digital instrumentation and control (I&C) systems of nuclear power plants. To support the transition of analog I&C systems to digital in nuclear power plants, probabilistic risk assessment (PRA) techniques are used. The hardware components of the I&C systems have reliability databases that can be used in the PRA studies. However, the failure data for redundant software components of the systems is sparse. Failure of components constitutes a CCF, wherein two or more components or systems fail due to a single shared cause and coupling mechanism. This paper proposes a quantification approach that can simultaneously model hardware and software components, incorporate the CCFs of software systems in the models, and bridge the gap between the failure quantification of models and the development of CCF parametric databases. We demonstrate the dual error propagation method (DEPM) by developing I&C systems failure models for a representative digital reactor trip system. The DEPM models are built to simulate the control and data flows within the systems and can accommodate failure states. By expanding DEPM to software CCFs, we generated alpha factor parameter estimates for each of the modeled error propagation mechanisms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy–funded projects for advancing HPC Software via AI: Ellora and Durban.

Teranishi, Keita [ORNL] (ORCID:0000000166472690)↗
DOE OSTI2021

Analysis of ZPPR-15 experimental data for VTR software validation

As part of the Versatile Test Reactor (VTR) program, extensive validation work is being performed to demonstrate the fidelity of the Argonne Reactor Code (ARC) software package presently used for the core design studies. It is anticipated that this software will be also used for initial operations although additional software may be introduced at a later time to supplement or replace some components. The validation work was focused on accumulating reactor models that are consistent with the VTR and thus can be used to validate the accuracy of predictions made by the software used to design and operate the VTR. The U.S. does not have any active experimental facilities that can neutronically mockup the VTR core design. However, in the past, the U.S. had extensive experimental facilities for fast reactor neutronics. In that regard, the ZPPR-15 experiments carried out at ANL during the 1980s work best because the fuel form is closest to the VTR design, there is a considerable amount of processed data available for use, and existing staff are already familiar with the experimental machine and measurements. Because of previous collaborative work, many of the ZPPR-15 loadings of interest have already been processed into MCNP and preliminary ARC models, and a full uncertainty quantification has already been completed for many of the ZPPR-15 measurements. This manuscript covers the recent analysis work on the ZPPR-15 experiments and compares it with the experimental results. With respect to the ARC software components, this manuscript focuses on validation of the MC 2 , DIF3D and GAMSOR using the ZPPR-15 experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

The ATLAS experiment software on ARM

With an increased dataset obtained during the Run 3 of the LHC at CERN and the even larger expected increase of the dataset by more than one order of magnitude for the HL-LHC, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM architecture is becoming a competitive and energy efficient alternative. Some surveys indicate its increased presence in HPCs and commercial clouds, and some WLCG sites have expressed their interest. Chip makers are also developing their next generation solutions on ARM architectures, sometimes combining ARM and GPU processors in the same chip. Consequently it is important that the ATLAS software embraces the change and is able to successfully exploit this architecture. We report on the successful porting to ARM of the Athena software framework, which is used by ATLAS for both online and offline computing operations. Furthermore we report on the successful validation of simulation workflows running on ARM resources. For this we have set up an ATLAS Grid site using ARM compatible middleware and containers on Amazon Web Services (AWS) ARM resources. The ARM version of Athena is fully integrated in the regular software build system and distributed in the same way as other software releases. In addition, the workflows have been integrated into the HEPscore benchmark suite which is the planned WLCG wide replacement of the HepSpec06 benchmark used for Grid site pledges. In the overall porting process we have used resources on AWS, Google Cloud Platform (GCP) and CERN. A performance comparison of different architectures and resources will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

PluginPlay: Enabling exascale scientific software one module at a time

For many computational chemistry packages, being able to efficiently and effectively scale across an exascale cluster is a heroic feat. Collective experience from the Department of Energy’s Exascale Computing Project suggests that achieving exascale performance requires far more planning, design, and optimization than scaling to petascale. In many cases, entire rewrites of software are necessary to address fundamental algorithmic bottlenecks. This in turn requires a tremendous amount of resources and development time, resources that cannot reasonably be afforded by every computational science project. It thus becomes imperative that computational science transition to a more sustainable paradigm. Key to such a paradigm is modular software. While the importance of modular software is widely recognized, what is perhaps not so widely appreciated is the effort still required to leverage modular software in a sustainable manner. The present manuscript introduces PluginPlay, https://github.com/NWChemEx-Project/PluginPlay, an inversion-of-control framework designed to facilitate developing, maintaining, and sustaining modular scientific software packages. Here this manuscript focuses on the design aspects of PluginPlay and how they specifically influence the performance of the resulting package. Although, PluginPlay serves as the framework for the NWChemEx package, PluginPlay is not tied to NWChemEx or even computational chemistry. We thus anticipate PluginPlay to prove to be a generally useful tool for a number of computational science packages looking to transition to the exascale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Experimental Setup for Grid Control Device Software Updates in Supply Chain Cyber-Security

Supply chain cyberattacks that exploit insecure third-party software are a growing concern for the security of the electric power grid. These attacks seek to deploy malicious software in grid control devices during the fabrication, shipment, installation, and maintenance stages, or as part of routine software updates. Malicious software on grid control devices may inject bad data or execute bad commands, which can cause blackouts and damage power equipment. This paper describes an experimental setup to simulate the software update process of a commercial power relay as part of a hardware-in-the-loop simulation for grid supply chain cyber-security assessment. The laboratory setup was successfully utilized to study three supply chain cyber-security use cases.

Keller, Joseph↗
DOE OSTI2023

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the solution of Bayesian inverse problems governed by complex forward models described by partial differential equations (PDEs) remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We present hIPPYlib-MUQ, an extensible and scalable software framework that contains implementations of state-of-the art algorithms aimed to overcome the challenges of high-dimensional, PDE-constrained Bayesian inverse problems. These algorithms accelerate MCMC sampling by exploiting the geometry and intrinsic low-dimensionality of parameter space via derivative information and low rank approximation. The software integrates two complementary open-source software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse problems using automatically-generated adjoint-based derivatives, but it lacks full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients and Hessians to permit large-scale solution. By combining these two complementary libraries, we created a robust, scalable, and efficient software framework that realizes the benefits of each and allows us to tackle complex large-scale Bayesian inverse problems across a broad spectrum of scientific and engineering disciplines. To illustrate the capabilities of hIPPYlib-MUQ, we present a comparison of a number of MCMC methods available in the integrated software on several high-dimensional Bayesian inverse problems. These include problems characterized by both linear and nonlinear PDEs, various noise models, and different parameter dimensions. The results demonstrate that large (~ 50×) speedups over conventional black box and gradient-based MCMC algorithms can be obtained by exploiting Hessian information (from the log-posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.

97 MATHEMATICS AND COMPUTING↗