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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 OSTI2018

Verification of a rapid mooring and foundation design tool

Marine renewable energy devices require mooring and foundation systems that suitable in terms of device operation and are also robust and cost effective. In the initial stages of mooring and foundation development a large number of possible configuration permutations exist. Filtering of unsuitable designs is possible using information specific to the deployment site (i.e. bathymetry, environmental conditions) and device (i.e. mooring and/or foundation system role and cable connection requirements). The identification of a final solution requires detailed analysis, which includes load cases based on extreme environmental statistics following certification guidance processes. Static and/or quasi-static modelling of the mooring and/or foundation system serves as an intermediate design filtering stage enabling dynamic time-domain analysis to be focused on a small number of potential configurations. Mooring and foundation design is therefore reliant on logical decision making throughout this stage-gate process. The open-source DTOcean (Optimal Design Tools for Ocean Energy Arrays) Tool includes a mooring and foundation module, which automates the configuration selection process for fixed and floating wave and tidal energy devices. As far as the authors are aware, this is one of the first tools to be developed for the purpose of identifying potential solutions during the initial stages of marine renewable energy design. While the mooring and foundation module does not replace a full design assessment, it provides in addition to suitable configuration solutions, assessments in terms of reliability, economics and environmental impact. This article provides insight into the solution identification approach used by the module and features the verification of both the mooring system calculations and the foundation design using commercial software. Several case studies are investigated: a floating wave energy converter and several anchoring systems. It is demonstrated that the mooring and foundation module is able to provide device and/or site developers with rapid mooring and foundation design solutions to appropriate design criteria.

42 ENGINEERING↗
NASA NTRS2018

Sensitivity Analysis for Multidisciplinary Systems (SAMS)

This report describes the research conducted under an interagency collaboration agreement between the Aerospace Systems Directorate of the Air Force Research Laboratory (AFRL/RQ) and the Computational AeroSciences Branch of NASA Langley (NASA LaRC). Both organizations have a long-term goal of developing a modular computational system for coupling fluids and structures to enable both analysis and optimization of aerospace vehicles. Ultimately, the system should support multiple solvers within the fluid and structure domains to allow the best combination for the task at hand, as well as to allow for institutional preferences of specific software components. Towards this goal, the current research was focused on enhancing the existing modal aeroelastic analysis in the NASA FUN3D (Fully-UNstructured three-dimensional CFD (Computational Fluid Dynamics) code) software (Biedron et al. 2018), as well as developing new aeroelastic analysis and optimization capabilities based on a non-linear finite-element method. The methods and enhancements described in this document pertain to FUN3D Version 13.4.

Biedron, Robert T.↗
DOE OSTI2025

ElectricityLCI

The ElectricityLCI is a Python package for creating regionalized life cycle inventory models of U.S. electricity generation, consumption, and distribution using standardized facility and generation data for use with open-source LCA software.

Electricity; LCA; LCI; Python; life cycle analysis↗
DOE OSTI2018

Towards a systematic framework for the synthesis of operable process intensification systems

In this work we present an integrated framework to derive intensified designs with guaranteed safety and operability performance. A step-by-step procedure is described featuring: (i) superstructure-based process synthesis using Generalized Modular Representation Framework, (ii) development of advanced multi-parametric control strategies following the PAROC framework and software platform, and (iii) inherent safety analysis at early design stage utilizing quantitative risk assessment. Here, two case studies with increasing intensification complexity, namely heat exchanger network synthesis and reactive distillation column design, are investigated to demonstrate the capabilities of the proposed framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Spatial Environmental Assessment Tool (SEAT)

SAND2024-08551O The Spatial Environmental Assessment Tool (SEAT) evaluates potential environmental risk associated with marine energy device deployment by coupling modeled impacts with risk severity maps. As a graphical user interface-based software, it interacts with marine energy stressor and receptor data to evaluate the risk of changes to marine environment. SEAT, which relies on spatial and temporal data, is a plug-in for geospatial software QGIS. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Chartrand, Chris↗
DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Mass storage system reference model, Version 4

The high-level abstractions that underlie modern storage systems are identified. The information to generate the model was collected from major practitioners who have built and operated large storage facilities, and represents a distillation of the wisdom they have acquired over the years. The model provides a common terminology and set of concepts to allow existing systems to be examined and new systems to be discussed and built. It is intended that the model and the interfaces identified from it will allow and encourage vendors to develop mutually-compatible storage components that can be combined to form integrated storage systems and services. The reference model presents an abstract view of the concepts and organization of storage systems. From this abstraction will come the identification of the interfaces and modules that will be used in IEEE storage system standards. The model is not yet suitable as a standard; it does not contain implementation decisions, such as how abstract objects should be broken up into software modules or how software modules should be mapped to hosts; it does not give policy specifications, such as when files should be migrated; does not describe how the abstract objects should be used or connected; and does not refer to specific hardware components. In particular, it does not fully specify the interfaces.

Coleman, Sam↗
NASA NTRS2009

Space Communications and Navigation (SCaN) Network Simulation Tool Development and Its Use Cases

In this work, we focus on the development of a simulation tool to assist in analysis of current and future (proposed) network architectures for NASA. Specifically, the Space Communications and Navigation (SCaN) Network is being architected as an integrated set of new assets and a federation of upgraded legacy systems. The SCaN architecture for the initial missions for returning humans to the moon and beyond will include the Space Network (SN) and the Near-Earth Network (NEN). In addition to SCaN, the initial mission scenario involves a Crew Exploration Vehicle (CEV), the International Space Station (ISS) and NASA Integrated Services Network (NISN). We call the tool being developed the SCaN Network Integration and Engineering (SCaN NI&E) Simulator. The intended uses of such a simulator are: (1) to characterize performance of particular protocols and configurations in mission planning phases; (2) to optimize system configurations by testing a larger parameter space than may be feasible in either production networks or an emulated environment; (3) to test solutions in order to find issues/risks before committing more significant resources needed to produce real hardware or flight software systems. We describe two use cases of the tool: (1) standalone simulation of CEV to ISS baseline scenario to determine network performance, (2) participation in Distributed Simulation Integration Laboratory (DSIL) tests to perform function testing and verify interface and interoperability of geographically dispersed simulations/emulations.

Jennings, Esther↗
NASA NTRS2022

Enabling Innovative Research on the International Space Station to Solve the Challenges of A Human Mission to Mars: Results of the ISS4Mars International Workshops 2020–2021

During the ISS4Mars workshops in 2020–2021, personnel from the International Space Station (ISS) partner agencies convened to reflect on scenarios for how the ISS could be used and its operations possibly modified to simulate aspects of a human mission to Mars. Scientific leaders, operations experts, crewmembers, managers, and flight surgeons discussed the five hazards of human spaceflight—gravity transitions, radiation, isolation and confinement, distance from Earth, and hostile closed environments—and considered how an ISS-based analog of Mars transit could benefit assessments and mitigations of these hazards. A focused writing team then discussed the advantages and disadvantages of each approach identified by the workshop participants before developing a set of eight use cases to consider the feasibility of implementing on the ISS. The writing team also identified the prerequisites needed, including ground analog studies simulating a mission to Mars required to verify measurements and procedures, before testing could begin on the ISS. Five of the use cases were considered feasible to assess in simulations using an ISS-based analog of Mars transit if some ground rules and assumptions were met. These five use cases were Earth-independent medical operations, Earth-independent integrated operations, life support and food for a one year duration, lower-body negative pressure as a countermeasure against the effects of exposure to microgravity, and fitness levels after landing. In addition, three more extensive interventions—extended Mars surface operations, a small-volume transit analog, and artificial gravity—were deemed unfeasible for testing on the ISS. Experience gained from the five use cases executed on the ISS may help answer some of the questions in the deferred scenarios, or it may be possible to complete them on another platform (e.g. commercial space station, lunar habitat). Simulating conditions during a Mars mission on the ISS will afford higher fidelity for assessing multiple integrated hazards of human spaceflight, however, ground analogs of Mars missions can be used to ensure effective measures and experimental design before testing begins on the ISS. The strategic concepts refined as part of these workshops were brought to a multilateral forum, Mulitlateral Human Research Planel for Exploration (MHRPE), where ISS partner agencies are now discussing implementation plans to provide new opportunities to use the ISS to prepare for deep space exploration over the coming decade. In this publication we present a summary of the international strategic plans for future research that will enable operations, software, and countermeasures to be developed that will reduce the risk to humans during future crewed missions to Mars.

Human space exploration↗
DOE OSTI2019

Production Operations Management System

The Production Operations Management System (POMS) is a set of software tools which allows production teams and analysis groups across multiple Fermilab experiments to launch, modify and monitor large scale campaigns of related Monte Carlo or data processing jobs. POMS provides a web service interface that enables automated jobs submission on distributed resources according to customers’ requests and subsequent monitoring and recovery of failed submissions, debugging and record keeping. POMS interfaces with existing HEP data access, processing, movement and monitoring tools at Fermilab, including Jobsub, dCache, SAM, and FIFEmon, and in combination with them handles the creation and tracking of unique filenames for multistage campaigns. A flexible, interactive GUI interface exists as part of the system to visualize campaign stages, parallelization tracks and data flow. An important feature of POMS is a one-to-one connection between the GUI campaign visualizer and a text based representation of the complete campaign configuration. An extensible library of template campaign configurations is available. The templates are user modifiable and map cleanly to and from the GUI description of the campaign. Future releases of POMS will interface with Rucio for file movement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

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. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗
DOE OSTI2024

General Applications for Hamilton Vantage (GenApps for Vantage) v0.6.1

General applications for Hamilton Vantage is a flexible liquid handling method used to automate the most widely applicable types of automated liquid transfers. General applications can also be used as a tool to onboard new fully-automated workflows by breaking them down step by step into single transfers. The goal of General Applications is to make using the Hamilton Vantage liquid handler as easy & practical as using a handheld pipette for the end user. The software supports plate-to-plate transfers for a variety of method types including: Stamp: One-to-One, Stamp: One-To-Many, Split, Combine, Hitpick, and Qtray plating. General Applications eliminates the need for automation engineers to customize individual methods for each new workflow that gets onboarded. Steps can be customized within GenApps according to the needs of the researcher. The software utilizes a GUI to prompt the users to input variables – Allowing for flexible control over plate types, transfer volumes, number of replicates, tip types, liquid classes, mixing steps, aspiration/dispense heights and more. General Applications also generates a deck image and setup instructions to guide the researcher on how to load the deck and start the instrument.

Yoder, Sam↗
NASA NTRS2012

Development of 3-D Ice Accretion Measurement Method

A research plan is currently being implemented by NASA to develop and validate the use of a commercial laser scanner to record and archive fully three-dimensional (3-D) ice shapes from an icing wind tunnel. The plan focused specifically upon measuring ice accreted in the NASA Icing Research Tunnel (IRT). The plan was divided into two phases. The first phase was the identification and selection of the laser scanning system and the post-processing software to purchase and develop further. The second phase was the implementation and validation of the selected system through a series of icing and aerodynamic tests. Phase I of the research plan has been completed. It consisted of evaluating several scanning hardware and software systems against an established selection criteria through demonstrations in the IRT. The results of Phase I showed that all of the scanning systems that were evaluated were equally capable of scanning ice shapes. The factors that differentiated the scanners were ease of use and the ability to operate in a wide range of IRT environmental conditions.

Lee, Sam↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
DOE OSTI2025

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator↗
NASA NTRS2009

Modern Material Analysis Instruments Add a New Dimension to Materials Characterization and Failure Analysis

Modern analytical tools can yield invaluable results during materials characterization and failure analysis. Scanning electron microscopes (SEMs) provide significant analytical capabilities, including angstrom-level resolution. These systems can be equipped with a silicon drift detector (SDD) for very fast yet precise analytical mapping of phases, as well as electron back-scattered diffraction (EBSD) units to map grain orientations, chambers that admit large samples, variable pressure for wet samples, and quantitative analysis software to examine phases. Advanced solid-state electronics have also improved surface and bulk analysis instruments: Secondary ion mass spectroscopy (SIMS) can quantitatively determine and map light elements such as hydrogen, lithium, and boron - with their isotopes. Its high sensitivity detects impurities at parts per billion (ppb) levels. X-ray photo-electron spectroscopy (XPS) can determine oxidation states of elements, as well as identifying polymers and measuring film thicknesses on coated composites. This technique is also known as electron spectroscopy for chemical analysis (ESCA). Scanning Auger electron spectroscopy (SAM) combines surface sensitivity, spatial lateral resolution (10 nm), and depth profiling capabilities to describe elemental compositions of near and below surface regions down to the chemical state of an atom.

Panda, Binayak↗
DOE OSTI2020

Production Operations Management System (POMS) for Fermilab Experiments

The Production Operations Management System (POMS) software allows production teams and analysis groups across multiple Fermilab experiments to launch, modify and monitor large scale campaigns of related Monte Carlo or data processing jobs, and currently manages the majority of production computing of experiments at Fermilab.POMS provides a web service interface that enables automated job submission on distributed resources according to customers’ requests, as well as subsequent monitoring of those submissions as well as recovery of failed submissions, debugging and record keeping.POMS interfaces with existing HEP data access, processing, movement and monitoring tools at Fermilab, including Jobsub (for job submission), SAM (for data management), and Landscape (for monitoring), and in combination with them handles the creation of intermediate datasets of files for multistage campaigns.POMS visualizes these campaigns with an interactive GUI interface. An important feature of POMS is a one-to-one connection between the GUI campaign visualizer and a text based representation of the complete campaign configuration. An extensible library of template campaign configurations is available. The templates are user modifiable and map cleanly to and from the GUI description of the campaign.This paper will discuss the current usage, history, and some technical details of POMS, as well as planned future extensions, particularly interfacing with Rucio as well as SAM for file movement.

Mengel, Marc↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗