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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Status of the Advanced Oxygen Generation Assembly Design

Future Exploration missions will require an Oxygen Generation Assembly (OGA) to electrolyze water to supply oxygen for crew metabolic consumption. The system design will be based on the International Space Station (ISS) OGA but with added improvements based on lessons learned during ISS operations and technological advances since the original OGA was designed and built. The goal of these improvements will be to reduce system weight, crew maintenance time and spares mass while increasing reliability. Over the past year, the team has performed additional design reviews, testing and analysis in an effort to optimize upgrade efforts and achieve the best value that meets Exploration mission requirements. Upgrades that will be incorporated include: redesign of the electrolysis cell stack, redesign of the hydrogen dome, replacement of the hydrogen sensors, redesign of the recirculation loop deionizing bed, and incorporation of recirculation loop nitrogen purging and water flushing. The ISS OGA will be upgraded to an Advanced OGA (AOGA) configuration and its operation demonstrated in a relevant flight environment.

Oxygen Generation Assembly↗
NASA NTRS2009

Offshore Hydrokinetic Energy Conversion for Onshore Power Generation

Design comparisons have been performed for a number of different tidal energy systems, including a fully submerged, horizontal-axis electro-turbine system, similar to Verdant Tidal Turbines in New York's East River, a platform-based Marine Current Turbine, now operating in Northern Ireland's Strangford Narrows, and the Rotech Lunar Energy system, to be installed off the South Korean Coast. A fourth type of tidal energy system studied is a novel JPL/Caltech hydraulic energy transfer system that uses submerged turbine blades which are mechanically attached to adjacent high-pressure pumps, instead of to adjacent electrical turbines. The generated highpressure water streams are combined and transferred to an onshore hydroelectric plant by means of a closed-cycle pipeline. The hydraulic energy transfer system was found to be cost competitive, and it allows all electronics to be placed onshore, thus greatly reducing maintenance costs and corrosion problems. It also eliminates the expenses of conditioning and transferring multiple offshore power lines and of building offshore platforms embedded in the sea floor.

hydraulic↗
DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2000

NASA's Aero-Space Technology

This presentation reviews the three pillars and the associated goals of NASA's Aero-Space Technology Enterprise. The three pillars for success are: (1) Global Civil Aviation, (2) Revolutionary Technology Leaps, (3) Advanced Space Transportation. The associated goals of the first pillar are to reduce accidents, emissions, and cost, and to increase the aviation system capacity. The goals of the second pillar are to reduce transoceanic travel time, revolutionize general aviation aircraft, and improve development capacity. The goals associated with the third pillar are to reduce the launch cost for low earth orbit and to reduce travel time for planetary missions. In order to meet these goals NASA must provide next-generation design capability for new and or experimental craft which enable a balance between reducing components of the design cycle by up to 50% and or increasing the confidence in design by 50%. These next-generation design tools, concepts, and processes will revolutionize vehicle development. The presentation finally reviews the importance of modeling and simulation in achieving the goals.

Milstead, Phil↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗
NASA NTRS2008

Second Generation Prototype Design and Testing for a High Altitude Venus Balloon

This paper describes the development of a second generation prototype balloon intended for flight in the upper atmosphere of Venus. The design of this new prototype incorporates lessons learned from the construction and testing of the first generation prototype, including finite element analyses of the balloon stresses and deformations, measured leak performance after handling and packaging, permeability and optical property measurements on material samples, and sulfuric acid testing. An improved design for the second generation prototype was formulated based on these results, although the spherical shape and 5.5 m diameter size were retained. The resulting balloon has a volume of 87 cubic meters and is capable of carrying a 45 kg payload at a 55 km altitude at Venus. The design and fabrication of the new prototype is described, along with test data for inflation and leakage performance.

mobility↗
DOE OSTI2022

Considerations in upconversion: A practical guide to sum-frequency generation spectrometer design and implementation

Here, in this tutorial review, we discuss how the choice of upconversion pulse shape in broadband vibrational sum-frequency generation (SFG) spectrometer design impacts the chemical or physical insights one can obtain from a set of measurements. A time-domain picture of a vibrational coherence being mapped by a second optical field is described and the implications of how this mapping, or upconversion process, takes place are given in the context of several popular and emerging approaches found in the literature. Emphasis is placed on broadband frequency-domain measurements, where the choice of upconversion pulse enhances or limits the information contained in the SFG spectrum. We conclude with an outline for a flexible approach to SFG upconversion using pulse-shaping methods and a simple guide to design and optimize the associated instrumentation.

47 OTHER INSTRUMENTATION↗
NASA NTRS2018

Oxygen Generation Assembly Design for Exploration Missions

Future Exploration missions will require an Oxygen Generation Assembly (OGA) to electrolyze water to supply oxygen for crew metabolic consumption. The system design will be based on the International Space Station (ISS) OGA but with added improvements based on lessons learned during ISS operations. These improvements will reduce system weight, crew maintenance time and resupply mass from Earth while increasing reliability. Currently, the design team is investigating the feasibility of the upgrades by performing ground tests and analyses. Upgrades being considered include: redesign of the electrolysis cell stack, deletion of the hydrogen dome, replacement of the hydrogen sensors, deletion of the wastewater interface, redesign of the recirculation loop deionizing bed and redesign of the cell stack Power Supply Module. The upgrades will be first demonstrated on the ISS OGA.

Takada, Kevin↗
NASA NTRS1989

SRB thermal environments

The objective was to utilize and expand the Solid Rocket Booster (SRB) orbital flight test data base for better predictions of future flight environments. There were five tasks associated with this effort: analyze the internal aft skirt wind tunnel data and incorporate it into a data base for generating design and preflight reeentry thermal environments; generate reentry design thermal environments for the SRB steel case with the nozzle extension off; generate reentry design thermal environments for the SRB Filament Wound Case with the nozzle extension off; develop an engineering tool to analyze the 3-D flowfield around the SRB aft skirt during reentry for the purpose of obtaining the frequency and severity of the belching gas intrusion internal to the aft skirt; and perform SRM transient joint flow analysis for subscale and full scale motor firing as well as determine the effects of debonds of the insulation on the fill time and heating within the field joint insulation. In addition, this work was extended to provide support for the 51L Shuttle SRB failure analysis.

Crain, W. K.↗
DOE OSTI2025

Steam generator model design parameter sensitivity study for small modular reactor system

Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Impact of Timing in the Design Process on Students’ Application of Design for Additive Manufacturing Heuristics

The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules or heuristics. It can be challenging for novice designers to create successful designs for additive manufacturing (AM), due to its recent surge in popularity and lack of formal education or training. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process: before the initial design, between the initial design and redesign, and after the redesign. The novelty and quality of each of the resulting designs were evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better-suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants who received heuristic information between the design sessions experienced a decrease in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students’ perceptions of their ability to perform DfAM-related tasks. Furthermore, these results validate the practicality of design heuristics in lecture form as AM training tools while also emphasizing the importance of iteration in the design process.

computer-aided design↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Mod-5A Wind Turbine Generator Program Design Report. Volume 2: Conceptual and Preliminary Design, Book 1

The design, development and analysis of the 7.3 MW MOD-5A wind turbine generator is documented. There are four volumes. In Volume 2, book 1 the requirements and criteria for the design are presented. The conceptual design studies, which defined a baseline configuration and determined the weights, costs and sizes of each subsystem, are described. The development and optimization of the wind turbine generator are presented through the description of the ten intermediate configurations between the conceptual and final designs. Analyses of the system's load and dynamics are presented.

Source record↗