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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 73 records · Page 4

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NASA NTRS1979

Thermophysical property data - Who needs them

Specific examples are cited herein to illustrate the universal needs and demands for thermophysical property data. Applications of the principle of similarity in fluid mechanics and heat transfer and extensions of the principle to fluid mixtures are discussed. It becomes quite clear that no matter how eloquent theories or experiments in fluid mechanics or heat transfer are, the results of their application can be no more accurate than the thermophysical properties required to transform these theories into practice, or in the case of an experiment, to reduce the data. Present-day projects take place on such a scale that the need for international standards and mutual cooperation is evident.

Hendricks, R. C.↗

NASA NTRS1978

CAD/CAM data management needs, requirements and options

The requirements for a data management system in support of technical or scientific applications and possible courses of action were reviewed. Specific requirements were evolved while working towards higher level integration impacting all phases of the current design process and through examination of commercially marketed systems and related data base research. Arguments are proposed for varied approaches in implementing data base systems ranging from no action necessary to immediate procurement of an existing data base management system.

Lopatka, R. S.↗

DOE OSTI2021

SECARB-USA: Needs Assessment Framework for Storage Complexes (Task 2.1.b)

A team of SECARB storage experts examined 63 formations at 39 sites that were selected to represent the range of diversity of newly assessed, as well as well-advanced, storage prospects in the SECARB region. We inventoried the data needs triggered by the requirements to obtain a Class VI UIC storage permit, the data needs that results from the requirement to create geocellular fluid flow models to support that permit, and by pragmatic and best practice inputs such as public acceptance, regulatory readiness, and pore space leasing. We anchored both the needs inventory and the processes for and cost of meeting the needs with data from 9 sites in in the SECARB area that have advanced far in characterization. Results show that the total cost of characterization prior to obtaining a permit is convergent, because the permit and modeling requirements drive projects to obtain the same types of data for all cases. The high cost data that control cost are 1) drilling, coring, core-testing, logging, sampling and testing a characterization well and 2) collection of a 3-D seismic survey to map reservoir and confining system properties over the area of the plume or the area of elevated pressure. In 5 of our case study sites we determined that one or both of these costs could be avoided because the needs are met by available data.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗

DOE OSTI2025

Livewire: Automatic Annotations

Diogenes processes datasets to provide data quality metrics for the Livewire platform and creates standardized data dictionaries from data annotations. Diogenes needs data annotations that clearly outline thenformat and organization of the data. It also relies on the type, class, and unit of each data piece for comprehensive analysis, which it cannot determine independently. The Annotation Tool significantly reduces the time needed to create annotations for Diogenes by generating data annotations with the correct formatting and content. It also employs machine learning and hard-coded models to automatically annotate data class, quality type, and data units.

33 - ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2000

Atomic Data in X-Ray Astrophysics

With the launches of the Chandra X-ray Observatory (CXO) and the X-ray Multimirror Mission (XMM) and the upcoming launch of the Japanese mission ASTRO-E, high resolution X-ray spectroscopy of cosmic sources has begun. Early, deep observations of three stellar coronal sources will provide not only invaluable calibration data, but will also give us benchmarks for the atomic data under collisional equilibrium conditions. Analysis of the Chandra X-ray Observatory data, and data from other telescopes taken simultaneously, for these stars is ongoing as part of the Emission Line Project. Goals of the Emission Line Project are: (1) to determine and verify accurate and robust diagnostics and (2) to identify and prioritize issues in fundamental spectroscopy which will require further theoretical and/or laboratory work. The Astrophysical Plasma Emission Database will be described in some detail, as it is introducing standardization and flexibility into X-ray spectral modeling. Spectral models of X-ray astrophysical plasmas can be generally classified as dominated by either collisional ionization or by X-ray photoionization. While the atomic data needs for spectral models under these two types of ionization are significantly different, there axe overlapping data needs, as I will describe. Early results from the Emission Line Project benchmarks are providing an invaluable starting place, but continuing work to improve the accuracy and completeness of atomic data is needed. Additionally, we consider the possibility that some sources will require that both collisional ionization and photoionization be taken into account, or that time-dependent ionization be considered. Thus plasma spectral models of general use need to be computed over a wide range of physical conditions.

Brickhouse, N. S.↗

NASA NTRS1972

The Prospects of Utilizing Remotely Sensed Data in the Preparation of Environmental-Sociological Models

The use of remote sensing to acquire needed data concerning soils, water conditions, microclimatic conditions, flora, land use, recreational potential, and cultural conditions was studied for solving data needs. The procedures and means whereby such data can be acquired and processed into an information format are demonstrated.

Honea, R. B.↗

NASA NTRS1982

Wind and turbine characteristics needed for integration of wind turbine arrays into a utility system

Wind data and wind turbine generator (WTG) performance characteristics are often available in a form inconvenient for use by utility planners and engineers. The steps used by utility planners are summarized and the type of wind and WTG data needed for integration of WTG arrays suggested. These included long term yearly velocity averages for preliminary site feasibility, hourly velocities on a 'wind season' basis for more detailed economic analysis and for reliability studies, worst-case velocity profiles for gusts, and various minute-to-hourly velocity profiles for estimating the effect of longer-term wind fluctuations on utility operations. wind turbine data needed includes electrical properties of the generator, startup and shutdown characteristics, protection characteristics, pitch control response and control strategy, and electro-mechanical model for stability analysis.

Park, G. L.↗

DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗

DOE OSTI2021

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Needs Assessment Framework for Storage Complexes

42 ENGINEERING↗

NASA NTRS1991

Microgravity science requirements and the need for data compression

The Microgravity Science and Applications Div. (MSAD) of the NASA Office of Space Science and Applications (OSSA) is responsible for encouraging and directing the research of a wide range of physical phenomena in reduced gravity. Under MSAD's direction, NASA-Lewis is presently developing the concept of a multiuser facility which will perform combustion science experiments in space. This facility, the Combustion Experiment Module (CEM), will be located in either the Shuttle Spacelab or the Space Station Freedom lab and will be operational by mid-1997. In addition to standard instrumentation to measure temperature, pressure, and acceleration, CEM shall use a variety of imaging and optical diagnostic techniques. Images shall be the primary source of experimental data. These images create an enormous amount of data which must be archived on orbit for later analysis. Also, ground based investigators will require enough data from the orbiting facility to determine if the experimental parameters need to be changed before proceeding with the next run. The storage and transmission of this data present a major challenge to the CEM design. Data compression will play a major role in the design of the CEM diagnostics system.

Hartz, William G.↗

NASA NTRS1997

Efficient GO2/GH2 Injector Design: A NASA, Industry and University Cooperative Effort

Developing new propulsion components in the face of shrinking budgets presents a significant challenge. The technical, schedule and funding issues common to any design/development program are complicated by the ramifications of the continuing decrease in funding for the aerospace industry. As a result, new working arrangements are evolving in the rocket industry. This paper documents a successful NASA, industry, and university cooperative effort to design efficient high performance GO2/GH2 rocket injector elements in the current budget environment. The NASA Reusable Launch Vehicle (RLV) Program initially consisted of three vehicle/engine concepts targeted at achieving single stage to orbit. One of the Rocketdyne propulsion concepts, the RS 2100 engine, used a full-flow staged-combustion cycle. Therefore, the RS 2100 main injector would combust GO2/GH 2 propellants. Early in the design phase, but after budget levels and contractual arrangements had been set the limitations of the current gas/gas injector database were identified. Most of the relevant information was at least twenty years old. Designing high performance injectors to meet the RS 2100 requirements would require the database to be updated and significantly enhanced. However, there was no funding available to address the need for more data. NASA proposed a teaming arrangement to acquire the updated information without additional funds from the RLV Program. A determination of the types and amounts of data needed was made along with test facilities with capabilities to meet the data requirements, budget constraints, and schedule. After several iterations a program was finalized and a team established to satisfy the program goals. The Gas/Gas Injector Technology (GGIT) Program had the overall goal of increasing the ability of the rocket engine community to design efficient high-performance, durable gas/gas injectors relevant to RLV requirements. First, the program would provide Rocketdyne with data on preliminary gas/gas injector designs which would enable discrimination among candidate injector designs. Secondly, the program would enhance the national gas/gas database by obtaining high-quality data that increases the understanding of gas/gas injector physics and is suitable for computational fluid dynamics (CFD) code validation. The third program objective was to validate CFD codes for future gas/gas injector design in the RLV program.

Tucker, P. K.↗

DOE OSTI2023

Modular machine learning-based elastoplasticity: Generalization in the context of limited data

The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.

42 ENGINEERING↗

DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗

DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1978

A study on the utilization of advanced composites in commercial aircraft wing structure: Executive summary

The overall wing study objectives are to study and plan the effort by commercial transport aircraft manufacturers to accomplish the transition from current conventional materials and practices to extensive use of advanced composites in wings of aircraft that will enter service in the 1985-1990 time period. Specific wing study objectives are to define the technology and data needed to support an aircraft manufacturer's commitment to utilize composites primary wing structure in future production aircraft and to develop plans for a composite wing technology program which will provide the needed technology and data.

Watts, D. J.↗

NASA NTRS1992

The manned transportation system study - Defining human pathways into space

The Manned Transportation System (MTS) Study, conducted by a NASA-Industry Team (NIT), has developed substantiating data for subsequent NASA decisions on the “right” set of manned transportation elements needed for human access to space. It also provides the framework for detailed definition of those manned transportation elements. Including the next manned transportation system, to be developed. Process and product are presented to inform the aerospace community and subject our approach and conclusions to peer review, NASA/JSC lead the NIT, with participation from KSC, LaRC, MSFC, and NASA/Headquarters, along with six aerospace contractor (Boeing, General Dynamics, Lockheed Martin Marietta, McDonnell Douglas, and Rockwell). Each major milestone was archived through team consensus. Our mission model was derived from the FY90 Civil Needs Data Base (CNDB) and included flight assignments for Department of Defense (DOD) missions. Identifying and defining architecture evaluation criteria, i.e. attributes, specified the amount and type of data needed for each concept under consideration. Several architectures, each beginning with today’s transportation systems, were defined using representative systems to explore our future options and address specific questions currently being debated. Our solutions are a function of the level of space activity the nation chooses to follow. However, they all emphasize affordability, safety, routineness, and reliability. Finally, key issues associated with our current business practices were challenged and the impact associated with those practices quantified.

NASA Langley Research Center↗

DOE OSTI2021

Design Readiness And Maturity Assessment (DRAMA) Tool for Advanced Reactors

Abstract – This research is developing a formal, repeatable method to assess the readiness and maturity of an advanced nuclear reactor design for licensing and deployment. This design readiness and maturity assessment (DRAMA) tool will be capable of determining the readiness of the design and of all parties needed to bring a particular advanced reactor design to fruition. Beneficiaries and stakeholders include the design team, research organizations (required to collect needed data and develop design tools), standards organizations, research facilities to support gathering of needed data, supply chain and construction organizations, and everyone responsible for legal and regulatory infrastructure (including defining import-export requirements). Recent experience has shown that even the most experienced engineering and construction organizations, with decades of experience in the nuclear power business, have had significant challenges in bringing designs to completion, licensing the designs, and constructing new plants. For new entries into the field, simply understanding the unique environmental and regulatory requirements have been daunting. A significant part of the challenge is that new entries into the market do not know what they don't know. This tool will provide design teams a better understanding of their readiness to proceed to licensing (and other steps in the process), while at the same time providing a valuable metric for other interested organizations, such as funding agencies, national regulators, and international markets. The DRAMA tool will provide an assessment of the likelihood of successfully completing licensing and deployment and will also create the capability to assess the ability of regulatory infrastructure and the supply chain to support the deployment of any design or class of designs. It will also help prioritize research and policy efforts to improve the likelihood of deployment of the next generation of advanced reactors.

Arndt, Steven↗