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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Extending radiative transfer models by use of Bayes rule

This paper presents a procedure that extends some existing radiative transfer modeling techniques to problems in atmospheric science where curvature and layering of the medium and dynamic range and angular resolution of the signal are important. Example problems include twilight and limb scan simulations. Techniques that are extended include successive orders of scattering, matrix operator, doubling, Gauss-Seidel iteration, discrete ordinates and spherical harmonics. The procedure for extending them is based on Bayes' rule from probability theory.

Whitney, C.↗
NASA NTRS1980

Large space structures activity at MSFC

This paper describes a broad based set of activities which have been undertaken at the Marshall Space Flight Center (MSFC) developing technologies necessary to emplace and operate large structures in space. Progress has been made in the development of processes and equipment needed for the in space fabrication of structural members in both aluminum and graphite composite materials. Designs for member joining attachments have been completed and the evaluation of procedures for structural assembly have been simulated and evaluated utilizing the MSFC Neutral Buoyancy Facility. A concept of a flight test necessary for flight verification of the technologies for space fabrication, assembly, and operation of large space structure is described. Recently initiated is a project to develop design characteristics of a deployable structure required for the Science and Applications Space Platform (SASP). This is being accomplished through the design, fabrication, analysis, and test of a full scale ground test article which reflects the influence of system requirements of the SASP. Development of designs for erectable/deployable structures which are modular and have various applications to large space structures is in progress.

Denton, S. J.↗
NASA NTRS2005

Thermal Conductivity Based on Modified Laser Flash Measurement

The laser flash method is a standard method for thermal diffusivity measurement. It employs single-pulse heating of one side of a thin specimen and measures the temperature response of the other side. The thermal diffusivity of the specimen can be obtained based on a one-dimensional transient heat transfer analysis. This paper reports the development of a theory that includes a transparent reference layer with known thermal property attached to the back of sample. With the inclusion of heat conduction from the sample to the reference layer in the theoretical analysis, the thermal conductivity and thermal diffusivity of sample can be extracted from the temperature response data. Furthermore, a procedure is established to select two points from the data to calculate these properties. The uncertainty analysis indicates that this method can be used with acceptable levels of uncertainty.

Lin, Bochuan↗
NASA NTRS2012

Performance Data Errors in Air Carrier Operations: Causes and Countermeasures

Several airline accidents have occurred in recent years as the result of erroneous weight or performance data used to calculate V-speeds, flap/trim settings, required runway lengths, and/or required climb gradients. In this report we consider 4 recent studies of performance data error, report our own study of ASRS-reported incidents, and provide countermeasures that can reduce vulnerability to accidents caused by performance data errors. Performance data are generated through a lengthy process involving several employee groups and computer and/or paper-based systems. Although much of the airline indUStry 's concern has focused on errors pilots make in entering FMS data, we determined that errors occur at every stage of the process and that errors by ground personnel are probably at least as frequent and certainly as consequential as errors by pilots. Most of the errors we examined could in principle have been trapped by effective use of existing procedures or technology; however, the fact that they were not trapped anywhere indicates the need for better countermeasures. Existing procedures are often inadequately designed to mesh with the ways humans process information. Because procedures often do not take into account the ways in which information flows in actual flight ops and time pressures and interruptions experienced by pilots and ground personnel, vulnerability to error is greater. Some aspects of NextGen operations may exacerbate this vulnerability. We identify measures to reduce the number of errors and to help catch the errors that occur.

countermeasures↗
DOE OSTI2013

An Advanced Platform for Development and Evaluation of Photovoltaic Inverters Using Hardware-in-the-Loop

A rapid shift in the world's energy paradigm towards a higher proliferation of renewables is underway. As these new resources are interconnected with the EPS, it is essential that they not only comply with the relevant interconnection standards, but that they also are evaluated under various dynamic resource input and grid conditions to ensure that they will properly participate in the advancedEPS architecture. To this end, NREL leveraged controller hardware-in-the-loop (CHIL) and power hardware-in-the-lopp (PHIL) simulation techniques to create an advanced platform for the development and evaluation of photovoltaic (PV) inverters. This paper describes a CHIL-based methodology by which PV inverter controllers can be tested against simulated inverter hardware before prior to at powertesting, a PHIL-based PV simulator that can be used to test a PV inverter's response to the truly dynamic output of a PV array, and a grid interconnection evaluator that leverages PHIL techniques to automate the execution and analysis of key interconnection standard test procedures. A number of demonstration test results are presented. These capabilities make up a novel platform that will helpdevelop the PV inverters that will support the next generation EPS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition) on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Instrument failure detection and isolation in a system with variable plant parameters

This paper considers the problem of detecting instrument failures in a system in which there are plant parameter variations. The method described is based on the use of Kalman filters to provide a detection comparison in a manner similar to Clark's dedicated observer method. To improve the sensitivity to sensor variation and reduce the sensitivity to plant parameter variation the Kalman filters are designed to be sub-optimal. A procedure is described to design the sub-optimal filters so as to improve a measure of the ratio of sensor to plant sensitivity.

Alexandro, F. J., Jr.↗
NASA NTRS1991

Wing design code using three-dimensional Euler equations and optimization

This paper describes a new wing design code which is based on the Euler equations and a constrained numerical optimization technique. The geometry modification is based on a set of fundamental modes define on the unit interval. A design example involving a high speed civil transport wing is presented to demonstrate the usefulness of the design code. It is shown that the use of an Euler solver in the direct numerical optimization procedures is affordable on the current generation of supercomputers.

Chang, I-Chung↗
NASA NTRS1998

On-Orbit Model Refinement for Controller Redesign

High performance control design for a flexible space structure is challenging since high fidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. A new procedure for refining a multivariable open loop plant model based on closed-loop response data is presented. Using a minimal representation of the state space dynamics, a least squares prediction error method is employed to estimate the plant parameters. This control-relevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance. This paper presents an algorithm for iterative closed-loop system identification and controller redesign along with illustrative examples.

Whorton, Mark S.↗
NASA NTRS2008

Measurement Assurance for End-Item Users

The goal of a Quality Management System (QMS) as specified in ISO 9001 and AS9100 is to assure the end product meets specifications and customer requirements. Measuring devices, often called measuring and test equipments (MTE), provide the evidence of product conformity to the prescribed requirements. Therefore the processes which employ MTE can become a weak link to the overall QMS if proper attention is not given to development and execution of these processes. Traditionally, calibration of MTE is given more focus in industry standards and process control efforts than the equally important proper usage of the same equipment. It is a common complaint of calibration laboratory personnel that MTE users are only interested in "a sticker." If the QMS requires the MTE "to demonstrate conformity of the product," then the quality of the measurement process must be adequate for the task. This leads to an ad hoc definition; measurement assurance is a discipline that assures that all processes, activities, environments, standards, and procedures involved in making a measurement produce a result that can be rigorously evaluated for validity and accuracy. To evaluate that the existing measurement processes are providing an adequate level of quality to support the decisions based upon this measurement data, an understanding of measurement assurance basics is essential. This topic is complimentary to the calibration standard, ANSI/NCSL Z540.3-2006, which targets the calibration of MTE at the organizational level. This paper will discuss general measurement assurance when MTE is used to provide evidence of product conformity, therefore the target audience of this paper is end item users of MTE. A central focus of the paper will be the verification of tolerances and the associated risks, so calibration professionals may find the paper useful in communication with their customers, MTE users.

Mimbs, Scott M.↗
NASA NTRS2016

Tangle-Free Finite Element Mesh Motion for Ablation Problems

In numerical simulations involving boundaries that evolve in time, the primary challenge is updating the computational mesh to reflect the physical changes in the domain. In particular, the fundamental objective for any such \mesh motion" scheme is to maintain mesh quality and suppress unphysical geometric anamolies and artifacts. External to a physical process of interest, mesh motion is an added component that determines the specifics of how to move the mesh given certain limited information from the main system. This paper develops a set of boundary conditions designed to eliminate tangling and internal collision within the context of PDE-based mesh motion (linear elasticity). These boundary conditions are developed for two- and three-dimensional meshes. The paper presents detailed algorithms for commonly occuring topological scenarios and explains how to apply them appropriately. Notably, the techniques discussed herein make use of none of the specifics of any particular formulation of mesh motion and thus are more broadly applicable. The two-dimensional algorithms are validated by an extensive verification procedure. Finally, many examples of diverse geometries in both two- and three-dimensions are shown to showcase the capabilities of the tangle-free boundary conditions.

Droba, Justin↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRS2012

Green Planet Architecture - A Methodology for Self-Sustainable Distributed Renewable Energy Ecosystems

Our planet has been endowed with a host of natural mechanisms to keep the environment and climate in balance. Humans are now facing the need to restore this balance that has been upset in the past years because of a growing population and resource demands. To steer dependency away from freshwater crops and decrease environmental damage from humanity s fuel and energy demands, it is necessary to take advantage of the natural adaptive biomass resources that are already in place. Using methods of Green Planet Architecture, based on compilations of current research and procedures, could lead to new forms of energy and fueling as well as new sources for food and feed. Green Planet Architecture involves climatic adaptive biomass; geospatial intelligence; agri- and aqua-culture life cycles; and soil, wetland, and shoreline restoration. Plants such as Salicornia, seashore mallow, castor, mangroves, and perhaps Moringa can be modified (natural, model-assisted, or genetically modified) to thrive in salt-water and brackish water or otherwise not arable conditions, making them potentially new crops that will not displace traditional farming. These fueling sources also have potential to be used in other rapid-growth industries, such as the aviation industry, that have incentive to move towards more sustainable fuel supplies. This paper highlights an example of how synergistic development of biomass resources and geospatial intelligence high-performance computing capabilities can be focused to resolve potential drought-famine problems. These techniques, provide a basis for future e-science-based discovery (and access) through technology that can be expanded to support global societal applications.

Saxena, Nikita T.↗
DOE OSTI2016

Off-line tracking of series parameters in distribution systems using AMI data

Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimation procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.

Williams, Tess L.↗
DOE OSTI2022

Setting boundaries on the recipe for a successful RbF post-deposition treatment of CIGS

RbF post-deposition treatments have been explored in the literature for increasing the open-circuit voltage, fill factor, and hence the efficiency of Cu(In,Ga)Se 2 solar cells. However, given the few papers documenting the experimental steps, it was difficult to quickly reproduce the results. This contribution describes some of the optimization steps that led to a successful RbF PDT based on device performance. Here we present results that put boundaries on the temperatures of the RbF cell and the lamp (for sample heating) setpoint. The best recipe for our specific growth process is documented in detail so that others may copy the procedure and possibly arrive at a successful RbF PDT in a reasonable time.

14 SOLAR ENERGY↗
DOE OSTI2020

On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation

Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often requires large amounts of simulation data that may not be available in the case of computationally expensive systems. In this paper, to alleviate this issue for uncertainty propagation, we explore the application of transfer learning techniques using training data generated from both high- and low-fidelity models. We explore two strategies for coupling these two datasets during the training procedure, namely, the standard transfer learning and the bi-fidelity-weighted learning. In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data. The high-fidelity data are then used to adapt the parameters of the upper layer(s) of the low-fidelity network, or train a simpler neural network to map the output of the low-fidelity network to that of the high-fidelity model. In the latter approach, the entire low-fidelity network parameters are updated using data generated via a Gaussian process model trained with a small high-fidelity dataset. The parameter updates are performed via a variant of stochastic gradient descent with learning rates given by the Gaussian process model. Using three numerical examples, we illustrate the utility of these bi-fidelity transfer learning methods where we focus on accuracy improvement achieved by transfer learning over standard training approaches.

97 MATHEMATICS AND COMPUTING↗