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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2020

Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II

Beam-driven plasma wakefield acceleration (PWFA) achieves the same energy gain in a single meter, for which conventional accelerators require several kilometers, however much work is still required to match the beam quality of conventional accelerators. The PWFA processes to be studied at the FACET-II facility will utilize extremely short (down to a few fs) high energy (10 GeV), high charge (few nC) electron and eventually positron bunches. The PWFA process is extremely sensitive to the detailed longitudinal current profiles of these bunches and it would be of great benefit to have precise measurement and control of these profiles. We present an adaptive model tuning technique for FACET-II to adaptively tune online models based on real time accelerator and beam data to continuously provide a non-invasive diagnostic which predicts the longitudinal phase space (LPS) of extremely short and highly compressed electron beams, which otherwise require destructive X-band transverse deflecting cavity (XTCAV)-based measurements. Based on simulation studies, our method has the potential for: (1). The development of a non-invasive longitudinal phase space diagnostic by adaptively tuning models based on non-invasive measurements such as energy spread spectra which could be recorded at all of the bunch compressors in the facility. (2). Utilize these diagnostic to perform model-independent feedback-based to achieve desired longitudinal phase space distributions. (3). Utilize the model-independent feedback approach to maximize energy gain while minimizing emittance growth and energy gain variance of the PWFA process by tuning accelerator parameters, while monitoring what longitudinal phase space the algorithm has found, information that will be useful for further analytical and simulation studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Abstract Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
DOE OSTI2021

wtDigiTwin (Wind Turbine Digital Twin) [SWR-21-15]

This wind turbine digital twin software (wtDigiTwin) provides a digital twin solution for wind turbine applications. The focus of wtDigiTwin is to estimate loads, motions and environmental conditions for an operating wind turbine. The program uses supervisory control and data acquisition (SCADA) measurements as inputs, together with a wind turbine model. The wind industry is currently challenged by the high cost of operation and maintenance. These costs could be mitigated if component failures are predicted, but such predictions are difficult unless the turbines are equipped with expensive measuring devices. The alternative is to use a digital twin such as wtDigiTwin to estimate the necessary signals. wtDigiTwin can perform online prediction of signals that are otherwise not measured, using a limited set of reliable measurements and a physics-based model. The predicted signals can be used in applications that have direct cost benefits: 1) real-time estimation of the fatigue consumption of key components of the wind turbine; 2) root cause analyses and failure detections ; 3) lifetime reassessments ; 4) improvements to follow-on designs. The current version provides examples to estimate wind speed, thrust, torque, tower-top position, and tower loads on an onshore wind turbine using the following measurements tower top acceleration, generator torque, pitch, and rotational speed. The model combines a linear state-space model, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The state space model is obtained either using OpenFAST linearizations, or using the yams package provided with the software.

Branlard, Emmanuel↗
DOE OSTI2024

Collaborative Research: Enhancing Laser-Based Ion Sources with High Data Rate Techniques

This collaborative research project focuses on leveraging advanced machine learning techniques to analyze and optimize data from high-repetition-rate laser experiments. The main goal is to apply modern computing hardware, customized data acquisition firmware/software, and machine learning approaches to improve data analysis and experimental control. The project also explores how methodology can be developed on smaller-scale experimental setups and then translated to larger facilities within DOE's LaserNetUS network. With extensive data collection and modeling, the research aims to predict and optimize experimental parameters to enhance performance and efficiency.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗
NASA NTRS1975

Exploratory study to induce fan noise in the test section of the NASA Langley full-scale wind tunnel

Measures to reduce the intensity of fan noise in the NASA Langley 30 ft x 60 ft subsonic wind tunnel were sought. Measurements were first performed to document existing aerodynamic and acoustic conditions. The purpose of these experiments was to (1) obtain the transfer function between the sound power output of the fan and the sound pressure on the test platform, (2) evaluate the sound attenuation around the tunnel circuit, (3) measure simultaneously the flow profile and the turbulence spectrum of the inflow to the fan and the noise on the test platform, and (4) perform flow observations and identify secondary noise sources. Subsequently, these data were used to predict (1) the relative contribution of the major aerodynamic parameters to total fan noise and (2) the effect of placing a dissipative silencer in the collector duct upstream of the fan. Promising noise control measures were identified and recommendations were made on how to evaluate them.

Ver, I. L.↗
NASA NTRS1981

Traveling-wave maser, closed cycle refrigerator automation

At the present time all traveling-wave maser and closed-cycle refrigerator system (TWM-CCR) and their power supply and helium compressor assemblies are operated manually. Many man-hours are required of highly trained and experienced operators, resulting in high operator cost. Of greater importance are the problems of unpredictable failure and long downtimes. Since field performance is not monitored, system degradation is detected only when it has progressed to the point of catastrophic failure. Without the aid of performance histories, failure diagnosis is arduous and slow. Rational and functional requirements for the TWM-CCR Automation project are the following: (1) to improve reliability and reduce downtime by providing unattended monitor and control, alarm and fault location, failure prediction and self-corrective action; (2) to reduce operator cost by providing automatic tuning and cooldown procedures; and (3) to improve system operations and development by providing the needed engineering data.

Mcneil, J. A.↗
NASA NTRS1981

Space Construction Experiment Definition Study (SCEDS), part 1. Volume 1: Executive summary

Definition was completed on a basic flight experiment which will provide data on the construction of large space systems from the orbiter which could not be practicably obtained from ground tests. Dynamic behavior of a representative large structure was predicted. On-orbit construction operations were studied. Orbiter control during and after construction was investigated. Evolutionary or supplemental flight experiments for the development of augmentation of a basic flight experiment were identified and defined.

Source record↗
NASA NTRS1986

Magnetic configuration of the Venus magnetosheath

A data set consisting of nearly six Venus years of Pioneer Venus Orbiter magnetometer data is analyzed in order to describe the configuration of the magnetosheath. A coordinate system is developed which isolates the effects of the solar wind flow and the interplanetary magnetic field (IMF). The data indicate that the magnetosheath field magnitude is responsive to solar wind dynamic pressure and that the compression of the upstream field is controlled by magnetosonic Mach number. The draping of the field is similar to that predicted by gasdynamic modeling; specific draping features include distinct dependence on the radial and the transverse components of the IMF, and a tendency of the field to encircle the planet at low altitude. Certain features of the processes of mass loading by magnetosheath fluctuations and by the motional electric field are examined. Magnetic fluctuations can dominate the magnetosheath field for periods of low interplanetary cone angle and in general for the magnetosheath regions near the quasi-parallel part of the bow shock. The magnetosheath magnetic field magnitude displays a hemispherical asymmetry; the sense of this asymmetry is controlled by the cross-flow component of the IMF in a manner which indicates that ion pickup by the convective electric field occurs preferentially in one hemisphere. The results of this survey indicate that mass loading is a significant process which should be incorporated into computational models but that a fluid treatment of the planetary ion component may not be appropriate.

Phillips, J. L.↗
NASA NTRS1996

The Implications of ACTS Technology on the Requirements of Rain Attenuation Modeling for Communication System Specification and Analysis at 30/20 GHz and Beyond

The advent of the use of the Ka-Band for space communications, coupled with the introduction of digital modulation techniques as well as multiple-beam methodology for satellites, has deemed it necessary to reassess the plethora of rain attenuation prediction models in use. The Advanced Communication Technology Satellite (ACTS) Project, undertaken by the National Aeronautics and Space Administration in 1983, offered such challenges to rain attenuation prediction modeling. Up to 1983, no such single modeling formalism existed that could fill such requirements. Not even the work done by the NASA Propagation Experimenters (NAPEX) Group had envisioned such requirements, so no dynamic Ka-Band data existed from which one could draw conclusions. In this paper, the basic rudiments of what has become to be known as the 'ACTS Rain Attenuation Prediction Model' will be presented. The concept of rain fade mitigation control availability will be introduced. A new evaluation is then presented for the performance of satellite communication systems, in particular, those to be operating within the Ka-Band and above, that will necessarily employ some type of dynamic rain fade mitigation procedure.

Manning, Robert M.↗
NASA NTRS1996

Bubble and Slug Flow at Microgravity Conditions: State of Knowledge and Open Questions

Based on the experiments carried out over the past decade at microgravity conditions, an overview of our current knowledge of bubbly and slug flows is presented. The transition from bubble to slug flow, the void fraction and the pressure drop are discussed from the data collected in the literature. The transition from bubble to slug flow may be predicted by introducing a critical void fraction that depends on the fluid properties and the pipe diameter; however, the role of coalescence which controls this transition is not clearly understood. The void fraction may be accurately calculated using a drift-flux model. It is shown from local measurements that the drift of the gas with respect to the mixture is due to non-uniform radial distribution of void fraction. The pressure drop happens to be controlled by the liquid flow for bubbly flow whereas for slug flow the experimental results show that pressure drops is larger than expected. From this study, the guidelines for future research in microgravity are given.

Colin, C.↗
NASA NTRS1995

In-Flight Thermal Performance of the Lidar In-Space Technology Experiment

The Lidar In-Space Technology Experiment (LITE) was developed at NASA s Langley Research Center to explore the applications of lidar operated from an orbital platform. As a technology demonstration experiment, LITE was developed to gain experience designing and building future operational orbiting lidar systems. Since LITE was the first lidar system to be flown in space, an important objective was to validate instrument design principles in such areas as thermal control, laser performance, instrument alignment and control, and autonomous operations. Thermal and structural analysis models of the instrument were developed during the design process to predict the behavior of the instrument during its mission. In order to validate those mathematical models, extensive engineering data was recorded during all phases of LITE's mission. This inflight engineering data was compared with preflight predictions and, when required, adjustments to the thermal and structural models were made to more accurately match the instrument s actual behavior. The results of this process for the thermal analysis and design of LITE are presented in this paper.

Roettker, William↗
DOE OSTI2024

The Role of Data Filtering in Open Source Software Ranking and Selection

Faced with more than 100M open source projects, a more manageable small subset is needed for most empirical investigations. More than half of the research papers in leading venues investigated filtering projects by some measure of popularity with explicit or implicit arguments that unpopular projects are not of interest, may not even represent "real" software projects, or that less popular projects are not worthy of study. However, such filtering may have enormous effects on the results of the studies if and precisely because the sought-out response or prediction is in any way related to the filtering criteria.This paper exemplifies the impact of this common practice on research outcomes, specifically how filtering of software projects on GitHub based on inherent characteristics affects the assessment of their popularity. Using a dataset of over 100,000 repositories, we used multiple regression to model the number of stars -a commonly used proxy for popularity- based on factors such as the number of commits, the duration of the project, the number of authors and the number of core developers. Our control model included the entire dataset, while a second filtered model considered only projects with ten or more authors. The results indicated that while certain characteristics of the repository consistently predict popularity, the filtering process significantly alters the relationships between these characteristics and the response. We found that the number of commits exhibited a positive correlation with popularity in the control sample but showed a negative correlation in the filtered sample. These findings highlight the potential biases introduced by data filtering and emphasize the need for careful sample selection in empirical research of mining software repositories. We recommend that empirical work should either analyze complete datasets such as World of Code, or employ stratified random sampling from a complete dataset to ensure that filtering is not biasing the results.

Malviya Thakur, Addi↗
DOE OSTI2022

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

This work develops and implements an NMPC control system to facilitate fuel-optimal platooning of Class 8 vehicles over challenging terrain. Prior research has shown that Cooperative Adaptive Cruise Control (CACC), which allows multiple Class 8 vehicles to follow in close succession, can save between 3 and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g. rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of Nonlinear Model Predictive Control (NMPC) with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. Furthermore, this comparison yields the comparative fuel savings and energy efficiency benefit of NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Wave Tank Testing Report for Controls Validation of a Heaving Point Absorber

The core objectives of this project is to improve the power capture of three different wave energy conversion (WEC) devices by more than 50% using an advanced control system and validate the attained improvements using wave tank and full scale testing. In parallel, we will bring along the development of a wave prediction system that is required to enable effective control and test it at full scale. The purposes of this report are to: 1. Plan and document the 1/25th scale device testing at the wave-tank facility; 2. Document the test article, setup and methodology, sensor and instrumentation, mooring, electronics, wiring, and data flow and quality assurance; 3. Communicate the testing results between the associated members; 4. Facilitate reviews that will help to ensure all aspects (risk, safety, testing procedures, etc.); 5. Provide a systematic guide to setting up, executing and decommissioning the experiment.

16 TIDAL AND WAVE POWER↗
NASA NTRS1975

Comparison of flight measurements with predictions from aeroelastic models in the NASA Langley Transonic Dynamics Tunnel

The NASA Langley Transonic Dynamics Tunnel, which has a variable density, Freon-12 (or air) test medium, was designed to study the dynamics and aeroelastic problems of aerospace vehicles. During the operation of this facility there have been various opportunities to compare wind tunnel and flight test results. Some of these opportunities arise from routine flight checks of the prototype, others from carefully designed comparative wind tunnel and flight experiments. A collection of data obtained from various published and unpublished sources is presented. The topics covered are: gust and buffet response, control surface effectiveness, flutter, and active control of aeroelastic effects. Some benefits and shortcomings of Freon-12 as a test medium are also discussed. Although areas of uncertainty are evident and there is a continuing need for improvements in model simulation and testing techniques, the results presented herein indicate that predictions from aeroelastic model tests are, in general, substantiated by full-scale flight tests.

Reed, W. H., III↗