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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Development of Quality Assessment Techniques for Large Eddy Simulation of Propulsion and Power Systems in Complex Geometries

Large-eddy-simulation (LES) is quickly becoming a method of choice for studying complex thermo-physics in a wide range of propulsion and power systems. It provides a means to study coupled turbulent combustion and flow processes in parameter spaces that are unattainable using direct-numerical-simulation (DNS), with a degree of fidelity that can be far more accurate than conventional engineering methods such as the Reynolds-averaged Navier-Stokes (RANS) approximation. However, development of predictive LES is complicated by the complex interdependence of different type of errors coming from numerical methods, algorithms, models and boundary con- ditions. On the other hand, control of accuracy has become a critical aspect in the development of predictive LES for design. The objective of this project is to create a framework of metrics aimed at quantifying the quality and accuracy of state-of-the-art LES in a manner that addresses the myriad of competing interdependencies. In a typical simulation cycle, only 20% of the computational time is actually usable. The rest is spent in case preparation, assessment, and validation, because of the lack of guidelines. This work increases confidence in the accuracy of a given solution while minimizing the time obtaining the solution. The approach facilitates control of the tradeoffs between cost, accuracy, and uncertainties as a function of fidelity and methods employed. The analysis is coupled with advanced Uncertainty Quantification techniques employed to estimate confidence in model predictions and calibrate model's parameters. This work has provided positive consequences on the accuracy of the results delivered by LES and will soon have a broad impact on research supported both by the DOE and elsewhere.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Multiscale analysis of stamp forming of a woven composite

The stamp forming of a woven composite with thermoplastic matrix was investigated in this study. A Mesoscale finite element model of a unit cell of a 2 x 2 twill weave of pre-impregnated tows was used to estimate the effective in-plane shear properties for modeling forming processes and predicting formability. In-plane shear stiffness was shown to be a controlling factor in the formability of unconsolidated textile sheets. The Stamp forming of a cone-shaped part was modeled for comparison with the experiments. Results show that the simplified process modeling scheme has the potential to predict issues with wrinkling during die closure. Furthermore, the present work suggests that complex representations of the relatively small fiber direction strains may be unwarranted for this type of process modeling.

Garnich, Mark R.↗
NASA NTRS2014

Cabin Environment Physics Risk Model

This paper presents a Cabin Environment Physics Risk (CEPR) model that predicts the time for an initial failure of Environmental Control and Life Support System (ECLSS) functionality to propagate into a hazardous environment and trigger a loss-of-crew (LOC) event. This physics-of failure model allows a probabilistic risk assessment of a crewed spacecraft to account for the cabin environment, which can serve as a buffer to protect the crew during an abort from orbit and ultimately enable a safe return. The results of the CEPR model replace the assumption that failure of the crew critical ECLSS functionality causes LOC instantly, and provide a more accurate representation of the spacecraft's risk posture. The instant-LOC assumption is shown to be excessively conservative and, moreover, can impact the relative risk drivers identified for the spacecraft. This, in turn, could lead the design team to allocate mass for equipment to reduce overly conservative risk estimates in a suboptimal configuration, which inherently increases the overall risk to the crew. For example, available mass could be poorly used to add redundant ECLSS components that have a negligible benefit but appear to make the vehicle safer due to poor assumptions about the propagation time of ECLSS failures.

Risk↗
DOE OSTI2016

Role of Defects in Swelling and Creep of Irradiated SiC

Silicon carbide is a promising cladding material because of its high strength and relatively good corrosion resistance. However, SiC is brittle and therefore SiC-based components need to be carefully designed to avoid cracking and failure by fracture. In design of SiC-based composites for nuclear reactor applications it is essential to take into account how mechanical properties are affected by radiation and temperature, or in other words, what strains and stresses develop in this material due to environmental conditions. While thermal strains in SiC can be predicted using classical theories, radiation-induced strains are much less understood. In particular, it is critical to correctly account for radiation swelling and radiation creep, which contribute significantly to dimensional instability of SiC under radiation. Swelling typically increases logarithmically with radiation dose and saturates at relatively low doses (damage levels of a few dpa). Consequently, swelling-induced stresses are likely to develop within a few months of operation of a reactor. Radiation-induced volume swelling in SiC can be as high as 2%, which is significantly higher than the cracking strain of 0.1% in SiC. Swelling-induced strains will lead to enormous stresses and fracture, unless these stresses can be relaxed via some other mechanism. An effective way to achieve stress relaxation is via radiation creep. Although it has been hypothesized that both radiation swelling and radiation creep are driven by formation of defect clusters, existing models for swelling and creep in SiC are limited by the lack of understanding of specific defects that form due to radiation in the range of temperatures relevant to fuel cladding in light water reactors (LWRs) (<1000°C). For example, defects that can be detected with traditional transmission electron microscopy (TEM) techniques account only for 10-45% of the swelling measured in irradiated SiC. Here, we have undertaken an integrated experimental and modeling effort to discover the previously invisible defects in irradiated SiC and to determine the contributions of these defects to radiation swelling. Knowledge of the most stable defect structures and the rate controlling processes during defect evolution is essential for development of predictive models for swelling and creep as a function of temperature and radiation dose. This research has been enabled by state-of-the-art imaging techniques, such as the aberration corrected scanning transmission electron microscopy (STEM) (FEI TITAN) closely coupled with multi-scale models of stable defect clusters and their evolution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training

In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread- level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run opera- tions to improve hardware utilization and system throughput. Our runtime system demonstrates a significant performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 36% performance (execution time) im- provement on average (up to 49%) for four neural network models, and achieves high performance close to the optimal one manually obtained by the user.

Liu, Jiawen↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Advanced dc motor controller for battery-powered electric vehicles

A motor generation set is connected to run from the dc source and generate a voltage in the traction motor armature circuit that normally opposes the source voltage. The functional feasibility of the concept is demonstrated with tests on a Proof of Principle System. An analog computer simulation is developed, validated with the results of the tests, applied to predict the performance of a full scale Functional Model dc Controller. The results indicate high efficiencies over wide operating ranges and exceptional recovery of regenerated energy. The new machine integrates both motor and generator on a single two bearing shaft. The control strategy produces a controlled bidirectional plus or minus 48 volts dc output from the generator permitting full control of a 96 volt dc traction motor from a 48 volt battery, was designed to control a 20 hp traction motor. The controller weighs 63.5 kg (140 lb.) and has a peak efficiency of 90% in random driving modes and 96% during the SAE J 227a/D driving cycle.

Belsterling, C. A.↗
NASA NTRS1992

Return flux experiment

All spacecraft emit molecules via outgassing, thruster plumes, vents, etc. The return flux is the portion of those molecules that scatter from the ambient atmosphere and return to the spacecraft. Return flux allows critical spacecraft surfaces to become contaminated even when there is no direct line of sight between the contamination source and the critical surface. Data from the Long Duration Exposure Facility (LDEF) show that contamination of LDEF surfaces could not have come entirely from direct flux. The data suggest significant return flux. Several computer models have been developed to simulate return flux, but the predictions have never been verified in orbit. Large uncertainties in predictions lead to overly conservative spacecraft designs. The purpose of the REturn FLux EXperiment (REFLEX) is to fly a controlled experiment that can be directly compared with predictions from several models.

Tveekrem, June L.↗
DOE OSTI2023

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗
DOE OSTI2014

Portal dosimetry for VMAT using integrated images obtained during treatment

Purpose: Portal dosimetry provides an accurate and convenient means of verifying dose delivered to the patient. A simple method for carrying out portal dosimetry for volumetric modulated arc therapy (VMAT) is described, together with phantom measurements demonstrating the validity of the approach. Methods: Portal images were predicted by projecting dose in the isocentric plane through to the portal image plane, with exponential attenuation and convolution with a double-Gaussian scatter function. Appropriate parameters for the projection were selected by fitting the calculation model to portal images measured on an iViewGT portal imager (Elekta AB, Stockholm, Sweden) for a variety of phantom thicknesses and field sizes. This model was then used to predict the portal image resulting from each control point of a VMAT arc. Finally, all these control point images were summed to predict the overall integrated portal image for the whole arc. The calculated and measured integrated portal images were compared for three lung and three esophagus plans delivered to a thorax phantom, and three prostate plans delivered to a homogeneous phantom, using a gamma index for 3% and 3 mm. A 0.6 cm{sup 3} ionization chamber was used to verify the planned isocentric dose. The sensitivity of this method to errors in monitor units, field shaping, gantry angle, and phantom position was also evaluated by means of computer simulations. Results: The calculation model for portal dose prediction was able to accurately compute the portal images due to simple square fields delivered to solid water phantoms. The integrated images of VMAT treatments delivered to phantoms were also correctly predicted by the method. The proportion of the images with a gamma index of less than unity was 93.7% ± 3.0% (1SD) and the difference between isocenter dose calculated by the planning system and measured by the ionization chamber was 0.8% ± 1.0%. The method was highly sensitive to errors in monitor units and field shape, but less sensitive to errors in gantry angle or phantom position. Conclusions: This method of predicting integrated portal images provides a convenient means of verifying dose delivered using VMAT, with minimal image acquisition and data processing requirements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Vertical control of DIII-D discharges with strong negative triangularity

Through predictive modeling validated by a series of experiments on DIII-D, the vertical stability of low β diverted plasmas with strong negative triangularity (NT) ($\delta\sim-0.6$) is assessed. As a result of their unique magnetic geometry, NT plasmas feature larger Shafranov shifts and more elongated inner flux surfaces than positive triangularity counterparts, typically leading to enhanced vertical instability growth rates. However, coupling with the non-conformal vessel DIII-D wall reduces these growth rates to controllable values, providing a path forward for stabilizing strongly NT plasma with a diverted geometry. These discharges are used to validate GSdesign (part of the TokSys code suite) stability calculations in strong NT plasmas on DIII-D, with errors of no more than ~20% observed between modeled and experimentally measured growth rates. Additions of diagnostic noise and power supply tuning to the TokSys model are needed to accurately capture the time dependence of DIII-D NT discharges, assisting with the design of control schemes specific to the DIII-D poloidal field coils. Lastly, implications of these results on a future NT reactor are briefly described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Prediction of non-intuitive metabolic targets with bayesian metabolic control analysis to improve 3-hydroxypropionic acid production in Aspergillus niger

Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

3-hydroxypropionic acid↗
NASA NTRS2018

Predicting the Operational Acceptance of Airborne Flight Reroute Requests Using Data Mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.

Operational Acceptability↗
NASA NTRS2012

Prediction of BVI Noise for an Active Twist Rotor Using a Loosely Coupled CFD/CSD Method and Comparison to Experimental Data

Numerical predictions of the acoustic characteristics of an Active Twist Rotor (ATR), using two methods to compute the rotor blade aerodynamics and elastic blade motion are compared to experimental data from a wind tunnel test in the NASA Langley Transonic Dynamics Tunnel (TDT) in 2000. The first method, a loosely coupled iterative method, utilizes the Computational Fluid Dynamics (CFD) code OVERFLOW 2 and the Computational Structural Dynamics (CSD) code CAMRAD II. The second method utilizes the CAMRAD II free-wake model only. The harmonic active-twist control to the main rotor blade system is identified with three parameters - harmonic actuation frequency, actuation amplitude, and control phase angle. The resulting aerodynamics and blade motion data from the two methods are then used in the acoustics code PSU-WOPWOP to predict acoustic pressure on a spherical array of equally spaced observers surrounding the rotor. This spherical distribution of pressure is used to compute the sound power level representing baseline and actuated conditions. Sound power levels for three categories of noise are defined as - blade-vortex interaction sound power level (BVIPWL), low frequency sound power level (LFPWL), and overall sound power level, OAPWL. Comparisons with measured data indicate the CFD/CSD analysis successfully captures the trends in sound power levels and the effects of active-twist control at advance ratios of 0.14 and 0.17. The free-wake model predictions show inconsistent sound power levels relative to the trends in the experimental and CFD data. This paper presents the first ever comparison between CFD/CSD acoustic predictions for an active-twist rotor and experimental measurements.

Fogarty, David E.↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2015

Experiments and Model for Serration Statistics in Low-Entropy, Medium-Entropy, and High-Entropy Alloys

High-entropy alloys (HEAs) are new alloys that contain five or more elements in roughly equal proportion. We present new experiments and theory on the deformation behavior of HEAs under slow stretching (straining), and observe differences, compared to conventional alloys with fewer elements. For a specific range of temperatures and strain-rates, HEAs deform in a jerky way, with sudden slips that make it difficult to precisely control the deformation. An analytic model explains these slips as avalanches of slipping weak spots and predicts the observed slip statistics, stress-strain curves, and their dependence on temperature, strain-rate, and material composition. The ratio of the weak spots’ healing rate to the strain-rate is the main tuning parameter, reminiscent of the Portevin-LeChatellier effect and time-temperature superposition in polymers. Our model predictions agree with the experimental results. The proposed widely-applicable deformation mechanism is useful for deformation control and alloys design.

36 MATERIALS SCIENCE↗
NASA NTRS1993

LDEF microenvironments, observed and predicted

A computer model for prediction of atomic oxygen exposure of spacecraft in low earth orbit, referred to as the primary atomic oxygen model, was originally described at the First Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium. The primary atomic oxygen model accounts for variations in orbit parameters, the condition of the atmosphere, and for the orientation of exposed surfaces relative to the direction of spacecraft motion. The use of the primary atomic oxygen model to define average atomic oxygen exposure conditions for a spacecraft is discussed and a second microenvironments computer model is described that accounts for shadowing and scattering of atomic oxygen by complex surface protrusions and indentations. Comparisons of observed and predicted erosion of fluorinated ethylene propylene (FEP) thermal control blankets using the models are presented. Experimental and theoretical results are in excellent agreement. Work is in progress to expand modeling capability to include ultraviolet radiation exposure and to obtain more detailed information on reflecting and scattering characteristics of material surfaces.

Bourassa, R. J.↗
DOE OSTI2017

Field Test of Wake Steering at an Offshore Wind Farm

In this paper, a field test of wake steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, SOWFA, for understanding wake dynamics and an engineering model, FLORIS, for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.

17 WIND ENERGY↗