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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Observation of Upper and Middle Tropospheric Clouds

The goal of this research has been to identify and describe the properties of climatically important cloud systems critically important to understanding their effects upon satellite remote sensing and the global climate. These goals have been pursued along several different but complementary lines of investigation: the design, construction, testing and application of instrumentation; the collection of data sets during Intensive Field Observation periods; the reduction and analysis of data collected during IFO's; and completion of research projects specifically designed to address important and timely research objectives. In the first year covered by this research proposal, three papers were authored in the refereed literature which reported completed analyses of FIRE 1 IFO studies initiated under the previous NASA funding of this topic area. microphysical and radiative properties of marine stratocumulus cloud systems deduced from tethered balloon observations were reported from the San Nicolas Island site of the first FIRE marine stratocumulus experiment. Likewise, in situ observations of radiation and dynamic properties of a cirrus cloud layer were reported from first FIRE cirrus IFO based from Madison, Wisconsin. In addition, application techniques were under development for monitoring cirrus cloud systems using a 403 MHz Doppler wind profiler system adapted with a RASS (Radio Acoustic Sounding System) and an infrared interferometer system; these instrument systems were used in subsequent deployments for the FIRE 2 Parsons, Kansas and FIRE 2 Porto Santo, ASTEX expeditions. In November 1991 and in June 1992, these two systems along with a complete complement of surface radiation and meteorology measurements were deployed to the two sites noted above as anchor points for the respective IFO'S. Subsequent research activity concentrated on the interpretation and integration of the IFO analyses in the context of the radiative properties of cloud systems and our ability to remotely observe radiative, thermodynamic and dynamic properties of these cloud systems.

Cox, Stephen K.↗
DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗
DOE OSTI2019

Investigation of Computational Corrections to X-Ray Computed Tomography for Dimensional Metrology of Complex Additively Manufactured Parts

X-ray computed tomography (CT) is a nondestructive evaluation technique that may be used to image complex internal features without the line-of-sight restrictions inherent in conventional optical and contact (stylus) metrology instrumentation. Applications of CT metrology include material defects (porosity), dimensions and surface texture. However, scan artifacts, such as streaking, caused primarily by beam hardening and scatter, limit the accuracy of CTbased metrology. Reduction of these artifacts would improve the accuracy material properties and dimensional metrology information. This paper reports on the development of additively manufactured (AM) Ti5553 and 316L measurement phantoms with integral lattice (lattice) structures specifically designed to exhibit these artifacts. The phantoms were scanned in three CT systems and a scanning-probe CMM measurement. Surfaces generated from uncorrected CT data exhibited “dimple” defects that are not present in the CMM measurement. Correction methods were developed in Livermore Tomography Tools (LTT) software and this paper reports on the successful correction of these artifacts. This work is expected to have application in areas where characterization of defects, dimensions and surface texture using CT are critical.

42 ENGINEERING↗
NASA NTRS1992

Overview for the reanalysis of Mariner 9 UV spectrometer data for ozone, cloud, and dust abundances, and their interaction over climate timescales

Mariner 9 UV spectrometer data were reinverted for the ozone abundance, cloud abundance, dust abundance, and polar-cap albedo. The original reduction of the spectra ignored the presence of atmospheric dust and clouds, even though their abundance is substantial and can mask appreciable amounts of ozone if not accounted for (Lindner, 1988). The Mariner 9 ozone data has been used as a benchmark in all theoretical models of atmospheric composition, escape, and photochemistry. A second objective is to examine the data for the interrelationship of the ozone cycle, dust cycle, and cloud cycle, on an annual, inter-annual, and climatic basis, testing predictions by Lindner (1988). This also has implications for many terrestrial ozone studies, such as the ozone hole, acid rain, and ozone-smog. A third objective is to evaluate the efficacy of the reflectance spectroscopy technique at retrieving the ozone abundance on Mars. This would be useful for planning ozone observations on future Mars missions or the terrestrial troposphere.

Lindner, Bernhard Lee↗
NASA NTRS1992

Forward-scatter radiant mapping

Forward-scatter systems have been much neglected for the study of meteors and meteor streams. A great deal of this neglect stems from the complicated geometry which has made the interpretation of results difficult in the past. This no longer presents a problem because of the computer power now available. There are practical advantages in using forward-scatter in that low-power transmitters are much easier to handle than the high-power ones used in pulsed back-scatter radars. The data reduction of the CW signals is also significantly simpler. Because the forward-scatter reflection geometry increases the duration of the echoes relative to the back-scatter case, the problem of the underdense ceiling is partially alleviated. We have built a 'short hop' forward-scatter system between Ottawa and London (Ont) for which the transmitter and receiver are separated by about 500 km. With it, we are able to measure unambiguously the directions of arrival of the echoes using a 5-antenna interferometer. Morton and Jones (1982, MN, 198, 737) have shown how the echo direction distribution can be deconvolved to yield the meteor radiant distribution for back-scatter data. We have extended the technique to the forward-scatter case and present some preliminary meteor radiant distribution maps.

Jones, James↗
NASA NTRS2020

Modeling Whitecaps on Global Scale

Whitecaps play an important role in the surface-atmosphere interactions across the ocean. They are directly linked to the energy dissipation rate during wave breaking and transfer of heat, momentum, and gas/aerosol exchange from the wind to the sea surface. Although the first models of W were dependent only on wind speeds, a large number of diverse models based on wind and sea state which include wave height, wave age, friction velocity, and stability effects have been proposed since then. However, it is recognized that most of the proposed W models have strong systematic (e.g., zonal bias) and random errors when compared against observations. This is partly due to the differences in environmental conditions, measurement techniques, and geographical locations among these studies. But, some of these biases are linked to the inability of the proposed models to capture the variability in W in certain wind/wave regimes. Despite the knowledge of existing biases, W residual relationships from the models with wind and wave fields remain highly uncertain, with residual trends varying between the published studies. Here, we take advantage of the availability of relatively dense observations of W from WindSat microwave satellite retrievals in combination with the University of Miami wave model which was recently incorporated within the NASA GMAO/GEOS system (GEOS-UMWM). We use Windsat W retrievals to assess and constrain the previously published W models and understand the relationships of residuals from models in different wind/wave regimes. We link these unexplained residual variations to additional factors such as swell index, drag coefficient etc and add information to the existing whitecap models. Since Windsat retrievals cover wide range of environmental conditions, it helps to reduce the uncertainties associated with differences in measurement techniques. Regression of wind-wave fields against all Windsat data points (CTL) results in larger residuals for lower wave age and W is overestimated upto ~4% for wave age < 10 and underestimated by upto ~2% as wave age increases. We attest to this bias by considering two approaches. One is to perform regression separately for different stages of wave development such as developing sea, fully developed, and wind sea regimes thereby understanding the sensitivity of regression coefficients to sea state (EXP1). Another is to derive coefficients of W models in EXP1 as a function of additional wind/wave factors such as swell index, drag coefficient, and mean squared slope, deriving more nonlinear W models (EXP2). EXP2 provides reduction in Root Mean Squared Error (RMSE) by 0.1-0.3%. Sea surface drag has a stronger relationship with regression coefficients compared to swell index.These additional factors provide improved parameterizations in different wind and wave age regimes, with smaller unexplained/residual variations in W that has been a major concern in the W community.

Raman, Aishwarya↗
DOE OSTI2023

Ringing Artifact Reduction Method for Ultrasound Reconstruction Using Multi-Agent Consensus Equilibrium

Non-destructive characterization of multi-layered structures that can be accessed from only a single side is important for applications such as well-bore integrity inspection. Existing methods related to Synthetic Aperture Focusing Technique (SAFT) rapidly produce acceptable results but with significant artifacts. Recently, ultrasound model-based iterative reconstruction (UMBIR) approaches have shown significant improvements over SAFT. However, even these methods produce ringing artifacts due to the high fractional-bandwidth of the excitation signal.In this paper, we propose a ringing artifact reduction method for ultrasound image reconstruction that uses a multi-agent consensus equilibrium (RARE-MACE) framework. Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data. We test our method on simulated and experimental measurements and show substantial improvements in image quality compared to SAFT and UMBIR.

Alanazi, Abdulrahman M↗
DOE OSTI2020

An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties

The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the industry is recording near-zero average energy prediction bias. Herein, we present an overview of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including the improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight the opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty, and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary on how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the manuscript.

literature review↗
NASA NTRS1989

Aeroservoelastic modeling and applications using minimum-state approximations of the unsteady aerodynamics

Various control analysis, design, and simulation techniques for aeroelastic applications require the equations of motion to be cast in a linear time-invariant state-space form. Unsteady aerodynamics forces have to be approximated as rational functions of the Laplace variable in order to put them in this framework. For the minimum-state method, the number of denominator roots in the rational approximation. Results are shown of applying various approximation enhancements (including optimization, frequency dependent weighting of the tabular data, and constraint selection) with the minimum-state formulation to the active flexible wing wind-tunnel model. The results demonstrate that good models can be developed which have an order of magnitude fewer augmenting aerodynamic equations more than traditional approaches. This reduction facilitates the design of lower order control systems, analysis of control system performance, and near real-time simulation of aeroservoelastic phenomena.

Tiffany, Sherwood H.↗
NASA NTRS1993

Impact damage in filament wound composite bottles

Increasingly, composite materials are being used in advanced structural applications because of the significant weight savings they offer when compared to more traditional engineering materials. The higher cost of composites must be offset by the increased performance that results from reduced structural weight if these new materials are to be used effectively. At present, there is considerable interest in fabricating solid rocket motor cases out of composite materials, and capitalizing on the reduced structural weight to increase rocket performance. However, one of the difficulties that arises when composite materials are used is that composites can develop significant amounts of internal damage during low velocity impacts. Such low velocity impacts may be encountered in routine handling of a structural component like a rocket motor case. The ability to assess the reduction in structural integrity of composite motor cases that experience accidental impacts is essential if composite rocket motor cases are to be certified for manned flight. While experimental studies of the post-impact performance of filament wound composite motor cases haven been proven performed (2,3), scaling impact data from small specimens to full scale structures has proven difficult. If such a scaling methodology is to be achieved, an increased understanding of the damage processes which influence residual strength is required. The study described herein was part of an ongoing investigation of damage development and reduction of tensile strength in filament wound composites subjected to low velocity impacts. The present study, which focused on documenting the damage that develops in filament wound composites as a result of such impacts, included two distinct tasks. The first task was to experimentally assess impact damage in small, filament wound pressure bottles using x-ray radiography. The second task was to study the feasibility of using digital image processing techniques to assist in determining the 3-D distribution of damage from stereo x-ray pairs.

Highsmith, Alton L.↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
DOE OSTI2017

Optimizing the query performance of block index through data analysis and I/O modeling

Indexing technique has become an efficient tool to enable scientists to directly access the most relevant data records. But, the time and space requirements of building and storing indexes are expensive in the traditional approaches, such as R-tree and bitmaps. Recently, we started to address this issue by using the idea of "block index", and our previous work has shown promising results from comparing it against other well-known solutions, including ADIOS, SciDB, and FastBit. In this work, we further improve the technique from both theoretical and implementation perspectives. Driven by an extensive effort in characterizing scientific datasets and modeling I/O systems, we presented a theoretical model to analyze its query performance with respect to a given block size configuration. We also introduced three optimization techniques to achieve a 2.3x query time reduction comparing to the original implementation.

Wu, Tzuhsien↗
DOE OSTI2024

Role of physics in physics-informed machine learning

Physical systems are characterized by inherent symmetries, one of which is encapsulated in the units of their parameters and system states. These symmetries enable a lossless order-reduction, e.g., via dimensional analysis based on the Buckingham theorem. Despite the latter's benefits, machine learning (ML) strategies for the discovery of constitutive laws seldom subject experimental and/or numerical data to dimensional analysis. We demonstrate the potential of dimensional analysis to significantly enhance the interpretability and generalizability of ML-discovered secondary laws. Our numerical experiments with creeping fluid flow past solid ellipsoids show how dimensional analysis enable both deep neural networks and sparse regression reproduce old results, e.g., Stokes law for a sphere, and generate new ones, e.g., an expression for an ellipsoid misaligned with the flow direction. Furthermore, our results suggest the need to incorporate other physics-based symmetries and invariances into ML-based techniques for equation discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Final Technical and Scientific Report: Advanced Control of the Azura Wave Energy Device

The objective of the project was to develop a model predictive controller and associated hydraulic PTO for the Azura wave energy device to improve the annual energy production (AEP) and reduce the levelized cost of electricity (LCOE) of the device. Performance of the controller and hydraulic PTO were validated in a 1/15th scale wave tank test performed at the University of Maine. Three controllers, with three corresponding PTOs, were developed and tested as part of the project. The three controllers are an ideal MPC controller paired with an ideal PTO, an MPC controller with a heuristic paired with a directional force control hydraulic PTO, and a preliminary study of an MPC controller applied to a variable force hydraulic PTO. Experimental data was used to estimate the controller paired with the directional force control would increase the annual energy production of the Azura at WETS by 12%, with a corresponding reduction in LCOE of 10.8%. Preliminary testing of the variable force hydraulic PTO paired with a MPC shows this configuration has the potential to increase the AEP of the Azura at WETS by at least 33%. Further design and development work is necessary to develop the PTO technology to achieve these gains. Additionally, all control schemes require additional work to address the short-term wave prediction method needed to implement the control. In addition to validation of these control methods, NWEI demonstrated careful design of a wave tank test model, PTO, and instrumentation yields high quality test data, and the ability to test advanced control algorithms and include complex PTO dynamics via hardware-in-the-loop testing techniques.

16 TIDAL AND WAVE POWER↗
NASA NTRSDate not supplied

Ray Tracing Techniques for the Characterization of Lunar Communication Architectures

This paper provides an overview of the computational techniques used to characterize the viability of different lunar architectures and their ability to provide communication services to the lunar surface. This analysis was done with modern ray tracing techniques that allow for the computations to be done on Graphics Processing Unit (GPU) clusters for a high level of parallelism and severe reduction in computation time. The ray tracing computations were done with the GPU platform Compute Unified Device Architecture (CUDA) provided by NVIDIA which utilizes general-purpose computing on graphics processing units (GPGPU). This new method provides the advantage of being able to characterize a much larger portion of the lunar surface due to its computational efficiency as well as providing a more accurate representation of elevation angle limits instead of the typical and often inaccurate elevation angle mask. The Lunar surface can now be characterized with metrics such as contact time, outage time, and received data rate. With these metrics, different proposed Lunar architectures can be rapidly evaluated. This reduction in computation time not only leads to more accurate results but allows these results to be obtained in a time frame that allows for the complete characterization of the trade space. It is expected that these different architecture comparisons will lead to a conclusive determination of the optimal Lunar architecture and will allow for future Lunar missions to operate as close to real time as possible. In addition, this computation method can be used to recreate visibility figures generated by previous methods but with an increased level of accuracy.

Thomas Montano↗
DOE OSTI2021

Deep reinforcement learning control of hydraulic fracturing

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. In addition, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.

42 ENGINEERING↗
DOE OSTI2022

Bridging confined phase behavior of CH 4 -CO 2 binary systems across scales

Phase behavior of confined fluids may deviate significantly from that of the bulk fluid due to the fluid-wall interactions being a significant portion of all intermolecular interactions under confinement. Despite recent advancements in understanding confined phase behavior of pure fluids, confined phase behavior of mixtures remains an understudied topic. In this work, we examine the confined phase behavior of a CH 4 -CO 2 binary system by combining Monte Carlo (MC) simulations, a cubic equation of state (EoS), and the lattice Boltzmann method (LBM). First, the effects of confinement on density and phase distribution in nano-size pores are established using Gibbs Ensemble MC calculations, which produce precise results of liquid and vapor confined pressures and account for the modification of the phase change location. By comparing the phase envelopes of bulk and confined mixtures at a fixed temperature, here it is observed that the phase envelopes shrink with reductions in pore size. Based on this observation, we extend a modified Peng-Robinson EoS, which was originally developed for pure fluids under confinement, to mixtures via van-der-Waals-type mixing rules and by accounting for shifts in the critical properties of confined CH 4 -CO 2 . The resulting phase envelopes are in good agreement with the MC data. In addition, a local density model is used in combination with the confined EoS to calculate adsorption isotherms of CH 4 -CO 2 mixtures and to characterize the behavior of confined matter in nanopores. Finally, we incorporate this EoS in a multicomponent multiphase LBM that uses a pseudopotential model to represent intermolecular forces. This workflow utilizes multiscale simulation techniques to bridge the behavior of multicomponent systems across scales and to shed light on the confined phase behavior of CH 4 -CO 2 binary systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗