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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Final Project Report CFA-14-6357: A New Paradigm for Understanding Multiphase Ceramic Waste Form Performance

This project fabricated model multiphase ceramic waste forms with processing-controlled microstructures followed by advanced characterization with synchrotron and electron microscopy-based 3D tomography to provide elemental and chemical state-specific information resulting in compositional phase maps of ceramic composites. Details of 3D microstructural features were incorporated into computer-based simulations using durability data for individual constituent phases as inputs in order to predict the performance of multiphase waste forms with varying microstructure and phase connectivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1985

Performance study of a data flow architecture

Teams of scientists studied data flow concepts, static data flow machine architecture, and the VAL language. Each team mapped its application onto the machine and coded it in VAL. The principal findings of the study were: (1) Five of the seven applications used the full power of the target machine. The galactic simulation and multigrid fluid flow teams found that a significantly smaller version of the machine (16 processing elements) would suffice. (2) A number of machine design parameters including processing element (PE) function unit numbers, array memory size and bandwidth, and routing network capability were found to be crucial for optimal machine performance. (3) The study participants readily acquired VAL programming skills. (4) Participants learned that application-based performance evaluation is a sound method of evaluating new computer architectures, even those that are not fully specified. During the course of the study, participants developed models for using computers to solve numerical problems and for evaluating new architectures. These models form the bases for future evaluation studies.

Adams, George↗
DOE OSTI2021

Data processing pipeline for Tianlai experiment

The Tianlai project is a 21cm intensity mapping experiment for detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily aected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline called tlpipe to process the drift scan survey data from the Tianlai experiment. It performs oine data processing tasks such as radio frequency interference (RFI) agging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhnov regularization for m-mode analysis. In this paper we describe the design and implementation of the pipeline and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2021

Evaluation of Global Surface Water Temperature Data Sets for use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Runze Zhang↗
DOE OSTI2018

Sensing and Sampling of Trace Contaminations by a Dexterous Hexrotor UAV at Nuclear Facilities - 18600

Safe and efficient cleanups are a priority for decontamination and decommissioning of nuclear facilities. The contaminations of byproducts and wastes in such nuclear facilities pose a serious problems while decommissioning, to both the operators and public. Sensing these contaminations and cleaning them is an essential part of the safety procedures. The contaminations might range from low-energy to high-energy byproducts. Sensing these byproducts would require contact based or non-contact based sensing, based on its energy levels. Byproducts such as americium, are low-energy which makes it difficult to sense and sample without contact. Therefore, the workers need to physically swab the surfaces to collect the samples of these contaminations. These tasks pose a great danger for the safety of the human workers which includes the risk to reach greater heights, exposure to the radiation and sampling the entire building. A robotic solution is much more desirable in these cases, where the worker can safely handle the contaminations from a safe area. Use of robotic technologies inside these hazardous environments ensures the safety and enhances the performance. The more suitable robotic solution for reaching great heights and covering large areas of nuclear facilities, are aerial vehicles. Multi-rotor unmanned aerial vehicle (UAV) are chosen for their ability to hover at a position and also fly around in confined spaces. Quadrotors are a common choice of multi-rotor UAVs both research and industry because of its ease to fly and commercial availability. For the applications during the cleanups we need the UAV to not only hover and fly in confined spaces, but also interact with the physical world. Despite of quadrotor's advantages, they are not a best suit as they can't interact with the physical world. The tasks involving physical interactions would require the UAV to apply arbitrary forces and torques in all six degrees of freedom (DOF). As quadrotors are under-actuated and non-holonomic in motion, they cannot exert all the required forces. At Collaborative Robotics Lab, Purdue University, we developed a novel multi-rotor UAV solution, Dexterous Hexrotor, which can exert arbitrary forces and torques in all six DOF, independently and instantaneously. This enables the UAV platform to quickly respond to the external disturbances and precisely hold its position during the mission. The Dexterous Hexrotor serves as a robotic tool for the workers to perform sensing and sampling during the cleanups. Workers can operate Dexterous Hexrotor manually and/or semi-autonomously with human intervention. Manual control requires the worker to fly the Dexterous Hexrotor using a remote control. So the workers should possess knowledge on flying the UAV and to perform physical interaction. A semiautonomous Dexterous Hexrotor requires human input to some extent and requires very less knowledge of flying. The autonomy is designed to operate in flight mode and sampling mode. In flight mode, Dexterous Hexrotor takes off from a 'safe' area, reaches the desired altitude, and fly autonomously towards a sampling point. This sampling point is the target position specified on the 2D reference map. This mode also includes navigation towards the sampling point, avoiding the obstacles in the constrained environment. After reaching a stopping point, Dexterous Hexrotor waits for the human input for a target location to swab and collect samples. The Dexterous Hexrotor is now in sampling mode and waits for the human input. The human operator selects the swabbing location through the live streaming video from onboard camera. Through visual servoing, aided by the map, the UAV will move closer towards the target position. It then transitions to the impact control scheme to perform physical interaction with the wall and collects samples. Once the sample is collected, it autonomously returns back to the takeoff position in 'safe' area for the analysis by mass spectrometry. In this paper, we majorly focus on the physical interaction strategy and the required components which enables the autonomy. Decommissioning of nuclear facilities requires decontamination inside the entire buildings. Decontamination of nuclear facilities involves sensing and sampling the huge buildings, long shafts, etc. Sampling the entire facility is not possible due to the limitation in Dexterous Hexrotor's flight time. But this can be achieved by using a swarm of Dexterous Hexrotors to cover larger areas. The onboard robotic arm can be designed based on the applications such as cleaning, applying sealants, etc. The modularity of the design allows us to switch components as per the requirements of the task. These kind of robotic solutions functioning as tools for the workers, aims to make the work environment safer and playful. (authors)

42 ENGINEERING↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗
DOE OSTI2023

A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks

This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.

Wang, Hongsheng↗
NASA NTRS1993

Simulations of diffusion-reaction equations with implications to turbulent combustion modeling

An enhanced diffusion-reaction reaction system (DRS) is proposed as a statistical model for the evolution of multiple scalars undergoing mixing and reaction in an isotropic turbulence field. The DRS model is close enough to the scalar equations in a reacting flow that other statistical models of turbulent mixing that decouple the velocity field from scalar mixing and reaction (e.g. mapping closure model, assumed-pdf models) cannot distinguish the model equations from the original equations. Numerical simulations of DRS are performed for three scalars evolving from non-premixed initial conditions. A simple one-step reversible reaction is considered. The data from the simulations are used (1) to study the effect of chemical conversion on the evolution of scalar statistics, and (2) to evaluate other models (mapping-closure model, assumed multivariate beta-pdf model).

Girimaji, Sharath S.↗
DOE OSTI2019

Asymmetrical Magnet Shape Optimization Based on S-C Mapping for Torque Profile Mitigation in Unidirectional Application of SPMS Machine

Surface-mounted Permanent Magnet Synchronous (SPMS) machines are widely used in applications that require low torque ripple, reduced vibration and acoustic noise. For those applications of SPMSM in which only unidirectional operation is needed, asymmetric design optimization show to have significant advantages over symmetric designs. In this paper, an analytical method based on Schwarz -Christoffel mapping is presented for asymmetrical magnet shape optimization in SPMS machine. In this method, permanent magnet is divided into finite cells so that each cell can be assigned with either air (OFF) or magnet (ON). The Optimization problem is solved using Genetic Algorithms (GAs). In order to remove non-manufacturable shapes, the cluster of materials and cleaning method is used and applied into magnet cells. The analytical method can significantly reduce the computation time used by optimization process as compared with methods based on Finite Element Analysis (FEA). This method is applied to a 12-pole SPMS machine. Validation of the S-C mapping method and final results are carried out using 2D and 3D finite element analysis. Here, the results obtained demonstrate that asymmetrical magnet shape improves cogging torque and torque ripple performance of SPMS machine by a significant margin.

42 ENGINEERING↗
DOE OSTI2025

Spatial Mapping of Chiral-Induced Spin Selectivity in Chiral Perovskite via Spin-Schottky Junction

Chiral halide perovskite (c-HP) semiconductors exhibit on average a large chiral-induced spin selectivity (CISS) effect. Nevertheless, the microscopic details of CISS and its integration in opto-spintronic constructs remain nascent. Reliable reporting of CISS performance characteristics represents a significant challenge in providing the necessary design rules. We show a Kelvin probe force microscopy (KPFM) method that can quantitatively evaluate and spatially map the chirality-dependent surface contact potential difference resulting from the formation of a spin-Schottky junction. We revealed inhomogeneity in the CISS response, where low-CISS regions in the c-HP films reduce the overall macroscopic average, likely serving as a key factor in optimizing macroscopic performance. We also observed that although c-HP films made from higher precursor concentrations lead to thicker films and higher carrier concentrations with subsequent larger barrier heights in the Schottky junction, stronger spin relaxation due to non-ideal film quality reduces spin polarization.

14 SOLAR ENERGY↗
NASA NTRS2010

Performance Sensitivity Studies on the PIAA Implementation of the High-Contrast Imaging Testbed

This slide presentation reviews the sensitivity studies on the Phase-Induced Amplitude Apodization (PIAA), or pupil mapping using the High-Contrast Imaging Testbed (HCIT). PIAA is a promising technique in high-dynamic range stellar coronagraph. This presentation reports on the investigation of the effects of the phase and rigid-body errors of various optics on the narrowband contrast performance of the PIAA/HCIT hybrid system. The results have shown that the 2-step wavefront control method utilizing 2-DMs is quite effective in compensating the effects of realistic phase and rigid-body errors of various optics

Phase-Induced Amplitude Apodization (PIAA)↗
DOE OSTI2018

Parallel accelerated vector similarity calculations for genomics applications

The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this study we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Finally, results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 10 15 ) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques applied to calculations of the Custom Correlation Coefficient for comparative genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Elevated temperature transmission Kikuchi diffraction in the SEM

Highlights: • The potential of high temperature in-situ TKD characterization is demonstrated. • High quality Kikuchi patterns can be obtained at high temperature. • The very local heat of the MEMS-based heating system minimises infrared radiation. • TKD is a suitable technique for in-situ studies of solid state dewetting. - Abstract: Transmission Kikuchi diffraction (TKD) facilitates automated orientation mapping of thin films in scanning electron microscopes (SEM). In this study TKD is applied for the first time to perform in-situ annealing experiments on gold thin films deposited on a MEMS-based heating system. The very local heating associated with this system enables reliable TKD measurements at elevated temperatures without notable disturbance from infrared radiation. The dewetting of an Au thin film into Au nanoparticles upon heating is followed with orientation mapping in a temperature range between 20 °C and 900 °C. The local thickness variation associated with the dewetting is observed qualitatively by observing the intensity of the transmitted beam, which decreases as the film thickness increases locally. The results of this study reveal that TKD is a well suited technique to study thin-film stability and solid state dewetting. Moreover, the outcome of this methodological study provides a baseline for further in-situ crystallographic studies of electron transparent samples in the SEM.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2002

Application of Modern Design of Experiments to CARS Thermometry in a Model Scramjet Engine

We have applied formal experiment design and analysis to optimize the measurement of temperature in a supersonic combustor at NASA Langley Research Center. We used the coherent anti-Stokes Raman spectroscopy (CARS) technique to map the temperature distribution in the flowfield downstream of an 1160 K, Mach 2 freestream into which supersonic hydrogen fuel is injected at an angle of 30 degrees. CARS thermometry is inherently a single-point measurement technique; it was used to map thc flow by translating the measurement volume through the flowfield. The method known as "Modern Design of Experiments" (MDOE) was used to estimate the data volume required, design the test matrix, perform the experiment and analyze the resulting data. MDOE allowed us to match the volume of data acquired to the precision requirements of the customer. Furthermore, one aspect of MDOE, known as response surface methodology, allowed us to develop precise maps of the flowfield temperature, allowing interpolation between measurement points. An analytic function in two spatial variables was fit to the data from a single measurement plane. Fitting with a Cosine Series Bivariate Function allowed the mean temperature to be mapped with 95% confidence interval half-widths of +/- 30 Kelvin, comfortably meeting the confidence of +/- 50 Kelvin specified prior to performing the experiments. We estimate that applying MDOE to the present experiment saved a factor of 5 in data volume acquired, compared to experiments executed in the traditional manner. Furthermore, the precision requirements could have been met with less than half the data acquired.

Danehy, P. M.↗
NASA NTRS1990

Satellite-derived reflectance of snow-covered surfaces in northern Minnesota

The reflectance of snow-covered surfaces in Minnesota is analyzed using Landsat-5 Thematic Mapper satellite data. Calculations are performed for satellite-derived reflectances integrated over the spectral region (0.45-0.9 micron). Corrections are applied for atmospheric effects and integrated reflectances (R1) are compared over agricultural and forested areas and over a lake using TM scenes acquired in November 1984 and January 1985. Integrated reflectances are then mapped and inter- and intra-scene comparisons of surface reflectance are compared. Temporal analysis of reflectance changes can then be performed rapidly and efficiently using color-coded images. It is noted that the average R1 within the November 1984 subscene was 0.429 + or - 0.176, whereas R1 within the January 1985 subscene was 0.669 + or - 0.236.

Hall, D. K.↗
NASA NTRS2011

A Super-Resolution Algorithm for Enhancement of FLASH LIDAR data

A novel method for enhancement of the spatial resolution of 3-dimensional Flash Lidar images is being proposed for generation of elevation maps of terrain from a moving platform. NASA recognizes the Flash LIDAR technology as an important tool for enabling safe and precision landing in future unmanned and crewed lunar and planetary missions. The ability of the Flash LIDAR to generate 3-dimensional maps of the landing site area during the final stages of the descent phase for detection of hazardous terrain features such as craters, rocks, and steep slopes is under study in the frame of the Autonomous Landing and Hazard Avoidance (ALHAT) project. Since single frames of existing FLASH LIDAR systems are not sufficient to build a map of entire landing site with acceptable spatial resolution and precision, a super-resolution approach utilizing multiple frames has been developed to overcome the instrument s limitations. Performance of the super-resolution algorithm has been analyzed through a series of simulation runs obtained from a high fidelity Flash LIDAR model and a high resolution synthetic lunar elevation map. For each simulation run, a sequence of FLASH LIDAR frames are recorded and processed as the spacecraft descends toward the landing site. Simulations runs having different trajectory profiles and varying LIDAR look angles of the terrain are also analyzed. The results show that adequate levels of accuracy and precision are achieved for detecting hazardous terrain features and identifying safe areas of the landing site.

Bulyshev, Alexander↗