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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Micromechanics Analysis Code Post-Processing (MACPOST) User Guide

As advanced composite materials have gained wider usage. the need for analytical models and computer codes to predict the thermomechanical deformation response of these materials has increased significantly. Recently, a micromechanics technique called the generalized method of cells (GMC) has been developed, which has the capability to fulfill this -oal. Tc provide a framework for GMC, the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) has been developed. As MAC/GMC has been updated, significant improvements have been made to the post-processing capabilities of the code. Through the MACPOST program, which operates directly within the MSC/PATRAN graphical pre- and post-processing package, a direct link between the analysis capabilities of MAC/GMC and the post-processing capabilities of MSC/PATRAN has been established. MACPOST has simplified the production, printing. and exportation of results for unit cells analyzed by MAC/GMC. MACPOST allows different micro-level quantities to be plotted quickly and easily in contour plots. In addition, meaningful data for X-Y plots can be examined. MACPOST thus serves as an important analysis and visualization tool for the macro- and micro-level data generated by MAC/GMC. This report serves as the user's manual for the MACPOST program.

Goldberg, Robert K.↗
DOE OSTI2023

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.

Geology↗
DOE OSTI2017

Contingency Analysis Post-Processing With Advanced Computing and Visualization

Contingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation caused by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly in order to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their work efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a WECC planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.

Chen, Yousu↗
DOE OSTI2019

l3d2vtk: An MCNPTools Utility to Enable LNK3DNT File Visualization & Post-processing [Slides]

This presentation discusses the objective of the project which is to provide a utility (among other utilities) that improves MCNP analysis post-processing and visualization capabilities. Much recent work has been done to improve MCNP post-processing and visualization. It also discusses the goals of the project, which are to create a product that is portable, open, easily interrogated by a user, no dependencies, including a mesh tally-to-VTK conversion, an EEOUT-to-VTK conversion, and an interactive, 3-D, visualization environments (e.g., ParaView). The presentation also discusses the reasons regarding why this is being done, which are to provide physical insight into behaviors and trends, to facilitate error mitigation (easier to “see” errors than “read” them), and to improve communication with collaborators. It also discusses why this is being done now as analyses are getting ever-more complicated and are more flexible in terms of build, test, and deployment capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1986

Pre- and post-processing for Cosmic/NASTRAN on personal computers and mainframes

An interface between Cosmic/NASTRAN and GIFTS has recently been released, combining the powerful pre- and post-processing capabilities of GIFTS with Cosmic/NASTRAN's analysis capabilities. The interface operates on a wide range of computers, even linking Cosmic/NASTRAN and GIFTS when the two are on different computers. GIFTS offers a wide range of elements for use in model construction, each translated by the interface into the nearest Cosmic/NASTRAN equivalent; and the options of automatic or interactive modelling and loading in GIFTS make pre-processing easy and effective. The interface itself includes the programs GFTCOS, which creates the Cosmic/NASTRAN input deck (and, if desired, control deck) from the GIFTS Unified Data Base, COSGFT, which translates the displacements from the Cosmic/NASTRAN analysis back into GIFTS; and HOSTR, which handles stress computations for a few higher-order elements available in the interface, but not supported by the GIFTS processor STRESS. Finally, the versatile display options in GIFTS post-processing allow the user to examine the analysis results through an especially wide range of capabilities, including such possibilities as creating composite loading cases, plotting in color and animating the analysis.

Kamel, H. A.↗
DOE OSTI2016

SU-G-IeP1-01: A Novel MRI Post-Processing Algorithm for Visualization of the Prostate LDR Brachytherapy Seeds and Calcifications Based On B0 Field Inhomogeneity Correction and Hough Transform

Purpose: This study aims at developing an MRI-only workflow for post-implant dosimetry of the prostate LDR brachytherapy seeds. The specific goal here is to develop a post-processing algorithm to produce positive contrast for the seeds and prostatic calcifications and differentiate between them on MR images. Methods: An agar-based phantom incorporating four dummy seeds (I-125) and five calcifications of different sizes (from sheep cortical bone) was constructed. Seeds were placed arbitrarily in the coronal plane. The phantom was scanned with 3T Philips Achieva MR scanner using an 8-channel head coil array. Multi-echo turbo spin echo (ME-TSE) and multi-echo gradient recalled echo (ME-GRE) sequences were acquired. Due to minimal susceptibility artifacts around seeds, ME-GRE sequence (flip angle=15; TR/TE=20/2.3/2.3; resolution=0.7×0.7×2mm3) was further processed.The induced field inhomogeneity due to the presence of titaniumencapsulated seeds was corrected using a B0 field map. B0 map was calculated using the ME-GRE sequence by calculating the phase difference at two different echo times. Initially, the product of the first echo and B0 map was calculated. The features corresponding to the seeds were then extracted in three steps: 1) the edge pixels were isolated using “Prewitt” operator; 2) the Hough transform was employed to detect ellipses approximately matching the dimensions of the seeds and 3) at the position and orientation of the detected ellipses an ellipse was drawn on the B0-corrected image. Results: The proposed B0-correction process produced positive contrast for the seeds and calcifications. The Hough transform based on Prewitt edge operator successfully identified all the seeds according to their ellipsoidal shape and dimensions in the edge image. Conclusion: The proposed post-processing algorithm successfully visualized the seeds and calcifications with positive contrast and differentiates between them according to their shapes. Further assessments on more realistic phantoms and patient study are required to validate the outcome.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Error analysis of real time and post processed or bit determination of GFO using GPS tracking

The goal of the Navy's GEOSAT Follow-On (GFO) mission is to map the topography of the world's oceans in both real time (operational) and post processed modes. Currently, the best candidate for supplying the required orbit accuracy is the Global Positioning System (GPS). The purpose of this fellowship was to determine the expected orbit accuracy for GFO in both the real time and post-processed modes when using GPS tracking. This report presents the work completed through the ending date of the fellowship.

Schreiner, William S.↗
DOE OSTI2017

Active non-volatile memory post-processing

A computing node includes an active Non-Volatile Random Access Memory (NVRAM) component which includes memory and a sub-processor component. The memory is to store data chunks received from a processor core, the data chunks comprising metadata indicating a type of post-processing to be performed on data within the data chunks. The sub-processor component is to perform post-processing of said data chunks based on said metadata.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TU-A-9A-03: Development and Verification of a Forward Model That Assists in Iterative Post-Processing Algorithms Used to Reduce Blur in Compton Backscatter Imaging Systems

Purpose: In Compton Backscatter Imaging (CBI), the source and detector reside on the same side of the patient. We previously demonstrated the applicability of CBI systems for medical purposes using an industrial system. To assist in post-processing images from a CBI system, a forward model based on radiation absorption and scatter principles has been developed. Methods: The forward model was developed in C++ using raytracing to track particles. The algorithm accepts phantoms of any size and resolution to calculate the fraction of incident photons scattered back to the detector, and can perform these calculations for any detector geometry and source specification. To validate the model, results were compared to MCNP-X, which is a Monte Carlo based simulation software, for various combinations of source specifications, detector geometries, and phantom compositions. Results: The model verified that the backscatter signal to the detector was based on three interaction probabilities: a) attenuation of photons going into the phantom, b) Compton scatter of photons toward the detector, and c) attenuation of photons coming out of the phantom. The results from the MCNP-X simulations and the forward model varied from 1 to 5%. This difference was less than 1% for energies higher than 30 keV, but was up to 4% for lower energies. At 50 keV, the difference was less than 1% for multiple detector widths and for both homogeneous and heterogeneous phantoms. Conclusion: As part of the optimization of a medical CBI system, an efficient and accurate forward model was constructed in C++ to estimate the output of CBI system. The model characterized individual components contributing to CBI output and increased computational efficiency over Monte Carlo simulations. It is now used in the development of novel post-processing algorithms that reduce image blur by reversing undesired contribution from outside the region of interest.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗
DOE OSTI2022

On the optimized 3D printing and post-processing of PETG materials

Glycol-modified polyethylene terephthalate (PETG) is a reliable middle ground between polylactic acid and acrylonitrile butadiene styrene due to its light weight, impact resistance, toughness, flexibility, and cost-effectiveness. However, during 3D printing, the filament form is prone to oozing and stringing. In this study, we optimized the PETG printability via g-code setting and printing parameter modification, and consideration of filament quality, build plate temperature and leveling, and printing design complexity. Printed PETGs were thermally and chemically annealed, and post-processing conditions including annealing time, temperature, and solvent use were investigated. Mechanical properties were evaluated in terms of print parameter changes and post-processing techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Implications of post-processing induced microstructural changes on the deformation and fracture response of additively manufactured Ti–6Al–4V

Ti–6Al–4V parts fabricated via electron beam melting (EBM) powder bed fusion are often subjected to hot isostatic pressing below the β-transus temperature to mitigate defects. During which the pressure aids in pore closure and the thermal exposure results in coarsening of α phase while retaining the columnar prior-β phase grain morphology present in the as-fabricated condition. The same post-processing treatment can also be carried out above β-transus temperature which is an effective way to modify both the grain morphology and the associated α textures. Here, the objective of this work is to correlate thermally induced microstructural changes to the deformation and fracture response of the EBM processed Ti–6Al–4V. To this end, we have carried out both sub-transus and super-transus heat-treatments of the as-fabricated material. The mechanical response of the as-fabricated and all heat-treated materials are characterized by in-situ tensile tests under a high-resolution digital optical microscope. This enabled us to capture large-scale panoramic images of the deforming microstructure, and overcome the trade-off between the image resolution and the field of view during in-situ experiments. The series of images captured throughout the imposed deformation are subsequently used to perform microstructure-based digital image correlation to measure microstructural-scale strains over a large area. The results of the in-situ tests together with detailed fractographic analyses are then used to elucidate how the heterogeneous deformation spanning over multiple microstructural length-scales, for example, at the scale of lamellae, colonies of lamellae and grain boundaries, affect the overall deformation and fracture response of the post-processed materials.

36 MATERIALS SCIENCE↗
NASA NTRS1987

A post-processing system for automated rectification and registration of spaceborne SAR imagery

An automated post-processing system has been developed that interfaces with the raw image output of the operational digital SAR correlator. This system is designed for optimal efficiency by using advanced signal processing hardware and an algorithm that requires no operator interaction, such as the determination of ground control points. The standard output is a geocoded image product (i.e. resampled to a specified map projection). The system is capable of producing multiframe mosaics for large-scale mapping by combining images in both the along-track direction and adjacent cross-track swaths from ascending and descending passes over the same target area. The output products have absolute location uncertainty of less than 50 m and relative distortion (scale factor and skew) of less than 0.1 per cent relative to local variations from the assumed geoid.

Curlander, John C.↗
DOE OSTI2025

Additive manufacturing of high‐density silicon carbide ceramics through post‐processing spark plasma sintering

This research advances the field of additive manufacturing (AM) of silicon carbide (SiC) ceramics by integrating spark plasma sintering (SPS) to enhance material density, mechanical strength, and thermal properties. Traditional AM techniques struggle to achieve the high‐density SiC required for demanding applications, such as aerospace engineering, where high thermal conductivity and mechanical strength are paramount. Here, our study addresses these challenges by incorporating SPS as a post‐processing step, achieving near‐theoretical maximum densities and significantly reducing porosity, thereby resulting in outstanding thermal conductivity in SiC ceramics. We developed a specialized SiC ink optimized for 3D printing, ensuring structural integrity after deposition through tailored rheological properties. The application of SPS facilitates rapid, uniform sintering, essential for attaining superior density, mechanical properties, and thermal performance. Our experimental results, confirmed through scanning electron microscopy analysis, demonstrate significant microstructural properties, mechanical strength, and thermal conductivity, showcasing the effectiveness of integrating SPS in AM processes. This innovative approach not only expands the capabilities of AM in producing complex, high‐density ceramic structures but also broadens the potential applications of SiC in demanding environments.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Laboratory Testbed Verification of Data Post-Processing Strategies for the WFIRST Coronagraph Instrument

One of the objectives of the WFIRST Coronagraph Instrument is to demonstrate post-processing algorithms for space-based exoplanet imaging data in the regime of very high contrast ratios (< 1E-8). We analyzed two 15-hour time series of laboratory images acquired with prototype coronagraph designs on a vacuum chamber testbed at JPL. These data sets enabled us to test the performance of reference differential imaging (RDI) PSF subtraction as a function of time and Euclidean (L2) speckle pattern distance. Starting from an analytical relationship between speckle pattern stability and classical subtraction gain, we compared the classical subtraction result to those of the KLIP RDI algorithm. In both data sets, KLIP RDI contrast gains are typically 3-4× greater than those of classical subtraction. These results suggest that a diverse reference PSF library will enable a significant gain in sensitivity beyond that suggested by the raw, frame-to-frame speckle pattern stability.

Ygouf, Marie↗
DOE OSTI2026

Post-processed surface meteorological, air-sea flux, SST, wave, and ship navigation/position merged data from NOAA Ship Pisces

These are post-processed surface data from the NOAA Ship Pisces, merged from several instruments. They consist of meteorological, ocean, ship navigation/position, and surface air-sea flux quantities calculated at three different time frequencies. Because the averaging of directions can be complex, they are provided as a courtesy. R1 in the filename indicates "revision 1" or version 1. R2 will include surface waves (TBD).

17 WIND ENERGY↗
DOE OSTI2024

Post-processing of phase change material in a zero-change commercial silicon photonic process

Integration of phase change material (PCM) with photonic integrated circuits can transform large-scale photonic systems by providing non-volatile control over phase and amplitude. The next generation of commercial silicon photonic processes can benefit from the addition of PCM to enable ultra-low power, highly reconfigurable, and compact photonic integrated circuits for large-scale applications. Despite all the advantages of PCM-based photonics, today’s commercial foundries do not provide them in their silicon photonic processes yet. We demonstrate the first-ever electrically programmable PCM device that is monolithically post-processed in a commercial foundry silicon photonics process using a few fabrication steps and coarse-resolution photolithography. These devices achieved 1.4 dB/μm of amplitude switching contrast using a thin layer of 12.5 nm GeSbTe in this work. We have also characterized the reconfiguration speed as well as repeatability of these devices over 20,000 switching cycles. Our solution enables non-volatile photonic VLSI systems that can be fabricated at low cost and high reliability in a commercial foundry process, paving the way for the development of non-volatile programmable photonic integrated circuits for a variety of emerging applications.

Optics↗