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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
DOE OSTI2022

QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests

Accurate and reliable near-real-time satellite precipitation estimation is of great importance for operational large-scale flood forecasting and drought monitoring. The state-of-the-art precipitation post-processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P-NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near-real-time predictors to further improve the model performance. Using the Integrated Multi-satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG-E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG-E and is also superior to the bias-corrected product (IMERG Final Run, IMERG-F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG-E, raw IMERG-E, and IMERG-F using ground observation show that the corrected IMERG-E improves correlation coefficients (0.7), mean error (-0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG-E (0.31, -0.72 and 5.5 mm/day) and IMERG-F (0.34, -0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P-NRT improves on the various deficiencies of the raw IMERG-E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post-processing for near-real-time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

54 ENVIRONMENTAL SCIENCES↗
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↗
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↗
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↗
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↗
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↗
DOE OSTI2021

Tailoring Microstructure and Mechanical Properties of Additively-Manufactured Ti6Al4V Using Post Processing

Additively-manufactured Ti-6Al-4V (Ti64) exhibits high strength but in some cases inferior elongation to those of conventionally manufactured materials. Post-processing of additively manufactured Ti64 components is investigated to modify the mechanical properties for specific applications while still utilizing the benefits of the additive manufacturing process. The mechanical properties and fatigue resistance of Ti64 samples made by electron beam melting were tested in the as-built state. Several heat treatments (up to 1000 °C) were performed to study their effect on the microstructure and mechanical properties. Phase content during heating was tested with high reliability by neutron diffraction at Los Alamos National Laboratory. Two different hot isostatic pressings (HIP) cycles were tested, one at low temperature (780 °C), the other is at the standard temperature (920 °C). The results show that lowering the HIP holding temperature retains the fine microstructure (~1% β phase) and the 0.2% proof stress of the as-built samples (1038 MPa), but gives rise to higher elongation (~14%) and better fatigue life. The material subjected to a higher HIP temperature had a coarser microstructure, more residual β phase (~2% difference), displayed slightly lower Vickers hardness (~15 HV10N), 0.2% proof stress (~60 MPa) and ultimate stresses (~40 MPa) than the material HIP’ed at 780 °C, but had superior elongation (~6%) and fatigue resistance. Heat treatment at 1000 °C entirely altered the microstructure (~7% β phase), yield elongation of 13.7% but decrease the 0.2% proof-stress to 927 MPa. The results of the HIP at 780 °C imply it would be beneficial to lower the standard ASTM HIP temperature for Ti6Al4V additively manufactured by electron beam melting.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microscale drivers and mechanisms of fracture in post-processed additively manufactured Ti–6Al– 4 V

Herein, we focus on understanding the microstructure-fracture correlations in a Ti–6Al– 4 V alloy additively manufactured via electron beam melting (EBM) and subjected to various post-process heat-treatments. Specifically, the as fabricated material is subjected to a sub-transus heat-treatment followed by air-cooling and a super-transus heat-treatment followed by either air- or furnace-cooling. Next, a series of in-situ single edge notch tension (SENT) tests are carried out under a high-resolution digital optical microscope. The panoramic high-resolution images captured during the in-situ tests are then used to characterize the planar deformation on the specimen surface using microstructure-based digital image correlation (DIC). Additionally, the results of the in-situ SENT tests together with DIC and post-mortem fractographic analyses provided us with a better understanding of the microstructure-fracture correlations in these materials. Our results show that the fracture mechanism of the as fabricated and sub-transus heat-treated materials is essentially the same, while the changes in the microstructure following the super-transus heat-treatments significantly affects the fracture mechanism. In this case, several microcracks of hundreds of microns in length first nucleate away from the deformed notch following extreme plastic deformation at discrete locations. Furthermore, the location of these microcracks in the super-transus heat-treated materials is extremely sensitive to the details of the underlying microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Parallel point defect identification in molecular dynamics simulations without post-processing: A compute and dump style for LAMMPS

Two classes for the molecular dynamics program LAMMPS – one compute style and one dump style – are presented that are designed to identify, count, and output point defects in cascade damage and related molecular dynamics simulations. The calculations are done in parallel across multiple MPI processes, and can be done at the same time as the original simulation. This drastically reduces storage requirements by eliminating the need to post-process every atom in the system. Non-cubic lattices, free surfaces, and large voids can be eliminated from the output by suitable choices of the reference lattice. The classes are derived from LAMMPS’s Dump and Compute classes, and pose no additional overhead to LAMMPS if they are not used.

42 ENGINEERING↗
DOE OSTI2022

Low-temperature selective laser sintering 3D printing of PEEK-Nylon blends: Impact of thermal post-processing on mechanical properties and thermal stability

Herein, a high-performance polymer (HPP) (polyether ether ketone, PEEK) blended with a commercial low-temperature polymer (polyamide 12, PA-12) was fabricated using a low-temperature selective laser sintering (SLS) 3D printer. Different post-processing heat treatments were implemented after fabrication to tune the thermal and mechanical properties of printed blends. The specific mechanical modulus of the blend was increased up to 79% in comparison to printed PA-12 samples with heat treatment. Additionally, the thermal resistance of the treated blends greatly exceeded the properties observed using PA-12 alone. This research proposes a methodology to fabricate and engineer HPP using desktop SLS printers through a secondary lower melting temperature material and heat treatments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The evolution of HCO + in molecular clouds using a novel chemical post-processing algorithm

Modelling the chemistry of molecular clouds is critical to accurately simulating their evolution. To reduce computational cost, 3D simulations generally restrict their chemistry to species with strong heating and cooling effects. Time-dependent information about the evolution of other species is therefore often neglected. We address this gap by post-processing tracer particles in the SILCC-Zoom molecular cloud simulations. Using a chemical network of 39 species and 301 reactions (including freeze-out of CO and H 2 O) and a novel algorithm to reconstruct a density grid from sparse tracer particle data, we produce time-dependent density distributions for various species. We focus upon the evolution of HCO + , which is a critical formation reactant of CO but is not typically modelled on the fly. We find that ∼ 90 per cent of the HCO + content of the cold molecular gas forms in situ around n HCO + ∼ 10 3 –10 4 cm −3 , over a time-scale of approximately 1 Myr. The remaining ∼ 10 per cent forms at high extinction sites, with minimal turbulent mixing out into the less dense gas. We further show that the dominant HCO + formation pathway is dependent on the visual extinction, with the reaction H 3 + + CO contributing 90 per cent of the total HCO + production above A V, 3D = 3. We produce the very first maps of the HCO + column density, N(HCO + ), and show that it reaches values as high as 10 15 cm −2 . We find that 50 per cent of the HCO + mass is located within AV ∼ 10–30 in a density range of 10 3.5 –10 4.5 cm −3 . Our maps of N(HCO + ) are shown to be in good agreement with recent observations of the W49A star-forming region.

79 ASTRONOMY AND ASTROPHYSICS↗