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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Sensitivity-informed, Process Model Abstraction Implementation in GoldSim - 19276

System-level Performance Assessment (PA) models rely on specific process-level models for some inputs, such as infiltration. For probabilistic PA, the value of the process model output depends on the values of input parameters that also vary in the PA model. The simplest conceptual solution is to draw values of the input parameters from their distributions, run the process level model at these values, and then use the output where it is needed in the PA model. However, this solution is unsatisfactory for multiple reasons: (1) the PA Model should be set up such that new runs can be made and distributions can change without having to go back and run the process-level model; (2) calling the process model from the system model is possible, but technical difficulties remain in coupling the two; and (3) there is no uncertainty in the process-level model results incorporated into the PA model. Therefore, it is desirable to develop a method that maps the values of PA Model inputs to process level model outputs, in real time within the PA Model set up and using as simple of a map as possible without losing key features of the relationship. This has been termed 'model abstraction' because information about process model runs is 'abstracted' to be used with the PA model. A simple 'nearest neighbor' abstraction approach is presented to overcome these problems, in which a sensitivity-informed distance metric is used to map a system-level model realization to the most appropriate process-level model realization. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparison of Silicon Decarbonization Methods to Reduce Process Emissions and Energy Consumption

The demand for silicon is rising due to its use in solar cells, electronics, and alloying as part of the green transition. However, these products require silicon metal (metallurgical grade silicon, >98% Si) as their main raw material before undergoing further chemical refining, usually via gas phase in methods such as the Siemens process. Silicon metal is currently produced in a submerged arc furnace via carbothermic reduction of quartz, which results in 10-12 tCO2/tSi. More than 40% of these emissions are process emissions, meaning they do not include contributions from electricity generation or transport. This talk will discuss promising methods to reduce or eliminate these process emissions. These include the use of biocarbon and carbon capture, aluminothermic reduction, electrochemical reduction with molten salts, and use of hydrogen in a modified Siemens process. Biocarbon and carbon capture is a straightforward method to reduce the net carbon emissions of the process, but long-term sustainable production of high-quality charcoal is a challenge. Aluminothermic reduction makes good use of aluminum byproducts such as dross but is perhaps limited in its applications to aluminum-silicon alloy products. Electrochemical reduction is promising as a method of producing high purity Si from quartz, without further need for the Siemens process, but is still in early stages of research and slow processing times are a concern. Finally, the use of hydrogen in a modified Siemens process would also result in a high purity Si product from quartz, however the process has considerable logistical challenges with corrosive and unstable gases at high temperatures. This talk will further expand on the potential merits and challenges of each method, while comparing their potential to reduce emissions and/or energy consumption while meeting global demand.

decarbonization↗
DOE OSTI2025

A fast computational framework for the design of solvent-based plastic recycling processes

Multicomponent plastics cannot be processed using mechanical recycling technologies, hindering efforts to deal with plastic waste. Multicomponent plastics include multilayer plastic films, which are widely used for food and healthcare packaging. Multilayer films combine several layers (potentially dozens) of different polymers to protect products from external factors (e.g., oxygen, water, temperature, shock, and light). Solvent-based separation processes have emerged as a promising alternative to recycle these complex materials. For instance, the Solvent-Targeted Recovery and Precipitation (STRAP TM ) process uses sequential solvent washes to selectively dissolve and separate constituent polymers from multicomponent plastic waste, including films. STRAP TM process design (separation sequence, type of solvents, and operating conditions) changes significantly depending on the design of the multilayer plastic film (e.g., number, types, and proportions of polymers). The ability to quickly quantify the economic and environmental benefits of diverse STRAP TM process designs is essential to accelerate the development of sustainable recycling processes and more recyclable multilayer film products. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, and techno-economic and life cycle analysis to quickly evaluate STRAP TM designs. The computational framework is general and can be used to study the processing of complex multilayer plastic waste streams that contain many layers. Furthermore, we highlight the different uses of the framework via targeted case studies.

Computational framework↗
DOE OSTI2019

Fatigue of additive manufactured Ti-6Al-4V, Part I: The effects of powder feedstock, manufacturing, and post-process conditions on the resulting microstructure and defects

Additive manufacturing provides an appealing means to process titanium alloy parts with new levels of conformability, complexity, and weight reduction. However, due to the heating/cooling rates and heat transfer associated with directed energy source material processing, the as-built AM parts contain unique material and microstructural features. In order to confidently manufacture fatigue critical additive manufactured (AM) Ti-6Al-4V parts, a better understanding of the interrelationships between powder feedstock, AM processes, structure of the processed parts, their resulting mechanical properties, and their performance under realistic loadings is necessary. Part I of this two-part collection focuses on the powder-process-structure relationships and how powder feedstock, manufacturing, and post-processing conditions can affect the microstructure and defect features that ultimately contribute to the fatigue performance of Ti-6Al-4V parts. The material and physical phenomena inherent to the AM process of Ti-6Al-4V are discussed in detail and related to the phase composition/structure, grain morphology, surface characteristics, defect size/distribution, and post-process treatments available for AM parts. This investigation is the foundation for the structure-performance relationships that will be covered in detail in Part II.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗
DOE OSTI2021

EGS Collab Experiment 1: Circulation Testing Processed data

This submission includes processed and reduced data for circulation testing that was conducted at the 164' fracture on the 4850 ft level of the Sanford Underground Research Facility. The circulation tests were done to test the flow through the 164' fracture in the EGS Collab Experiment 1 testbed on the 4850 ft level of the Sanford Underground Research Facility in order to validate computer models concerned with flow processes and heat exchange processes in EGS. This data was processed from raw data that is available in the GDR submission linked below: "EGS Collab Circulation Testing Raw Data and Documentation." Python scripts used to process and reduce the data are included. The scripts also serve as "documentation" as well as examples for data processing. More information about the test itself as well as the rationale and process of data processing is available at wiki page linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Process Control Plan to Monitor Acceptable Levels of Flux and other residues

IPC J-STD –001G, Amendment 1 requires that the assembler have a sampling plan to assure that the process remains in control once qualified and validated. It is well documented that electrochemical failures occur at component sites where flux residues are not fully activated. It is also well known that specific components have a higher risk of electrochemical failure. Selecting a control plan that monitors the process and its performance on challenging components provides assurance that the process maintains control once established during process qualification. The test board used to qualify the process establishes an upper and lower spec limit for each of the component types on the test board. Surface Insulation Resistance upper and lower control limits, using challenging components that are representative of production hardware, represent a golden process condition. The golden image is a measure of process deviations that represents the performance of a resistance curve within a specific period. The objective is to judge whether the process in or out of control.

42 ENGINEERING↗
DOE OSTI2023

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A manufacturing process design for producing a membrane-based energy recovery ventilator with high aspect ratio support ribs

Heating, ventilation, and air conditioning systems represent nearly half of building energy usage in the United States. Recent building regulations requiring increased air turnover within buildings will result in even greater air conditioning usage. To alleviate these concerns, membrane-based heat-and-moisture exchangers known as energy recovery ventilators have been developed to reduce the energy expenditure of air conditioning systems by pre-conditioning incoming air through sensible and latent heat transfer with building air exhaust. Here in this work, we propose and validate a manufacturing process design for channel lamination based on surface mount adhesives capable of meeting the process requirements of a membrane-based ventilator. In particular, the device requires rib supports with height-to-width aspect ratios greater than one. The proposed manufacturing process design is capable of meeting this process requirement by stretching the adhesive after initial adhesive tacking. A manufacturing process flow diagram, a machine tool specification and a cost model are proposed for meeting process requirements. A set of design constraints are developed detailing the adhesive requirements necessary for meeting requirements. The cost model is validated by building a sub-scale mesochannel array demonstrating the ability to meet process requirements. Results show that additional work is needed to validate the curing step but that the process is capable of producing a mesochannel array with an acceptable level of variation.

42 ENGINEERING↗
DOE OSTI2020

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review

Pressure-Retarded Osmosis (PRO) is an osmotic process that has been used to harvest energy from salinity gradients using a semi permeable membrane. A comparison between open-loop PRO (OLPRO) and closed-loop PRO (CLPRO) was made regarding their performance and costs. In CLPRO, where the diluted draw solution is re-concentrated in the regeneration system to be reutilized in the process, has recently received an intensive focus as the most viable configuration for a standalone power plant. The choice of the PRO draw solution in CLPRO is crucial to garner a high osmotic pressure as the key for the feasibility of the process. Here, in this review, the draw solutions are critically evaluated in the literature in terms of energy output as well as the method of regeneration used to recirculate them. A set of practical criteria has been suggested to appraise the adequacy of the solution for CLPRO application. It was concluded that NH 3 – CO 2 theoretically can produce 170 W/m 2 of power density. Inorganic draw solutes such as NaCl can generate high power density up to 87 W/m 2 . Organic draw solutes with their remarkably low reverse salt flux (RSF) have promising potential for future application in PRO. Similarly, the regeneration systems of the diluted draw solutions have also been reviewed and discussed. How the energy consumption of the regeneration process affects the feasibility of CLPRO is explained. For the specific case of osmotic heat engines (OHEs), when the energy of the regeneration process is supplied by heat waste, the range of applicability of the heat waste in CLPRO in terms of efficiency is defined and compared to Organic Rankine Cycle (ORC). The results showed that CLPRO has better efficiency than ORC for temperatures T < 80 °C, which makes it a promising process or low-grade heat energy recovery. In addition, a PRO-RO hybrid system coupled with solar power can reduce the net specific energy consumption (SEC) to 0.39 kWh/m 3 . The conditions that regeneration processes should operate under to make PRO viable are discussed in the last section. Overall, the study indicates the key factors for optimizing the performance of CLPRO process.

42 ENGINEERING↗
DOE OSTI2018

Solution-Processed Layered Gallium Telluride Thin-Film Photodetectors

Using scalable solution processing, layered gallium telluride (GaTe) nanoflake dispersions are produced in surfactant-free, low-boiling-point, water–ethanol cosolvent mixtures. During exfoliation, chemical degradation of the ambient-reactive GaTe crystals is minimized by using deoxygenated solvents in a sealed tip ultrasonication system. The structural and chemical integrity of the solution-processed GaTe nanoflakes is subsequently confirmed with a comprehensive suite of microscopic and spectroscopic analyses. Furthermore, field-effect transistors and phototransistors based on individual solution-processed GaTe nanoflakes show electronic and optoelectronic properties, respectively, that are comparable to micromechanically exfoliated GaTe. Minimal solution-processing residues from the surfactant-free, low-boiling-point cosolvent dispersion medium coupled with the high intrinsic hole doping of GaTe produces the highest electrical conductivity among solution-processed layered nanoflake thin films without post-treatment. Large-area photodetectors based on these electrically percolating thin films of solution-processed GaTe nanoflakes show a positive correlation between responsivity and illumination intensity, with a high photoconversion gain that is explained by a combination of defect-mediated optical processes and photothermal effects. Finally, overall, this study establishes solution-processed layered GaTe nanoflakes as a leading candidate for high-performance, large-area, thin-film photodetectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Sensitivity studies for the main r process: β-decay rates

The pattern of isotopic abundances produced in rapid neutron capture, or r-process, nucleosynthesis is sensitive to the nuclear physics properties of thousands of unstable neutron-rich nuclear species that participate in the process. It has long been recognized that the some of the most influential pieces of nuclear data for r-process simulations are β-decay lifetimes. In light of experimental advances that have pushed measurement capabilities closer to the classic r-process path, we revisit the role of individual β-decay rates in the r process. We perform β-decay rate sensitivity studies for a main (A > 120) r process in a range of potential astrophysical scenarios. We study the influence of individual rates during (n, γ)-(γ, n) equilibrium and during the post-equilibrium phase where material moves back toward stability. We confirm the widely accepted view that the most important lifetimes are those of nuclei along the r-process path for each astrophysical scenario considered. However, we find in addition that individual β-decay rates continue to shape the final abundance pattern through the post-equilibrium phase, for as long as neutron capture competes with β decay. Many of the lifetimes important for this phase of the r process are within current or near future experimental reach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Large deviations in stochastic heat-conduction processes provide a gradient-flow structure for heat conduction

We consider three one-dimensional continuous-time Markov processes on a lattice, each of which models the conduction of heat: the family of Brownian Energy Processes with parameter m (BEP(m)), a Generalized Brownian Energy Process, and the Kipnis-Marchioro-Presutti (KMP) process. The hydrodynamic limit of each of these three processes is a parabolic equation, the linear heat equation in the case of the BEP(m) and the KMP, and a nonlinear heat equation for the Generalized Brownian Energy Process with parameter a (GBEP(a)). We prove the hydrodynamic limit rigorously for the BEP(m), and give a formal derivation for the GBEP(a). We then formally derive the pathwise large-deviation rate functional for the empirical measure of the three processes. These rate functionals imply gradient-flow structures for the limiting linear and nonlinear heat equations. We contrast these gradient-flow structures with those for processes describing the diffusion of mass, most importantly the class of Wasserstein gradient-flow systems. The linear and nonlinear heat-equation gradient-flow structures are each driven by entropy terms of the form -log ρ; they involve dissipation or mobility terms of order ρ² for the linear heat equation, and a nonlinear function of ρ for the nonlinear heat equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Work flow of signal processing data of ground penetrating radar case of rigid pavement measurements

The signal processing of Ground Penetrating Radar (GPR) requires a certain work flow to obtain good results. Even though the Ground Penetrating Radar data looks similar with seismic reflection data, but the GPR data has particular signatures that the seismic reflection data does not have. This is something to do with coupling between antennae and the ground surface. Because of this, the GPR data should be treated differently from the seismic signal data processing work flow. Even though most of the processing steps still follow the same work flow of seismic reflection data such as: filtering, predictive deconvolution etc. This paper presents the work flow of GPR processing data on rigid pavement measurements. The processing steps start from raw data, de-Wow process, remove DC and continue with the standard process to get rid of noises i.e. filtering process. Some radargram particular features of rigid pavement along with pile foundations are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

ENRICHMENT OF r-PROCESS ELEMENTS IN DWARF SPHEROIDAL GALAXIES IN CHEMO-DYNAMICAL EVOLUTION MODEL

The rapid neutron-capture process (r-process) is a major process for the synthesis of elements heavier than iron-peak elements, but the astrophysical site(s) of the r-process has not yet been identified. Neutron star mergers (NSMs) are suggested to be a major r-process site according to nucleosynthesis studies. Previous chemical evolution studies, however, required unlikely short merger times of NSMs to reproduce the observed large star-to-star scatters in the abundance ratios of r-process elements to iron: the [Eu/Fe] of extremely metal-poor stars in the Milky Way (MW) halo. This problem can be solved by considering chemical evolution in dwarf spheroidal galaxies (dSphs), which would be building blocks of the MW and have lower star formation efficiencies than the MW halo. We demonstrate the enrichment of r-process elements in dSphs by NSMs using an N-body/smoothed particle hydrodynamics code. Our high-resolution model reproduces the observed [Eu/Fe] due to NSMs with a merger time of 100 Myr when the effect of metal mixing is taken into account. This is because metallicity is not correlated with time ∼300 Myr from the start of the simulation due to the low star formation efficiency in dSphs. We also confirm that this model is consistent with observed properties of dSphs such as radial profiles and metallicity distribution. The merger time and the Galactic rate of NSMs are suggested to be ≲300 Myr and ∼10{sup −4} year{sup −1}, respectively, which are consistent with the values suggested by population synthesis and nucleosynthesis studies. This study supports the argument that NSMs are the major astrophysical site of the r-process.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

PRODUCTION OF ALL THE r-PROCESS NUCLIDES IN THE DYNAMICAL EJECTA OF NEUTRON STAR MERGERS

Recent studies suggest that binary neutron star (NS-NS) mergers robustly produce heavy r-process nuclei above the atomic mass number A ∼ 130 because their ejecta consist of almost pure neutrons (electron fraction of Y {sub e} < 0.1). However, the production of a small amount of the lighter r-process nuclei (A ≈ 90-120) conflicts with the spectroscopic results of r-process-enhanced Galactic halo stars. We present, for the first time, the result of nucleosynthesis calculations based on the fully general relativistic simulation of a NS-NS merger with approximate neutrino transport. It is found that the bulk of the dynamical ejecta are appreciably shock-heated and neutrino processed, resulting in a wide range of Y {sub e} (≈0.09-0.45). The mass-averaged abundance distribution of calculated nucleosynthesis yields is in reasonable agreement with the full-mass range (A ≈ 90-240) of the solar r-process curve. This implies, if our model is representative of such events, that the dynamical ejecta of NS-NS mergers could be the origin of the Galactic r-process nuclei. Our result also shows that radioactive heating after ∼1 day from the merging, which gives rise to r-process-powered transient emission, is dominated by the β-decays of several species close to stability with precisely measured half-lives. This implies that the total radioactive heating rate for such an event can be well constrained within about a factor of two if the ejected material has a solar-like r-process pattern.

79 ASTRONOMY AND ASTROPHYSICS↗