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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2018

Computational framework for modeling membrane processes without process and solution property simplifications

Accurately modeling membrane processes is critical to evaluating novel process configurations, designing scalable membrane systems, informing process cost estimates, and directing future research. Most membrane process models trade accuracy for computational efficiency by employing simplified approximations of the process (i.e. no salt flux, no pressure drop) and solution properties (i.e. ideal solution, and constant density, viscosity, and diffusivity). Our report introduces a detailed one-dimensional finite difference model for evaluating membrane processes that avoids these common simplifications. We apply this model to quantify the error introduced by these simplifications for case studies of reverse osmosis, osmotically assisted reverse osmosis, forward osmosis, and pressure retarded osmosis. While the magnitude of error introduced by these simplifications is reliant on the case study parameters and specifications, we find that existing model formulations can underestimate or overestimate average water flux by nearly 50% for some membrane processes operating under standard conditions. Lastly we investigate the error introduced by simplified inlet-outlet models that do not solve the governing system of differential equations, and we assess the accuracy of novel inlet-outlet formulations that use a log and geometric mean, instead of the typical arithmetic mean, to represent non-linear water flux profiles.

42 ENGINEERING↗
DOE OSTI2023

Effect of fiber reinforcement on the process parameters of injection molding manufacturing process

Use of fillers with the resin is common in injection molding manufacturing technique to enhance the stiffness and strength of the manufactured components. However, the addition of fillers changes the rheological and mechanical properties of the material, required adjustments in the process parameters used for the manufacturing. The present study aims to investigate this impact of fillers on the process parameters of the injection molding manufacturing process. Neat Acrylonitrile Butadiene Styrene (ABS) and ABS reinforced with short glass fibers are used as the materials to perform the comprehensive numerical analysis including complete process sequence, namely filling, packing, cooling and warpage. Parametric studies are conducted to optimize the manufacturing process for minimal warpage (or shrinkage) in the produced part. The selected process parameters for the parametric study include cooling time, packing pressure, and mold temperature, all of which directly impact the quality of the manufactured parts. The optimal values obtained for these parameters are compared to examine the effect fiber reinforcement has on the manufacturing process.

Garg, Nikhil↗
DOE OSTI2019

Permanganate Oxidation of Defense Waste Processing Facility (DWPF) Recycle Collection Tank (RCT) Simulants – Protocol Runs for Nominal and Chemical Process Cell (CPC) Foamover Conditions

Savannah River National Laboratory (SRNL) has completed a work scope in support of the Savannah River Remediation (SRR) Nitric-Glycolic flowsheet. Glycolic acid will replace formic acid as a reducing agent in the pre-processing of High-Level Waste sludge performed at the Defense Waste Processing Facility (DWPF). Glycolic acid will perform the same function as formic acid, namely reduction of mercury and adjustment of feed rheology and melter oxidation/reduction potential, while significantly reducing the potential for hydrogen generation in DWPF processing. Development testing has demonstrated glycolic acid virtually eliminates hydrogen production in the pre-processing steps. The Nitric-Glycolic flowsheet significantly improves DWPF’s ability to address one of the Savannah River Site’s key challenges - the incorporation of effluent received from the Salt Waste Processing Facility (SWPF). SWPF will deliver significant effluent volume to DWPF, resulting in a concurrent increase in DWPF effluent returned to the Concentration, Storage and Transfer Facilities (CSTF). This work scope demonstrates that glycolate can be destroyed under the Nitric-Glycolic flowsheet prior to returning the DWPF recycle stream to CSTF. To avoid potential flammability issues due to thermolysis of glycolate in CSTF, SRR tasked SRNL to quantify and mitigate glycolate returns via DWPF’s recycle stream. The development of a strategy for glycolic mitigation was initiated with a system’s engineering workshop. Various chemical and/or physical solutions for how and where to destroy glycolate were considered – consistent with DWPF’s operational capabilities and process requirements. The workshop identified chemical oxidation of glycolate within the DWPF Recycle Collection Tank (RCT) as the most promising option. (The RCT collects offgas condensate from pre-processing, vitrification, and other unit operations performed in DWPF and is the singular return vessel delivering recycle effluent back to CSTF.) A series of down-select experimental tests were performed that showed sodium permanganate was the most effective oxidant. This work was performed to identify an appropriate operational protocol for DWPF implementation. Building upon the downselect experiments, the work targeted an improved description of suitable RCT conditions prior to the strike (pH, etc.), permanganate addition rate, and destruction reaction kinetics. Data collected were also used to determine the best expression of reaction stoichiometry and the effective minimum permanganate addition required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Actinide-rich and Actinide-poor r -process-enhanced Metal-poor Stars Do Not Require Separate r -process Progenitors

The astrophysical production site of the heaviest elements in the universe remains a mystery. Incorporating heavy-element signatures of metal-poor, $r$-process-enhanced stars into theoretical studies of $r$-process production can offer crucial constraints on the origin of heavy elements. In this study, we introduce and apply the "actinide-dilution with matching" model to a variety of stellar groups, ranging from actinide-deficient to actinide-enhanced, to empirically characterize r-process ejecta mass as a function of electron fraction. We find that actinide-boost stars do not indicate the need for a unique and separate $r$-process progenitor. Rather, small variations of neutron richness within the same type of $r$-process event can account for all observed levels of actinide enhancements. The very low-$Y_e$ , fission-cycling ejecta of an $r$-process event need only constitute 10%–30% of the total ejecta mass to accommodate most actinide abundances of metal-poor stars. We find that our empirical $Y_e$ distributions of ejecta are similar to those inferred from studies of GW170817 mass ejecta ratios, which is consistent with neutron-star mergers being a source of the heavy elements in metal-poor, $r$-process-enhanced stars.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

AVIRIS and TIMS data processing and distribution at the land processes distributed active archive center

The U.S. Government has initiated the Global Change Research program, a systematic study of the Earth as a complete system. NASA's contribution of the Global Change Research Program is the Earth Observing System (EOS), a series of orbital sensor platforms and an associated data processing and distribution system. The EOS Data and Information System (EOSDIS) is the archiving, production, and distribution system for data collected by the EOS space segment and uses a multilayer architecture for processing, archiving, and distributing EOS data. The first layer consists of the spacecraft ground stations and processing facilities that receive the raw data from the orbiting platforms and then separate the data by individual sensors. The second layer consists of Distributed Active Archive Centers (DAAC) that process, distribute, and archive the sensor data. The third layer consists of a user science processing network. The EOSDIS is being developed in a phased implementation. The initial phase, Version 0, is a prototype of the operational system. Version 0 activities are based upon existing systems and are designed to provide an EOSDIS-like capability for information management and distribution. An important science support task is the creation of simulated data sets for EOS instruments from precursor aircraft or satellite data. The Land Processes DAAC, at the EROS Data Center (EDC), is responsible for archiving and processing EOS precursor data from airborne instruments such as the Thermal Infrared Multispectral Scanner (TIMS), the Thematic Mapper Simulator (TMS), and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). AVIRIS, TIMS, and TMS are flown by the NASA-Ames Research Center ARC) on an ER-2. The ER-2 flies at 65000 feet and can carry up to three sensors simultaneously. Most jointly collected data sets are somewhat boresighted and roughly registered. The instrument data are being used to construct data sets that simulate the spectral and spatial characteristics of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument scheduled to be flown on the first EOS-AM spacecraft. The ASTER is designed to acquire 14 channels of land science data in the visible and near-IR (VNIR), shortwave-IR (SWIR), and thermal-IR (TIR) regions from 0.52 micron to 11.65 micron at high spatial resolutions of 15 m to 90 m. Stereo data will also be acquired in the VNIR region in a single band. The AVIRIS and TMS cover the ASTER VNIR and SWIR bands, and the TIMS covers the TIR bands. Simulated ASTER data sets have been generated over Death Valley, California, Cuprite, Nevada, and the Drum Mountains, Utah using a combination of AVIRIS, TIMS, amd TMS data, and existing digital elevation models (DEM) for the topographic information.

Mah, G. R.↗
DOE OSTI2024

Techno-economic analysis of biomass value-added processing informed by pilot scale de-ashing of paper sludge feedstock

Paper sludge biomass represents an underutilized feedstock rich in pulped and processed cellulose which is currently a waste stream with significant disposal cost to industry for landfilling services. Effective fractionation of the cellulose from paper sludge presents an opportunity to yield cellulose as feedstock for value-added processes. A novel approach to cellulose fractionation is the sidehill screening system, herein studied at the pilot-plant scale. Composition analysis determined ash removal and carbohydrate retention of both sidehill and high-performance benchtop screening systems. Sidehill screening resulted in greater carbohydrates retention relative to benchtop screening (90% vs 66%) and similar ash removal (95% vs 98%). Techno-economic analysis for production of sugar syrup yielded a minimum selling price of $331/metric ton of sugar syrup including disposal savings, significantly less than a commercial sugar syrup without fractionation. Furthermore, sensitivity analysis showed that screening conditions played a significant role in economic feasibility for cellulosic yield and downstream processes.

09 BIOMASS FUELS↗
DOE OSTI2022

Process Scale-Up of an Energy-Efficient Membrane Solvent Extraction Process for Rare Earth Recycling from Electronic Wastes

This study reports the process scale-up and long-term performance of an energy-efficient and cost-effective membrane solvent extraction (MSX) process for separation and recovery of high purity rare earth oxides (REOs) from scrap permanent magnets (SPMs). Here, the rare earth elements (REEs), including dysprosium, neodymium, and praseodymium, are recovered from SPMs using a neutral extractant, tetraoctyl diglycolamide (TODGA) embedded in a microporous polypropylene hollow fiber membrane module. The MSX process performance is demonstrated with bench scale module with membrane surface area of 1.4 m 2 to industrial scale modules with membrane surface area of up to 20 m2 to enable the processing of up to 1 ton month –1 of SPMs. The purity and the yield of the recovered REOs are >99.5 wt% and >95%, respectively. The average extraction rate of REOs is >10 g m –2 hr –1 . A skid of MSX system is assembled with a membrane area of 40 m 2 . The MSX skid successfully recovers REOs with a capacity of 300 kg REOs/month. Finally, it is determined that the organic phase containing the extractant maintains its performance up to 250 h. The results suggest that the MSX process is an economically viable and environmentally friendly process for separation and recovery of REOs from electronic wastes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimodal Analysis of Spatially Heterogeneous Microstructural Refinement and Softening Mechanisms in Three-Pass Friction Stir Processed Al4Si Alloy

Multiple thermally and thermomechanically induced microstructural refinement mechanisms can be activated in metallic alloys when subjected to solid phase processing methods such as friction stir processing (FSP). In this work, we provide detailed descriptions of the relationship between region-specific microstructural refinement mechanisms and the variation in microhardness, through a systematic and multimodal microstructural characterization of an FSP-processed 75% cold-rolled Al-4 at.% Si model binary alloy. Spatially resolved high-energy synchrotron X-ray diffraction, electron backscattered diffraction, and scanning transmission electron microscopy were used to understand the spatially heterogeneous microstructural evolution due to FSP. Results provide insights into how mechanisms such as static recovery, static recrystallization, dynamic recovery and recrystallization, geometric and continuous dynamic recrystallization, and particle-stimulated static or dynamic grain nucleation may occur heterogeneously in the microstructure as a function of the distance from the stir zone in processed alloys, directly influencing the degree of softening. The systematic analysis of microstructures and hardness in the FSP-processed model binary alloy given in this work highlights the rich microstructural domains that can be uniquely harnessed through solid phase processing of metallic alloys.

Al4Si, Friction Stir Processing, Geometric Dynamic↗
DOE OSTI2025

The R -process Alliance: Enrichment of r -process Elements in a Simulated Milky Way–like Galaxy

We study the formation of stars with varying amounts of heavy elements synthesized by the rapid neutron-capture process (r-process) based on our detailed cosmological zoom-in simulation of a Milky Way–like galaxy with an N-body/smoothed particle hydrodynamics code, asura. Most stars with no overabundance in r-process elements, as well as the strongly r-process-enhanced (RPE) r-II stars ([Eu/Fe] > +0.7), are formed in dwarf galaxies accreted by the Milky Way within the 6 Gyr after the Big Bang. In contrast, over half of the moderately enhanced r-I stars (+0.3 < [Eu/Fe] ≤ +0.7) are formed in the main in situ disk after 6 Gyr. Our results suggest that the fraction of r-I and r-II stars formed in disrupted dwarf galaxies is larger the higher their [Eu/Fe] is. Accordingly, the most strongly enhanced r-III stars ([Eu/Fe] > +2.0) are formed in accreted components. These results suggest that non-r-process-enhanced stars and r-II stars are mainly formed in low-mass dwarf galaxies that hosted either none or a single neutron star merger, while the r-I stars tend to form in the well-mixed in situ disk. We compare our findings with high-resolution spectroscopic observations of RPE metal-poor stars in the halo and dwarf galaxies, including those collected by the R-Process Alliance. We conclude that observed [Eu/Fe] and [Eu/Mg] ratios can be employed in chemical tagging of the Milky Way’s accretion history.

Chemical enrichment↗
DOE OSTI2022

An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling

This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multistage Surface-Heated Vacuum Membrane Distillation Process Enables High Water Recovery and Excellent Heat Utilization: A Modeling Study

Surface-heated membrane distillation (MD) enhances the energy efficiency of desalination by mitigating temperature polarization (TP). However, systematic investigations of larger scale, multistage, surface-heated MD system with high water recovery and heat recycling are limited. Here, we explore the design and performance of a multistage surface-heated vacuum MD (SHVMD) with heat recovery through a comprehensive finite difference model. In this process, the latent heat of condensation is recovered through an internal heat exchanger (HX) using the retentate from one stage as the condensing fluid for the next stage and an external HX using the feed as the condensing fluid. Model results show that surface heating enhances the performance compared to conventional vacuum MD (VMD). Specifically, in a six-stage SHVMD process, 54.44% water recovery and a gained output ratio (GOR) of 3.28 are achieved with a surface heat density of 2000 W m –2 , whereas a similar six-stage VMD process only reaches 18.19% water recovery and a GOR of 2.15. Mass and energy balances suggest that by mitigating TP, surface heating increases the latent heat trapped in vapor. The internal and external HXs capture and reuse the additional heat, which enhances the GOR values. We show for SHVMD that the hybrid internal/external heat recovery design can have GOR value 1.44 times higher than that of systems with only internal or external heat recovery. Furthermore, by only increasing six stages to eight stages, a GOR value as high as 4.35 is achieved. The results further show that surface heating can reduce the energy consumption of MD for brine concentration. As a result, the multistage SHVMD technology exhibits a promising potential for the management of brine from industrial plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Kinetic processes of phase separation and aggregation behaviors in slot-die processed high efficiency Y6-based organic solar cells

Morphological optimization has proved to be one crucial factor contributing to the 19% efficiency of Y6-based organic solar cells (OSCs). Although the relationship between component miscibility and film morphology has been established, it has not been clarified how the film formation processes proceed, especially for methods compatible with large-area fabrication, i.e. slot-die coating, which restricts the further optimization of the morphology. Herein, we comprehensively investigate the effect of miscibility and the film-formation kinetic process on film morphology during processing with different solvents. A highest power conversion efficiency (PCE) of 17.38% can be obtained in a D18:Y6 device processed with slot-die coating in an open-air environment. However, due to their relatively poor miscibility, incorporating a trace amount of chlorobenzene (CB) into CF can sufficiently promote Y6 aggregation, leading to increased phase separation and thus the average PCE drops to 15.16%. In contrast, a PM6:Y6 blend shows insensitive changes in the kinetic process as well as the final morphology, and thus in the comparable PCEs when cast with different solvents, which is ascribed to their relatively good miscibility. As a result, this work provides scientific guidelines for device optimization by combining the intermediate gap from miscibility to kinetic process and their impact on final morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗