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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration (PCC)

Coal is a globally abundant resource that historically has been used for power generation via combustion. As the power industry replaces coal with cleaner methods of generation, energy rich coal could be used in other chemical and fuel applications. This study presents a coal utilization option in which coal combustion is replaced with a carbon-free nuclear power plant and the coal is upgraded to valuable products for a variety of markets. Coal is prepared for conversion first by the pyrolysis process, which will optimize solid, liquid, and gaseous products based on the market size and potential product value, maximizing the monetary value of coal. This process is designed using bituminous coal from the Appalachian region as a basis to provide a pathway to preserve or transition coal-related jobs and create new jobs associated with the clean energy transition. Process modeling will be used to determine each component’s sensitivities, costs, inputs, and outputs Advanced and light-water reactors are considerations to supply the heat, steam, and electricity to the process. This paper focuses on the technical and market analysis used to determine the optimal processes and product pathways for the carbon refinery. Product pathways are on activated carbon, formic acid synthesis, and methanol synthesis for further upgrading to marketable chemical and polymer products.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

In situ characterization of material extrusion printing by near-infrared spectroscopy

Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. Here, we present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Pre-Hardware Optimization of Spacecraft Image Processing Algorithms and Hardware Implementation

Spacecraft telemetry rates and telemetry product complexity have steadily increased over the last decade presenting a problem for real-time processing by ground facilities. This paper proposes a solution to a related problem for the Geostationary Operational Environmental Spacecraft (GOES-8) image data processing and color picture generation application. Although large super-computer facilities are the obvious heritage solution, they are very costly, making it imperative to seek a feasible alternative engineering solution at a fraction of the cost. The proposed solution is based on a Personal Computer (PC) platform and synergy of optimized software algorithms, and reconfigurable computing hardware (RC) technologies, such as Field Programmable Gate Arrays (FPGA) and Digital Signal Processors (DSP). It has been shown that this approach can provide superior inexpensive performance for a chosen application on the ground station or on-board a spacecraft.

Kizhner, Semion↗
DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗
DOE OSTI2022

Optimization of stochastic feature properties in laser powder bed fusion

Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. As such, in this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm 2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.

316 L stainless steel↗
DOE OSTI2020

Fabrication of field-effect transistors with transfer-free nanostructured carbon as semiconducting channel material

Carbon nanostructures as active channel material in field effect transistors (FETs) are appealing in microelectronics for their improved performance such as high speed and low energy dissipation. However, these devices require the incorporation of nanostructure transfer steps in the fabrication process flow, which makes difficult its application in large scale integration circuits. Here we present a novel method for the fabrication of FETs with nanostructured carbon in the channel with p-type semiconducting properties and intermediate drain-source current (IDS) on/off ratio. The method is based on the use of Ni nanoparticles in the source-drain gap region as seed material for the formation of carbon nanostructures in the FET channel. FETs without Ni nanoparticles in the channel showed no modulation of IDS as a function of gate voltage. The device fabrication process does not require any carbon nanostructure transfer steps since it directly forms carbon nanostructures electrically connected to the device's source and drain electrodes via electron-beam evaporation of carbon and conventional lithographic processes. Since all device fabrication steps are compatible with Si technology processes, they are capable of being further optimized following process development protocols practiced by the semiconductor industry.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Electropolishing of a Full-Sized U-10Mo Plate

Electropolishing is used to remove material from the surface of a metal using electric potential and current. U-10Mo fuel is produced from a low enriched uranium plate alloyed with 10% molybdenum (U-10Mo). Electropolishing is being considered in two steps of the process of fabricating this U-10Mo fuel. First, it could be used as a means initially improving the surface finish of the cast plate while removing smut from the casting process from the surface prior to homogenization. Second, it could be used again after homogenization to remove oxidation and continue to improve the surface finish prior to hot rolling. The purpose of this work is to demonstrate the process on full-size ingots and optimize the process. A depleted uranium U-10Mo plate was electropolished three times: the first two times were prior to homogenization and the third after a homogenization step. The first two polishes decreased the overall roughness, as measured by laser confocal microscopy. After each polish, a smoother and shinier blue-gold surface was left on the plate. Upon homogenization, the surface had a more matte appearance and the surface roughness increased back to the pre-homogenization values obtained prior to the initial polish. After electropolishing the homogenized pieces, the changes in surface roughness increased, as measured by laser confocal microscopy. The final polishing step was not long enough to return the surface roughness to pre-homogenization conditions, implying less polishing is needed before the homogenization versus afterwards. Heat generation during the polish was addressed by circulating the electrolyte solution through an external heat exchanger; however, solubility and anodization challenges require further study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2003

Intelligent Work Process Engineering System

Optimizing performance on work activities and processes requires metrics of performance for management to monitor and analyze in order to support further improvements in efficiency, effectiveness, safety, reliability and cost. Information systems are therefore required to assist management in making timely, informed decisions regarding these work processes and activities. Currently information systems regarding Space Shuttle maintenance and servicing do not exist to make such timely decisions. The work to be presented details a system which incorporates various automated and intelligent processes and analysis tools to capture organize and analyze work process related data, to make the necessary decisions to meet KSC organizational goals. The advantages and disadvantages of design alternatives to the development of such a system will be discussed including technologies, which would need to bedesigned, prototyped and evaluated.

Williams, Kent E.↗
DOE OSTI2025

Restructuring and Optimizing Reactor Building Lifting Processes & Designing a Solution for an Overhead Door Handling Forklift Attachment

This poster presents two innovative projects aimed at enhancing operational efficiency and safety at the Advanced Test Reactor (ATR) Complex. The first project focuses on restructuring the existing lift book used for hoisting operations within the ATR Reactor Building. The current lift book, a cumbersome 150+ page document, is being transformed into a more efficient format using charts that allow for quick identification of maximum lift heights based on object footprint and weight. This new method also considers various parameters such as floor integrity and reactor status, dividing entries into four distinct charts to improve usability and safety during lifts. The second project involves designing a specialized forklift attachment for handling an overhead door at the ATR Reactor Building. The attachment is engineered to securely lift and lower a 1200lb, 14ft long door, ensuring safe replacement operations. The design process included research on forklift attachment codes, modeling in Autodesk Inventor, and testing through Finite Element Analysis (FEA) and hand calculations to confirm the attachment's structural integrity. Future work includes completing detailed drawings and an Engineering Calculations Analysis and Review (ECAR) document to proceed with manufacturing and assembly. Together, these projects demonstrate a commitment to optimizing reactor building operations through innovative engineering solutions and safety considerations.

42 - ENGINEERING↗
DOE OSTI2020

Integration of graphical approaches into optimization-based design of multistage liquid extraction

We propose two optimization models for designing two liquid extraction systems: simple multistage liquid extractors and extractors with extract reflux. Both models are motivated by the concepts of the modified McCabe-Thiele graphical method for multistage extractor design. The operating and equilibrium curves in the McCabe-Thiele method are represented by material balances and piece-wise linearized thermodynamics properties. The use of piece-wise approximations improves computational tractability of both optimization models. In addition, we consider some extensions such as dilute systems, insoluble solvents, and non-ideal stages. In conclusion, the applicability of the proposed models is demonstrated with four illustrative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Optimization of edge bead removal (EBR) process to enhance defect reduction in optical lithography

Defect reduction remains a critical objective in the integrated circuit manufacturing process, particularly within the highly re-entrant lithography modules where minimizing defects is crucial. Defects at the wafer edge can contaminate lithography modules and downstream processing equipment, leading to redistribution onto the wafer surface and adversely affecting overall device yield. A persistent challenge in the resist coating process is the formation of resist edge beads, driven by the strong Van der Waals attraction of excess photoresist (PR) to itself and the underlying substrate. The edge bead removal (EBR) process is a standard cleaning step designed to eliminate these edge beads and prevent potential contamination. Here, in this study, we identify the sources of EBR induced defects and additional EBR process encroachment toward edge patterning during the EBR cleaning process. This study provides a comprehensive study aimed at optimizing the EBR cleaning process to effectively eliminate EBR-induced defects, thereby enhancing overall device yield. Specifically, we identify three primary defects induced by the EBR cleaning process: rainbow-type, finger-shaped, and teardrop-type defects. Our experimental study reveals that in addition to EBR rinse time, PR cast time is crucial parameters contributing to the formation of these defects. By properly optimizing the PR cast time and EBR rinse time, we were able to remove nearly 100 % of dense clusters of defects that were easily visible even at low magnification optical microscopy throughout the wafer edge. We observed that shorter PR casting times shows edge defects caused by inefficient EBR process because of insufficient time for PR to fully settle causing superfluous PR to continue flowing toward wafer edge during EBR clearing step, leading to partial removal of PR at the wafer edge and the formation of rainbow defects. Proper optimization of both PR casting time and EBR chemistries dispense time is essential to resolve these defects, ensuring efficient EBR cleaning process and improved overall device yield.

42 ENGINEERING↗
DOE OSTI2026

Final Report: Process Intensification of Hydrogen Production through Sorption-Enhanced Gasification of Biomass

The University of Utah, in partnership with Idaho National Laboratory (INL), evaluated Sorption-Enhanced Gasification (SEG) as a transformative pathway for producing hydrogen with the potential for negative CO 2 emissions. SEG integrates gasification, water-gas shift, and in-situ carbon capture within a dual fluidized bed reactor to enable efficient clean hydrogen production. Key challenges related to biomass variability and process complexity were addressed through feedstock engineering, reaction optimization, and process validation. A co-pelletized biomass–limestone feedstock was developed to simplify feeding and introduction of makeup limestone. Kinetic and sorbent studies identified optimal operating conditions and confirmed the suitability of low-cost limestone, while catalysts were developed to reduce tar formation. Reactor modeling and techno-economic analysis indicated that SEG can achieve competitive hydrogen production costs, particularly when combined with carbon incentives, supporting its potential for scale-up and carbon-negative operation.

08 HYDROGEN↗
DOE OSTI2021

Parametric study and speciation analysis of rare earth precipitation using oxalic acid in a chloride solution system

Oxalic acid precipitation is a common step in the purification of rare earth elements (REE) from a concentrated pregnant leach solution (PLS). However, the presence of contaminants such as Al, Fe, and Ca in given amounts decreases the REE precipitation efficiency and product purity while also increasing the amount of oxalic acid needed to maximize recovery. As such, a statistically designed test program was performed to identify the optimal conditions necessary for a relatively low REE content PLS containing elevated concentrations of contaminant ions. The performance objective was maximization of REE precipitation efficiency while minimizing the oxalic acid dosage. A central composite design was utilized to quantify performance impacts and identify the ultimate set of parameter values for oxalic acid dosage, Fe(III) contamination concentration, solution pH, and reaction temperature. The resultant model suggested that oxalic acid dosage and reaction pH are the most significant factors for the REE precipitation efficiency, followed by the interaction of oxalic dosage and Fe concentration. Test results indicate that increasing the oxalic acid concentration from 0 g/L to 80 g/L improved the REE precipitation efficiency from approximately 4.2% to 95.0%. Furthermore, raising the solution pH from 0.5 to 2.5 considerably enhanced the precipitation efficiency from 0.0% to 98.9%. A solution temperature elevation decreased REE recovery, which indicated an exothermic reaction between REEs and oxalate anions. Finally, a high level of Fe contamination adversely impacted REE precipitation efficiency. Here, to further the understanding of the REE-oxalate system, a fundamental solution chemistry study was performed using the equilibrium constants of the reactions. The study resulted in the development of oxalate speciation diagrams and provided an analysis of the REE precipitation characteristics at various oxalate anion concentrations and Fe(III) contamination levels using MINTEQ software. The dominant Fe(III) species in the solution system were found to be Fe-(C 2 O 4 ) 3 3- , Fe-(C 2 O 4 ) 2- , and Fe-(C 2 O 4 ) + , which consume the majority of the oxalate anions. The simulated model was found to be in agreement with the experimental findings and helped to explain the adverse impact of increased iron concentrations on REE precipitation efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Fast truncated SVD of sparse and dense matrices on graphics processors

We investigate the solution of low-rank matrix approximation problems using the truncated singular value decomposition (SVD). For this purpose, we develop and optimize graphics processing unit (GPU) implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be assembled using numerical kernels from existing high-performance linear algebra libraries. Furthermore, the experiments with several sparse matrices arising in representative real-world applications and synthetic dense test matrices reveal a performance advantage of the block Lanczos algorithm when targeting the same approximation accuracy.

Computer Science↗