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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational Fluid Dynamics Simulations to Support Efficiency Improvements in Aluminum Smelting Process

Smelting is broadly described as the extraction of a metal from its ore. In the United States, aluminum is commonly produced by smelting alumina in bauxite using the Hall-Héroult process. Optimization of equipment and processes in conventional smelting is crucial to enhancing process efficiency and productivity, is necessary for improving the techno-economic feasibility, which directly manifests as the growth of the American economy. To achieve optima, insightful data on the multiphysics phenomena that are inherent to the process must be obtained through physical investigation or high-fidelity numerical simulations. The resolution of relevant scales in time and space for smelting operations requires intensive, high-performance computing (HPC) simulations. Hostile operating conditions limit physical data acquisition to specific techniques; therefore, these data do not describe the multiscale interaction of simultaneous effects. Fortunately, in recent decades, significant advancements in computing hardware and computational methods have made the numerical resolution of such a complex process possible. In this study, a high-fidelity simulation of aluminum smelting was performed using an open-source tool, OpenFOAM, which analyzed many parameters characteristic to underlying phenomena. A multiphysics model based on the Eulerian-Eulerian multifluid approach was adopted. This model can resolve critical issues in the electrolytic smelting of aluminum, such as bubbling of carbon dioxide from the anode(s), magnetohydrodynamics from electromagnetic effects, ionic dissolution of the alumina in the electrolyte, and the evolution of thermal profiles. This study provides valuable connectivity for characteristic data that can direct the future designs of efficient smelters. A basic framework to model and simulate the smelting process using OpenFOAM is presented for user modification in keeping with process development. Of relevance to the flow field, a detailed investigation of vortices produced by bubble motion and electromagnetics is discussed, along with their impact on the evolution of thermal profiles. The predictions show small-scale vortices in the clearance between the anode and cathode caused by magnetic forces. Predictions also indicate relatively large-scale vortices in the inter-anode space resulting from carbon dioxide rising through the electrolytic flow field. The formation of vortices at the edges of anodes was shown to direct alumina charged by the feeder to the bottom of the anodes, thus preventing the entrapment of gas bubbles in the periphery of the bottom of the anode. Symmetry was observed in the location of cold spots in the electrolytic mixture in the vicinity of the feeder. Cold spots were also observed in the clearance between the anode and cathode due to the flow’s transmission of unconverted alumina to this region.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Approaching the Radiative Efficiency Limit in Perovskite Solar Cells with Scalable Defect Passivation and Selective Contacts

This award aimed to enable perovskite solar cells to approach the radiative efficiency limit in scalable manufacturing environments by controlling recombination losses, especially surface recombination losses at electrodes and interfaces. The project combined organic molecular synthesis, perovskite film processing and characterization, and spectroscopic tool development for probing recombination centers. The project ultimately achieved record-low surface recombination velocity (SRV) in mixed cation methylammonium-free perovskite thin films, demonstrated photoluminescence as an effective process metrology tool to optimizing processing of device stacks, and showed that the aminopropyltrimethoxysilane (APTMS) is suitable for passivating the exposed perovskite interface in p-i-n stack devices. The project used combinations of phosphonic acids to modify the transparent conducting oxide and APTMS to passivate the perovskite/electron transport layer interface, thereby demonstrating reduction of SRVs in both partial and full device stacks. The project showed concomitant improvements in device performance, and demonstrated that APTMS passivation was compatible with large area coating of external stakeholder perovskite films using both scalable solution and vapor methods. Notably, the project also supplied surface passivating materials to a number of other US based and SETO-funded teams.

14 SOLAR ENERGY↗
DOE OSTI2023

Using Ultrasonics to Optimize the Processing Parameters of Thermoset Polymers [Slides]

Ultrasonics is used as a non-destructive evaluation method to improve our understanding of polymers. Coupling ultrasonics and FTIR can help determine which bonds are more relevant to the mechanical properties of the polymer. Modeling the cure kinetics can determine if the curing process of a sample deviates from the baseline. Cure kinetics influence the final chemical structure of the polymer. This method opens a new pathway to design and adapt polymers for high demanding applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Resin Testing and Modeling for Optimal Composite Processing

Polymer composites have properties such as high strength and stiffness, low weight, good thermal and chemical stability, as well as impact and abrasion resistance that make them ideal for high-performance applications. The chemistries of these materials are continuously improving, so determining their properties is vital for successfully producing them and achieving the desired results. Multiple methods can be employed to monitor characteristics such as heat flow, weight, dimension, and modulus as a function of time and temperature. By analyzing this information, models can be developed to predict outcomes of parameters not tested for. In one application, materials proposed for wet filament winding and the production of high pressure vessels can be analyzed to verify they will have the necessary low viscosity for good fiber wetting and long pot life for the extended handling inherent to this process. Such data about a prospective system provides valuable information on how that material could ultimately be processed to yield the desired part.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Improved Photosensor for Light Valves

Processing changes improve performance of liquid-crystal light valve for displaying projection TV images. New approach monitors performance of finished light valves for given changes in CdS process and experimentally to optimize process for good sensitivity and low negative memory.

Koda, N. J.↗
NASA NTRS1988

Optimization of Processing of Si3N4

Process changes iterated under guidance of x-radiography. In recent work at NASA Lewis Research Center, density gradients in sintered silicon nitride, characterized by x-radiography, identified and appeared strongly dependent upon powder-processing and sintering conditions. NASA technical memorandum describes systematic investigation, based upon preliminary work, of density-gradient/flexural-strength relationships as affected by processing.

Sanders, William A.↗
NASA NTRS1997

Computational Modeling in Structural Materials Processing

High temperature materials such as silicon carbide, a variety of nitrides, and ceramic matrix composites find use in aerospace, automotive, machine tool industries and in high speed civil transport applications. Chemical vapor deposition (CVD) is widely used in processing such structural materials. Variations of CVD include deposition on substrates, coating of fibers, inside cavities and on complex objects, and infiltration within preforms called chemical vapor infiltration (CVI). Our current knowledge of the process mechanisms, ability to optimize processes, and scale-up for large scale manufacturing is limited. In this regard, computational modeling of the processes is valuable since a validated model can be used as a design tool. The effort is similar to traditional chemically reacting flow modeling with emphasis on multicomponent diffusion, thermal diffusion, large sets of homogeneous reactions, and surface chemistry. In the case of CVI, models for pore infiltration are needed. In the present talk, examples of SiC nitride, and Boron deposition from the author's past work will be used to illustrate the utility of computational process modeling.

Meyyappan, Meyya↗
NASA NTRS1997

Automating the Process of Optimization in Spacecraft Design

Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces, and human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., generic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.

optimization↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1993

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

Becus, Georges A.↗