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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transforming biorefinery designs with ‘Plug-In Processes of Lignin’ to enable economic waste valorization

Biological lignin valorization has emerged as a major solution for sustainable and cost-effective biorefineries. However, current biorefineries yield lignin with inadequate fractionation for bioconversion, yet substantial changes of these biorefinery designs to focus on lignin could jeopardize carbohydrate efficiency and increase capital costs. We resolve the dilemma by designing ‘plug-in processes of lignin’ with the integration of leading pretreatment technologies. Substantial improvement of lignin bioconversion and synergistic enhancement of carbohydrate processing are achieved by solubilizing lignin via lowering molecular weight and increasing hydrophilic groups, addressing the dilemma of lignin- or carbohydrate-first scenarios. The plug-in processes of lignin could enable minimum polyhydroxyalkanoate selling price at as low as $6.18/kg. The results highlight the potential to achieve commercial production of polyhydroxyalkanoates as a co-product of cellulosic ethanol. Here, we show that the plug-in processes of lignin could transform biorefinery design toward sustainability by promoting carbon efficiency and optimizing the total capital cost.

09 BIOMASS FUELS↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2017

Standard High Solids Vessel Design De-inventory Simulant Qualification

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is working to develop a Standard High Solids Vessel Design (SHSVD) process vessel. To support testing of this new design, WTP engineering staff requested that a Newtonian simulant be developed that would represent the de-inventory (residual high-density tank solids cleanout) process. Its basis and target characteristics are defined in 24590-WTP-ES-ENG-16-021 and implemented through PNNL Test Plan TP-WTPSP-132 Rev. 1.0. This document describes the de-inventory Newtonian carrier fluid (DNCF) simulant composition that will satisfy the basis requirement to mimic the density (1.18 g/mL ± 0.1 g/mL) and viscosity (2.8 cP ± 0.5 cP) of 5 M NaOH at 25 °C.1 The simulant viscosity changes significantly with temperature. Therefore, various solution compositions may be required, dependent on the test stand process temperature range, to meet these requirements. Table ES.1 provides DNCF compositions at selected temperatures that will meet the density and viscosity specifications as well as the temperature range at which the solution will meet the acceptable viscosity tolerance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Simulant Basis for the Standard High Solids Vessel Design

The Waste Treatment and Immobilization Plant (WTP) is working to develop a Standard High Solids Vessel Design (SHSVD) process vessel. To support testing of this new design, WTP engineering staff requested that a Newtonian simulant and a non-Newtonian simulant be developed that would represent the Most Adverse Design Conditions (in development) with respect to mixing performance as specified by WTP. The majority of the simulant requirements are specified in 24590-PTF-RPT-PE-16-001, Rev. 0. The first step in this process is to develop the basis for these simulants. This document describes the basis for the properties of these two simulant types. The simulant recipes that meet this basis will be provided in a subsequent document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor

Focused Electron Beam Induced Processing (FEBIP) is a powerful tool for the “direct-write” of nanomaterials with the possibility of atomistic control on suspended 2D material substrates. FEBIP capabilities have been significantly expanded by using a localized jet-based delivery of precursors, and especially when a thermally energized supersonic micro-jet enhances the delivery of mass flux along with controlling the far-from-equilibrium thermodynamic state of adsorbed adatoms. The possibilities of growing nanomaterials with “dialed-in” composition with ultra-high growth rates and aspect ratios up to 100:1 have been demonstrated using the supersonic micro-jet-FEBIP. Bringing this scientific discovery to the level of maturity required for practical applications in additive nanomanufacturing requires simulation tools that are capable of capturing the complex flow physics of micro-jet-substrate interactions that bridge a wide range of flow regimes from the high-density gas micro-jet expanding into the vacuum environment of FEBIP. To address this significant computational challenge, a new approach has been developed and described in this work for a multiscale adaptive DSMC (Direct Simulation Monte Carlo) algorithm to predict the micro-jet gas dynamics in an FEBIP environment, spanning the full range of flow regimes from low Knudsen (Kn) number O(0.01) continuum flow to high Kn of O(10) for the molecular flow within a unified computational framework. Here, the fundamental principles of the adaptive DSMC algorithm are described, its viability as a DSMC technique is demonstrated, and computational improvements for the benchmark cases are discussed in the context of advancing 3D nanofabrication with FEBIP. Ultimately, combining the first principle simulations via adaptive DSMC with the complementary experimental data will enable the creation of the powerful CAD tools for in silico design and optimal operation of micro-jet-FEBIP.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Thermodynamic assessment of the hollandite high‐level radioactive waste form

Abstract Hollandite has been studied as a candidate ceramic waste form for the disposal of high‐level radioactive waste due to its inherent leach resistance and ability to immobilize alkaline‐earth metals such as Cs and Ba at defined lattice sites in the crystallographic structure. The chemical and structural complexity of hollandite‐type phases developed for high‐level waste immobilization limits the systematic experimental research that is required to understand phase development due to the large number of potential additives and compositional ranges that must be evaluated. Modeling the equilibrium behavior of the complex hollandite‐forming oxide waste system would aid in the design and processing of hollandite waste forms by predicting their thermodynamic stability. Thus, a BaO–Cs 2 O–TiO 2 –Cr 2 O 3 –Al 2 O 3 –Fe 2 O 3 –FeO–Ga 2 O 3 thermodynamic database was developed in this work according to the CALPHAD methodology. The compound energy formalism was used to model solid solution phases such as hollandite while the two‐sublattice partially ionic liquid model characterized the oxide melt. Results of model optimizations are presented and discussed including a 1473 K isothermal BaO–Cs 2 O–TiO 2 pseudo‐ternary diagram that extrapolates phase equilibrium behavior to regions not experimentally explored.

Utlak, Stephen A.↗
DOE OSTI2021

Alternative All-Metal Scroll Pump Design

In tritium process systems, vacuum pumps are typically used to evacuate volumes and piping, as well as transfer gas to other parts of the process. This was done using the combination of an all-metal scroll pump with a metal bellows backing pump. The all-metal scroll pump, manufactured by Normetex, has been unavailable since 2012, and efforts continue to find a suitable replacement. The main obstacle is finding a pump that has no oils or polymer components, which degrade when exposed to tritium and introduce corrosive and/or hazardous impurities into the process. There have been development efforts to design an all-metal scroll pump design alternative to the Normetex style. The first generation was fabricated out of machined stainless steel, but the thermal management and increased weight were not ideal. To mitigate the weight, an aluminum version was designed. The tritium holdup on untreated aluminum surfaces can be greater than stainless steel, so the wetted aluminum surfaces were electroless plated in nickel and then electroplated in gold. The development effort outlined in this report details the design of an improved version of the stainless steel version.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Design and optimization of processes for recovering rare earth elements from end‐of‐life permanent magnets

Recovery of rare earth elements (REEs) from end-of-life (EOL) products represents a strategic opportunity to strengthen the domestic supply chain for rare earth elements. This work presents a superstructure-based optimization framework for finding the most economical processing pathway for different EOL rare earth permanent magnets (REPMs). The framework evaluates state-of-the-art technologies across four processing stages—disassembly, demagnetization, leaching and extraction, and precipitation and calcination—using net present value (NPV) maximization and cost of recovery (COR) minimization objectives. A novel bottom-up costing framework for hydrogen decrepitation is also introduced. Two feedstocks were considered: REPMs from EOL hard disk drives (HDDs), and electric and hybrid electric vehicles (EVs and HEVs). While HDD recycling proved unprofitable due to limited feedstock availability, EVs/HEVs were profitable across a range of parameters and cost estimates. Therefore, our findings suggest that the proposed EOL EV/HEV recycling process may be economical and is worthy of further investigation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

3D Characterization of the Columnar-to-Equiaxed Transition in Additively Manufactured Inconel 718

Additive manufacturing (AM) provides enormous processing flexibility, enabling novel part geometries and optimized designs. Access to a local heat source further permits the potential for local microstructure control on the scale of individual melt pools, which can enable local control of part properties. In order to design tailored processing strategies for target microstructures, models predicting the columnar-to-equiaxed transition must be extended to the high solidification velocities and complex thermal histories present in AM. Here, we combine 3D characterization with advanced modeling techniques to develop a more complete understanding of the solidification process and evolution of microstructure during electron beam melting (EBM) of Inconel 718. Full calibration of existing microstructure prediction models demonstrates the differences between AM processes and more conventional welding techniques, underlying the need for accurate determination of key parameters that can only be measured directly in 3D. The ability to combine multisensor data in a consistent 3D framework via data fusion algorithms is essential to fully leverage these advanced characterization approaches. Thermal modeling provides insight on microstructure development within isolated solidification events and demonstrates the role of Marangoni effects on controlling solidification behavior.

Polonsky, Andrew↗
DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A comparison of control strategies for wave energy converters

In this study, we employ a numerical model to compare the performance of a number of wave energy converter control strategies. The controllers selected for evaluation span a wide range in their requirements for implementation. Each control strategy is evaluated using a single numerical model with a set of sea states to represent a deployment site off the coast of Newport, OR. A number of metrics, ranging from power absorption to kinematics, are employed to provide a comparison of each control strategy’s performance that accounts for both relative benefits and costs. The results show a wide range of performances from the different controllers and highlight the need for a holistic design approach which considers control design as a parallel component within the larger process WEC design.

13 HYDRO ENERGY↗
DOE OSTI2022

The interplay between vapour, liquid, and solid phases in laser powder bed fusion

The capability of producing complex, high performance metal parts on demand has established laser powder bed fusion (LPBF) as a promising additive manufacturing technology, yet deeper understanding of the laser-material interaction is crucial to exploit the potential of the process. By simultaneous in-situ synchrotron x-ray and schlieren imaging, we probe directly the interconnected fluid dynamics of the vapour jet formed by the laser and the depression it produces in the melt pool. The combined imaging shows the formation of a stable plume over stable surface depressions, which becomes chaotic following transition to a full keyhole. We quantify process instability across several parameter sets by analysing keyhole and plume morphologies, and identify a previously unreported threshold of the energy input required for stable line scans. The effect of the powder layer and its impact on process stability is explored. These high-speed visualisations of the fluid mechanics governing LPBF enable us to identify unfavourable process dynamics associated with unwanted porosity, aiding the design of process windows at higher power and speed, and providing the potential for in-process monitoring of process stability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mapping the pathways of photo-induced ion migration in organic-inorganic hybrid halide perovskites

Abstract Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications.

14 SOLAR ENERGY↗
DOE OSTI2023

Improved fabrication of scalable hybrid silicon nitride–thin-film lithium niobate electro-optic modulators

Strategies for improved fabrication of integrated thin-film lithium niobate electro-optic (EO) Mach–Zehnder modulators (MZMs) are reported using scalable processes and designs. The MZM devices utilize direct bonding of unetched and unpatterned thin-film lithium niobate to patterned and planarized silicon photonic microchips. The latter contains silicon nitride waveguide structures of various widths that are used to form hybrid modes that are suitable for high-bandwidth low-voltage EO modulators based on Pockels effect. We report that the incorporation of appropriately designed outgassing channels and certain modifications to key processing steps helped achieve a greater than 99% reduction in void density during bonding. Void reduction is critically important for these traveling-wave hybrid MZM devices in which the optical mode is controllably distributed between multiple thin layers and propagates over millimeter-scale lengths.

Mere, Viphretuo↗