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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ASME Code Qualification Plan for LPBF 316 SS

This report describes a plan to qualify laser powder bed fusion (LPBF) 316 stainless steel for use with the American Society of Mechanical Engineers (ASME) Boiler & Pressure Vessel Code Section III, Division 5 rules for metallic components in high temperature nuclear reactors. Accomplishing this goal would make the material and manufacturing process available to vendors for inclusion in the next generation of advanced, high temperature reactors. The general approach adopted here is to treat LPBF 316 as if it was a completely new material and to develop a plan to qualify the material according to the current ASME practices. One key goal of this work is to explore and develop accelerated qualification approaches that might reduce the time required to qualify new materials by reducing the need for long term testing. However, the qualification plan here does not employ any accelerated qualification approaches to provide a limiting, bounding description of the number, duration, and types of testing required to qualify LPBF 316 without such techniques and to describe a comprehensive dataset that could be used to explore and validate accelerated qualification approaches in the future. The report addresses the fundamental challenges to qualifying Advanced Manufacturing (AM) materials for high temperature applications and summarizes the ASME Section III qualification process as well as current efforts to qualify LBPF and DED 316 for low temperature applications. The report then discusses specific issues, both material and logistical, related to qualifying PBF 316 steel. The final chapters of the report describe a complete test plan designed to generate sufficient data to qualify the material as well as a data management plan for how to store and manage the data to eventually provide the test data packaged needed to qualify the material with ASME.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomistic modeling of tensile deformation and fracture of carbon fibers: Nanoscale stress redistribution, effect of local structural characteristics and nanovoids

The mechanisms of tensile deformation and fracture of carbon fibers are investigated in large-scale molecular dynamics simulations. The simulations performed for computational samples with different structural characteristics enable analysis of the structural sensitivity of the mechanical properties. It is found that the carbon fiber nanostructure can be represented by a fine (nanoscale) mixture of stiff and soft regions. Despite the nanoscale structural heterogeneity of the computational fibers, their fracture strain and tensile strength exceed the experimental values by almost an order of magnitude. Here, this observation is explained by the ability of the carbon fiber nanostructure, largely consisting of curved and folded layers of turbostratic carbon, to exhibit continuous nanoscale load redistribution during the deformation. The transient, fluid nature of a percolating cluster of stress-holding structural elements prevents stress localization at the early stage of the deformation and enables the fiber nanostructure to adapt to the increasing load. Overall, the results of the simulations suggest that the design of advanced carbon fiber manufacturing and post-processing methods focused on the elimination of critical mesoscopic structural defects (internal voids, foreign inclusions, and surface flaws) can yield a substantial increase in the strength and fracture strain of carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-time laser ultrasonic monitoring of laser-induced thermal processes

Abstract Intra- and inter-layer integrity of components fabricated with advanced manufacturing techniques, such as laser powder bed fusion, is dependent upon rapid heating, melting, and solidification processes. There is a need for new techniques to provide in situ feedback of these processes. Here a laser-based ultrasonic technique to probe thermal effects induced by a high-power continuous wave laser in titanium samples is described. Numerical simulations were performed to show that, for a spatially uniform heating beam, laser-induced surface acoustic waves are strongly influenced by surface heating conditions, are dispersive in the case of rapid heating, and that an abrupt velocity reduction happens upon the onset of surface melting. Furthermore, laser-based ultrasound experimental results which monitor the transient change of surface wave travel time associated with high power laser surface heating are provided. A pulsed laser is used to generate high frequency surface acoustic waves that propagate through the laser-heated region and are detected using a photorefractive crystal-based interferometer. Qualitative agreement is observed between theory and experiment with both showing a rapid reduction in the surface wave velocity at the onset of illumination and further decrease in surface wave velocity associated with melting. It is demonstrated that changes in the surface wave velocity can be used to track local heating and detect the onset of surface melting in real time.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Materials Scorecards, Phase 1: Advanced Materials and Manufacturing Technology

This report presents preliminary material score cards (Phase 1) based on a survey of industry experts and the limited literature available on the state of advanced manufacturing of materials for nuclear applications. Seven reactor types were considered, and the data is presented in tabular form. The caveat is that the scores are based on limited data and expert judgement. Considerable work remains to be done to prioritize materials for advanced manufacturing to support existing and advanced reactors. A central database of processing conditions, resulting microstructure, post processing, properties, and performance of these materials is needed to support the accelerated adoption of advanced manufacturing by the industry.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Creating microfluidic channels functionalized with micro- and nano-scale features via femtosecond laser surface processing

We demonstrate the ability to create functionalized microfluidic channels using femtosecond laser surface processing (FLSP). FLSP is an emerging advanced manufacturing technology used to modify the surface properties of materials directly and permanently by producing self-organized quasi-periodic micro- and nano-scale surface features along with surface and subsurface chemical and grain structure changes. We demonstrate on Hastelloy X that by controlling the laser fluence and pulse count, the depth of the microchannels and height of the FLSP microstructures within the microchannels can be controlled independently.

Ultrashort pulse laser applications, micro- and na↗
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↗
DOE OSTI2021

Regulatory Development for Advanced Manufacturing

Advanced Methods of Manufacturing (AMM) are new, novel manufacturing processes that have the potential to bring significant cost and schedule reductions to many industries, including the nuclear industry. These methods produce components faster and cheaper than many traditional methods. This includes replacing obsolete components at currently operating plants as well as manufacturing components of all sizes for advanced reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Tuning crosslinking of hybrid preceramic polymers in vat photopolymerization toward controlled ceramic yields

Control of preceramic polymer crosslinking for UV-curable processing is essential for fine 3D printing with high ceramic conversion for sustainable polymer-derived ceramics (PDC) engineering. While various factors influencing ceramic yield have been studied, the systematic exploration of the relationship between crosslinking and ceramic yield, especially when crosslinking increases volatile elements, remains open for further investigation. This study addresses this gap by utilizing vat photopolymerization (VP) additive manufacturing (AM) as a versatile platform for controlling preceramic crosslinking and ceramic yield. By rationally designing and tuning the photochemical crosslinking through digital light processing (DLP), we demonstrate that the ceramic yield can be enhanced from 64% to over 86%, even with added volatile elements. We reveal that the post-pyrolysis ceramic yield can be closely correlated with the pre-pyrolysis crosslinking of the preceramic network represented by its stiffness. This correlation thereby suggests a fast, energy-efficient, non-destructive methodology to predict and improve ceramic yield. Combined with these findings, our one-pot thiol-ene hybridization of polycarbosilane and polycarbosiloxane via DLP offers an exemplary method to generate hybrid preceramic polymers with tailored material properties toward target applications, and potentially even higher ceramic yields. This study thus contributes to achieving better resource- and energy-efficient preceramic polymer and PDC processing routes toward sustainable, advanced organic–inorganic materials manufacturing.

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

Quench process modeling & simulation in the heat-treatment of critical aerospace components

To manufacture light-weight, advanced metal alloy components for gas turbine engines, quench heat-treatment processes are typically used. By quenching the component from elevated temperatures, the alloy sometimes undergoes a solid-state phase transformation which produces special microstructures with the required, enhanced mechanical properties. However, the quenching can also lead to cracks forming in the component. Addressing the quench cracking problems adds a significant burden to the cost, schedule, and energy demand of manufacture. Currently, optimizing the quench process to mitigate or avoid the cracking is performed largely by trial-and-error, relying heavily on costly experimental (thermocouple)trials to understand the local thermal gradients which cause the cracks to form. In this first part (Phase 1) of the work, high-performance computing is employed to establish the ability of modern CFD (computational fluid dynamics) to alleviate or wholly replace the experimental quenching trials by virtual testing. A Baseline CFD model is defined and its accuracy established to be comparable to(and which usually exceeds) the accuracy of existing HTC (heat-transfer coefficient) based simulation methods of quenching. As a first-principles based approach, “calibration” of the Baseline CFD model is independent of the quench process itself, but instead relies on the accuracy of the underlying (modeled),generic two-phase fluid processes which cannot be currently resolved by CFD for large, industrial-scale cases. A novel, high-fidelity DNS capability has been developed and verified to examine and further improve upon the mean-field closure submodels on which the Baseline CFD approach is based.1

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Quench process modeling & simulation in the heat-treatment of critical aerospace components

To manufacture light-weight, advanced metal alloy components for gas turbine engines, quench heat-treatment processes are typically used. By quenching the component from elevated tempera-tures, the alloy sometimes undergoes a solid-state phase transformation which produces special microstructures with the required, enhanced mechanical properties. However, the quenching can also lead to cracks forming in the component. Addressing the quench cracking problems adds a significant burden to the cost, schedule, and energy demand of manufacture. Currently, optimizing the quench process to mitigate or avoid the cracking is performed largely by trial-and-error, relying heavily on costly experimental (thermocouple) trials to understand the local thermal gradients which cause the cracks to form. In this first part (Phase 1) of the work, high-performance computing is employed to establish the ability of modern CFD (computational fluid dynamics) to alleviate or wholly replace the experimental quenching trials by virtual testing. A baseline CFD model is defined and its accuracy established to be comparable to (and which usually exceeds) the accuracy of existing HTC (heat-transfer correlation) based simulation methods of quenching. As a first-principles based approach, “calibration” of the Baseline CFD model is independent of the quench process itself, but instead relies on the accuracy of the underlying (modeled), generic two-phase fluid processes which cannot be currently resolved by CFD for large, industrial-scale cases. A novel, high-fidelity DNS capability has been developed and verified to examine and further improve upon the mean-field closure submodels on which the Baseline CFD approach is based.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Manufacture of Carbon Foam in Continuous Process at Atmospheric Pressure

CFOAM LLC has been manufacturing vitreous carbon foam from coal in panel form for several years. The process is currently set up in batch mode as two steps, where the first step is conducted at very high pressure. In order to reduce cost and increase capacity, a new process for making carbon foam is being developed that can be performed at atmospheric pressure. This advance in turn would enable the development of a continuous manufacturing process, which is also being pursued. Both carbon foam panels and light weight aggregates are being investigate. Progress on the evolution of the pilot scale manufacturing plant and initial results of processing, microstructure, properties and performance of carbon foam products made using this new process will be discussed. This project is being supported by a cooperative agreement with the Department of Energy – National Energy Technology Laboratory (DE-FOA-0002185, Area of Interest 4: Coal-Derived Carbon Foam Produced via a Continuous Process, award number DE-FE-0031992).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Manufacture of Carbon Foam in Continuous Process at Atmospheric Pressure

CFOAM LLC has been manufacturing vitreous carbon foam from coal in panel form for several years. The process is currently set up in batch mode as two steps, where the first step is conducted at very high pressure. In order to reduce cost and increase capacity, a new process for making carbon foam is being developed that can be performed at atmospheric pressure. This advance in turn would enable the development of a continuous manufacturing process, which is also being pursued. Both carbon foam panels and light weight aggregates are being investigate. Construction of the pilot scale manufacturing plant has been completed and results of processing, microstructure, properties and performance of carbon foam products made using this new process will be discussed. This project is being supported by a cooperative agreement with the Department of Energy – National Energy Technology Laboratory (DE-FOA-0002185, Area of Interest 4: Coal-Derived Carbon Foam Produced via a Continuous Process, award number DE-FE-0031992).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Manufacture of Carbon Foam in Continuous Process at Atmospheric Pressure

CFOAM LLC has been manufacturing CFOAM® vitreous carbon foam from coal in panel form for several years. The process is currently set up in batch mode as two steps, where the first step is conducted at very high pressure. In order to reduce cost and increase capacity, a new process for making carbon foam is being developed that can be performed at atmospheric pressure. This advance in turn would enable the development of a continuous manufacturing process, which is also being pursued. Both carbon foam panels and light weight aggregates are being investigate. Progress on the evolution of the pilot scale manufacturing plant and initial results of processing, microstructure, properties and performance of carbon foam products made using this new process will be discussed. This project is being supported by a cooperative agreement with the Department of Energy – National Energy Technology Laboratory (DE-FOA-0002185, Area of Interest 4: Coal-Derived Carbon Foam Produced via a Continuous Process, award number DE-FE-0031992).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Recent developments in 3D-printed membranes for water desalination

The recognition of membrane separations as a vital technology platform for enhancing the efficiency of separation processes has been steadily increasing. Concurrently, 3D printing has emerged as an innovative approach to fabricating reverse osmosis membranes for water desalination and treatment purposes. This method provides a high degree of control over membrane chemistry and structural properties. In particular, when compared to traditional manufacturing techniques, 3D printing holds the potential to expedite customization, a feat that is typically achieved through conventional manufacturing methods but often involves numerous processes and significant costs. This review aims to present the current advancements in membrane manufacturing technology specifically tailored for water desalination purposes, with a particular focus on the development of 3D-printed membranes. A comprehensive analysis of recent progress in 3D-printed membranes is provided. However, conducting experimental work to investigate various influential factors while ensuring consistent results poses a significant challenge. To address this, we explore how membrane manufacturing processes and performance can be effectively pre-designed and guided through the use of molecular dynamics simulations. Finally, this review outlines the challenges faced and presents future perspectives to shed light on research directions for optimizing membrane manufacturing processes and achieving optimal membrane performance.

3D printing membranes↗
DOE OSTI2025

Machine learning-driven design and self-sensing capabilities of automotive bumper lattices for adaptive impact response

We present a novel approach to design an automotive bumper energy absorber using carbon fiber reinforced polymer composites, optimized to meet conflicting performance requirements for two distinct impact scenarios. The design must satisfy both a low-speed (2.5 mph) pendulum intrusion test, simulating vehicle-to-vehicle collisions, and a high-speed (25 mph) leg flexion test, replicating pedestrian impacts. These tests demand opposing deformation characteristics: high flexibility (deformation < 85 mm) for the former and high stiffness (deformation < 22 mm) for the latter. To address these contradictory requirements, we developed a machine learning (ML) framework for inverse optimization of lattice designs and material selection. Unlike traditional iterative design processes, our ML model directly outputs optimal design parameters and material choices based on target performance inputs. The energy absorber was fabricated using advanced additive manufacturing techniques, including extrusion deposition and digital light processing. The integration of carbon fibers provides multifunctionality to the bumper structure, enabling self-sensing capabilities through changes in electrical resistivity under compression. This electrical response demonstrates high repeatability under multiple cycles at 2% compression and exhibits distinct signatures during crack formation under high deformation. This research offers adaptive performance through innovative design methodologies and smart material integration. The approach has potential applications in various fields requiring adaptive energy absorption and real-time structural health monitoring.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2020

Manufacturing Process Design of a Micro-Scale Liquid-Liquid Extractor and Multi-phase Separator

Advances in process innovation within the chemical process industry are impeded by the cost of producing new innovative components. In this paper, various manufacturing process routes were evaluated for scaling up a micro-scale liquid-liquid extractor and multi-phase separator capable of separating immiscible phases that are used for the extraction of a chemical product into a lower boiling point solvent to make downstream distillation easier. A manufacturing process design methodology was used to evaluate both photochemical machining and binder jetting as a means for producing the micropost arrays within the separator. Cost curves show that for a 0.3 mm deep micropost array, photochemical machining was found to be much cheaper than binder jetting. However, because binder jetting processes enable microposts with over 13 times higher depth-to-width aspect ratios, binder jetting was found to decrease the overall costs of the extractor/separator by more than half for a 500 barrel per day plant, with breakeven as low as 15 barrels per day. These economics were confirmed by building extractor/separator plates and characterizing them dimensionally and functionally. The binder jet plates were found to perform better than photochemical plates leading to the complete separation of the organic phase from water. The utilization of slower sintering cycles combined with surface grinding provided acceptable dimensional tolerances for the binder jet plates. Using pressurized air during depowdering improved the economics for the binder jetting process.

42 ENGINEERING↗