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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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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Impact of electrode thick spot irregularities on polymer electrolyte membrane fuel cell initial performance

Polymer electrolyte membrane fuel cells have great potential for power generation in many applications, but to implement them on a larger scale, understanding of quality requirements, tolerances, and controls during manufacturing must improve. As part of this broad challenge, irregularities resulting from the membrane electrode assembly production process must be studied to determine if they impact the performance of the cell. One potential irregularity is a locally increased thickness of the coated or deposited electrode. In order to understand and provide insight into the impact of such irregularities, we intentionally created thick spots in the center of cathode electrodes. The thick spots fabricated by two different approaches were carefully characterized by optical microscopy, X-ray fluorescence spectroscopy and scanning electron microscopy (SEM). We found that the impact of the thick spots on the electrochemically active catalyst area and mass activity was minimal. However, specific electrode thick spot irregularities significantly impacted the mass transport properties of the cells. Based on SEM analysis, the likely cause of the performance drop is morphological changes associated with the creation of the thick spot irregularity. Spatial performance results obtained with a segmented cell system further indicated that the area of the performance impact exceeded that of the intentionally created thick spot.

25 ENERGY STORAGE↗

DOE OSTI2020

Modeling batch melting: Roles of heat transfer and reaction kinetics

Development of mathematical models of heat and mass transfer in glass-melting furnaces began in the 1970s and progressed rapidly with advances in sophisticated experimental/numerical techniques and increasing computational power. Today, practically all newly built or rebuilt furnaces are optimized with these models to meet stringent quality requirements, reduce the unit costs of manufacturing, or control emissions. One remaining hurdle is to model the batch-to-glass conversion accurately enough to reliably assess the glass production rate. This article summarizes two key aspects of the batch-conversion modeling—the heat transfer and the kinetics of conversion—and reviews the current state-of-the-art approaches to simulating them. We critically examine the advantages of the commonly used heat transfer approach, but also explain that its predictive capabilities are significantly restricted by the dependence of batch thermal properties on the time-temperature history. We argue that kinetic approaches to the batch-conversion modeling would offer a significant improvement when coupled with the heat transfer approach. Finally, we summarize key areas requiring further research on the way toward a realistic model of the batch blanket.

Pokorny, Richard↗

DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Eco-Friendly High-Performance Carbon Building Material Development from Coal

The development of eco-friendly high-performance building materials based on coal-derived materials is greatly beneficial for promoting environmental sustainability and the coal industry. This project focused on advancing technology to create innovative and scalable construction materials from two domestic sources: (1) coal-derived pyrolyzed char (PC), and (2) solvent-extracted coal deposit, extracts, and residue (CDER). It aimed to simultaneously develop and produce two types of coal-derived building products: (1) char-based concrete bricks (CCB) for wall construction in buildings, and (2) carbon-based structural units (CSU) intended as alternatives to traditional wood, concrete, or steel framing structures. The project team has successfully developed methods for manufacturing CCB samples. Extensive experiments were conducted to assess various manufacturing techniques for these samples. This included measuring their physical properties such as thermal conductivity (ranging from 0.26 to 0.38 W/mK) and bulk density (ranging from 0.85 to 1.15 g/cm 3 ), along with their mechanical properties (e.g., compressive strength ranging from 14 to 16 MPa at 28 days) for samples with a PC composition of 70%. Quality assurance and quality control tests, focusing on density and compressive strength, were also performed on samples selected randomly from pilot-scale production, and cured for a long term (over three months). The average long-term compressive strength of two randomly selected CCB samples was measured as 15.1 MPa, which was higher than the required target strength of 14 MPa. This indicates that the manufactured CCB samples from the preliminary pilot scale production run have good quality control and long-term strength performance.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Development of Quality Control Methods for Robust and Reliable Sensor Design

The successful adoption of additive manufacturing for the rapid prototyping of printed sensors requires the establishment of quantifiable metrics that can interrogate device performance. One measure of performance is the adhesion strength between the substrate and sensor interface, which is critical since the strength of this interface can dictate the accuracy and reliability of the printed sensor. In this work, a non-contact laser-induced spallation technique was used to quantify the adhesion of silver prints on an aluminum alloy substrate. The laser-based method was compared to a standardized pull-off adhesion test, which provided baseline measurements of adhesion strength. The sintering conditions were shown to play an important role in the film-substrate adhesion strength, as well as the cohesion and ductility of the film itself. The methods and results described in this work supports the establishment of process control steps that are necessary for verifying the reliability of printed devices prior to its deployment in critical experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Advancing Battery Manufacturing: Synchrotron Characterization for Industry

Large-scale battery manufacturing requires understanding the fundamental principles of materials and interfaces and relies on advanced techniques for detailed interrogation. Despite advancements in the industrial scale production and their associated quality control tools, challenges such as electrode heterogeneity, internal defects, and largescale material waste (e.g., scrap) can hamper manufacturing. Synchrotron X-ray characterization techniques offer spatial, temporal, and chemical resolution that can provide diagnostic insights for metrology across various manufacturing steps. This review examines the use of synchrotron tools to advance understanding of key steps in the battery manufacturing process. Recent examples demonstrate how synchrotron methods resolve manufacturing challenges and uncover degradation pathways that are otherwise inaccessible. Future directions for advancing battery manufacturing emphasize collaboration between academia and industry through the use of synchrotron X-ray techniques.

Batteries↗

DOE OSTI2025

The AMACStar ASIC for the HL-LHC ATLAS ITk Strip detector: design, verification, testing, and quality assurance

For the high-luminosity upgrade to the LHC (HL-LHC), the ATLAS detector at CERN requires an all-new inner detector, the Inner Tracker (ITk). The ITk Strip subdetector is made up of silicon modules, which include three types of radiation-hard ASICs. One of these is the Autonomous Monitor and Control (AMAC). The AMAC is manufactured by Global Foundries using 130 nm CMOS8RF DM technology and is approximately 3 by 5 mm in size. This ASIC autonomously monitors the temperatures, voltages, and currents in the module components while controlling critical values in order to prevent these quantities from reaching dangerous levels. The final design, AMACStar, was verified, tested, and ensured to perform all necessary functions. A quality control procedure using an in-house probe station set-up was developed in order to ensure that every individual chip on each wafer of AMACStars required by the ITk Strip project met performance requirements. The average per wafer yield of usable AMACStars is 92.33%, exceeding the design-specific 90% yield estimated for project costing. This estimate was based on actual yields for similar designs in this process.

Analogue electronic circuits↗

DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Request for Information on Establishing a New Manufacturing Institute (DE-FOA-0002564)

Deep decarbonization of major industries such as metals manufacturing requires extensive process integration and controls to manage feedstocks, side reactions, heat, water, and waste streams. The scale of the energy and capital investment requires that process integration be validated to a high level of confidence with no bias. Industries such as steel production have very thin profit margins. Therefore, lack of confidence in process integration, product quality, and economics is a major deterrence to changes in manufacturing capital investment. Public sector investment in reconfigurable pilot testbeds and a first-of-a-kind plant would be necessary to de-risk technical and financial barriers prior to industry-wide buildout. DOE should consider a National Lab-led hub for the testbeds. Lab testbeds could be utilized in campaigns and allow multiple industrial partners to evaluate and validate technologies prior to making major capital investments.

08 HYDROGEN↗

DOE OSTI2024

Catalyst Layer Design, Manufacturing and In-line Quality Control

In this project we successfully demonstrated the capabilities of the Reactive Spray Deposition Technology (RSDT) to fabricate large-scale CCMs for advanced PEMWEs that have one-order of magnitude lower PGM loading in their catalyst layers, and performance comparable with the commercial state-of-the-art CCMs. The RSDT is a unique methodology that combines the catalyst synthesis and CCM fabrication in one step and reduces dramatically the time for CCM manufacturing. As fabricated large-scale CCMs with geometric area of 680 cm2 demonstrated excellent activity and durability performance, and the novel duo-recombination layer design paves the way for solving the safety concerns related to PEMWEs. In addition, excellent activity and durability performance has been demonstrated with RSDT fabricated CCMs with thinner membranes and duo RL design. This is a novel approach for further performance improvement of the MEAs for PEMWEs that has been successfully demonstrated for the first time in this project. The integration of the in-situ laser diagnostics system along with the in-line optical quality control system within the RSDT that has been achieved and demonstrated in this project, is an example for possibility of designing and building advanced manufacturing technologies that can meet the requirements of the future manufacturing. Therefore, the RSDT offers a precise real-time monitoring and control of the particles size, composition, loading, porosity, thickness, and defects in the catalysts’ layers, which render this technology as the best candidate for manufacturing of cost effective CCMs for PEMWEs. By using RSDT we successfully met all project’s milestones, Go/No-Go decision, objectives, goals, and deliverables.

08 HYDROGEN↗

DOE OSTI2024

TRISO Fuel’s Safety Functions, Contributions to Reactor Safety, and Necessary Safety Limits

Safety functions are the actions, passive or active, that structures, systems, and components of nuclear facility that contribute to the safety of the workers, the public, or the environment. Well-defined safety functions are the foundation of a solid safety case for a reactor. For reactors that use TRISO-coated particles, the TRISO fuel plays an important part of the safety case because of its ability to contain radionuclides in the fuel itself. This ability enables the use of a functional containment strategy for the reactor where radionuclide retention is the primary safety function supported by the safety functions of controlling reactivity control and controlling heat rejection. This paper establishes at a deeper level the role that TRISO fuel plays in each of these safety functions and associated quality assurance and testing requirements for TRISO particle manufacturing to ensure these safety functions. Safety limits necessary to protect these safety functions include: operational limits, time at temperature limits, and fission gas release activity limits. In conclusion, this approach demonstrates the role that specific aspects of TRISO fuel play in protecting the safety of workers, the public, and the environment.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Automated Manufacturing of Grid Stiffened Panels with Radically Reduced Tooling

Grid stiffened continuous fiber reinforced composite panels are an attractive option for creating lightweight structures due to the tailorability for various applications and the resulting high specific properties. However, the panel stiffeners and stiffener intersections result in high tooling complexity and correspondingly high cost of implementation. These factors have limited the impact of such structures in the composites industry. Previous research has demonstrated the ability to produce high quality, high aspect ratio beams, representative of individual grid stiffeners, using E-glass/PET comingled tow via direct digital manufacturing. Further, prior preliminary efforts have demonstrated the potential to use the same approach to manufacture grid intersections that have continuous fiber in both directions. To expand on the previous efforts in grid stiffeners produced by direct digital manufacture with radically reduced tooling requirements, this effort compares two methods of providing positioning and consolidation, nozzle vs. roller. Both processes are based on a commingled yarn feedstock. The extrusion through a nozzle has been shown to enable grid intersection control through local variations in applied consolidation and serves as the baseline process. However, this approach requires a continuous placement path to create the complete grid stiffened panel as no mechanism for cutting and restarting has been implemented. Alternatively, a newly developed placement head incorporating cut and refeed, mounted to a 6-axis robot, offers the potential of improved path placement efficiency. The two techniques are used to produce similar grid composite stiffeners to evaluate the effectiveness of producing the grid intersections. Rate of deposition of the two end effectors are compared and the quality of the associated grid stiffeners, and intersections, are determined through measurement of geometry, fiber volume fraction and void fraction.

Engineering↗

DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗

DOE OSTI2023

Extrusion parameter control optimization for DIW 3D printing using image analysis techniques

Material extrusion is a well-recognized facet of additive manufacturing that involves the fabrication of parts through the deposition of structural material from an extrusion head from a bulk supply. In the subdivision of Direct Ink Writing (DIW) additive manufacturing, challenges arise when the structural material is flowable, synchronous extrusion control and tool movement becomes critical for achieving high-quality parts with low defect populations. DIW techniques are most used in laboratory settings using expensive custom instruments and may require specialized 3D slicing software. Here, in this study, the fabrication of an inexpensive, consumer-friendly progressive cavity pump dispensing system is detailed, in which can create high-quality parts by executing G-code commands produced from a commercial slicing software. The precision and repeatability of the movement-synchronized material extrusion is demonstrated through a series of optimization schemes, entailing the alteration of various control parameters, which directly affect the extrusion properties demonstrated during a print. In situ diagnostics were implemented to evaluate the results of the established optimization experiment. Using a machine vision technique, images of the optimization prints are processed. Following this, a supervised machine learning model was trained to autonomously judge whether or not the extrusion parameters produced a passing or failing result. The machine learning scheme serves as a preliminary benchmark for future layer-by-layer evaluation of more complex DIW parts. The construction of the printer and development of in situ characterization capabilities demonstrates the ability for this printer to create high-fidelity DIW parts for a fraction of the price of other systems.

42 ENGINEERING↗

DOE OSTI2023

Nondestructive property and defect characterization using X-rays and neutrons

As advanced manufacturing (AM) continues to mature as a fabrication technique, interest in its use for the fabrication of nuclear components continues to grow. AM nuclear parts offer the ability to create parts with complex and non-standard geometries that cannot be produced using traditional manufacturing techniques, circumvention of supply chain issues, and reduction of time from design to implementation. However, components fabricated with AM techniques must undergo nondestructive examination (NDE) to ensure they are fabricated to the required specifications to ensure safe and proper operation. The Advanced Materials and Manufacturing Technologies (AMMT) program has undertaken initial exploratory studies on several NDE techniques to evaluate their feasibility for research and development (R&D), as well as Quality Assurance and Quality Control (QA/QC), and in-service inspections of AM parts. This work describes research results on several techniques, including X-ray and neutron tomography and scattering, as well as photothermal radiometry. The experimental results are described and an overview for each techniques’ potential use on AM parts in the various phases of part development and lifetime is given. Finally, future directions for technique development and application to AM nuclear components are described.

36 MATERIALS SCIENCE↗

DOE OSTI2022

High-Silicon Steel Strip by Single-Step Shear Deformation Processing

It is well known that Fe-Si alloys with Si content higher than in conventional electrical sheet steels (>3.2% Si) can make a significant impact in improving the efficiency of electrical motors if they are available in sheet/foil (strip) forms at suitable cost. While the magnetic and electrical attributes (e.g., resistivity, core loss) of these high-Si Fe alloys, of relevance to electrical motor core laminations, are known to be exceptional, the alloys have limited workability, making them difficult to produce consistently in sheet/foil (strip) forms. Current processing techniques such as rolling, while adequate for producing conventional electrical steel sheet, do have important disadvantages - large energy consumption and emissions, limitations in processing of low-workability alloys (e.g., high-Si content steels), large-scale plant infrastructure, and less than adequate capability to engineer sheet metals with specific microstructures (e.g., fine-grained) and crystallographic textures (e.g., shear textures). It is therefore of interest to have an alternative commercial process that can produce sheet/foil (strip) from high-Si Fe alloys and which can also overcome some of the deficiencies of current multistage strip processes. The goal of the present project was design and demonstration of a new energy-efficient pilot process for producing high-Si electrical steel strip of commercial widths and thickness, and with superior electrical and magnetic properties than current electrical steels (Fe-3.2% Si as benchmark). The applications domain for these steels is electrical motor core laminations. We have addressed this goal by accomplishment of the following specific objectives and tasks: a) Development of an Fe-4Si-4Cr alloy with electrical resistivity >80 μΩ-cm, induction flux density >1.48 T at 5000 A/m and core loss 35% lower than the benchmark 3.2% Si alloy. The alloy which meets DOE target specifications for motor core attributes was designed with the Si content controlled for the electrical properties and the Cr content tailored to meet material/process workability requirements. b) A unique machining-based deformation processing system was designed and scaled-up to produce strip of commercial width (25 mm to 150 mm) and thickness (up to 0.5 mm) from the Fe-4Si-4Cr alloy and other alloys of varied workability including copper, Al6061-T6 and naval brass. The key attributes of the machining-based strip production are deformation processing by concentrated simple-shear; single-step production of strip from ingot using compact machine infrastructure; strip surface finish of Ra 0.35 to 1 micrometer that is comparable/superior to that of rolled strip; discrete production of strip that can potentially be done at point of use; and controllability of strip mechanical/formability properties by deformation control. c) The electrical, magnetic, surface quality, mechanical, formability, and metallurgical properties/attributes of the machining-based strip were established by direct ASTM standard or equivalent measurement techniques. d) Punching characteristics of the strip in terms of load, edge quality and macro defects were similar to those of conventional 3.2% Si electrical steels. These punching characteristics are critical from a manufacturability perspective for motor/transformer core applications. e) A modeling framework for energy analysis of multistage rolling and the machining-based deformation processing has been established. Application of this modeling to the two strip-processes showed that the machining-based process requires significantly lower specific energy for processing, ~ 25% of that for rolling. The modeling framework can be adapted for a range of sheet-metal forming, bulk metal forming, and machining processes. It can be used to identify key parameters controlling process specific energy. f) A comparative analysis of advantages and disadvantages of machining-based processing against rolling for strip production. The single stage machining-based processing, with compact infrastructure, represents a new manufacturing paradigm for sheet and foil manufacturing that can potentially also be applied to advanced titanium, aluminum, copper and magnesium alloys. The goals and objectives were accomplished by a cross-disciplinary project team comprising of personnel from Purdue University; M4 Sciences LLC, a small-business focused on advanced manufacturing technology development; the Pacific Northwest National Labs; and tool manufacturers. The team is currently in advanced discussions with multiple entities for future process development for commercialization.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Establishing an acoustic-property relationship in laser powder bed fusion with machine learning

Quality control of Laser Powder Bed Fusion (PBF-LB) additively manufactured parts is an important hurdle inhibiting the technology’s use structural applications. Acoustic monitoring of the laser powder bed fusion process can detect defects in-situ that are known to degrade mechanical properties. However, processing-structure-property (PSP) relationships are required to extrapolate from detected defects to part performance. Here, this study explores how acoustics may be a suitable signature linking processing conditions to properties, thus effectively substituting for structure in the PSP relationship. Establishing such a relationship would enable a part’s mechanical performance to be directly predicted from its acoustic signature, reducing the need for destructive testing or microstructural analysis to ensure a part will meet performance requirements. One hundred CoCrFeMnNi high entropy alloy tensile bars were printed across 13 process conditions in a series of 6 prints. The acoustic signatures of these tensile bars were used to train machine learning models to predict each part’s mechanical properties. By using both process information and acoustic information to predict mechanical properties, yield strength was predicted 18% more accurately and ductility to failure was predicted 10% more accurately than is achieved when using duplicate parts to predict part performance. Finally, individual acoustic frequencies were investigated to determine why acoustic signatures improve mechanical property predictions and the potential physical origins of these signatures. This work demonstrates how blending acoustics, process information, and machine learning can provide in-situ diagnostics of mechanical properties and improve the reliability of the PBF-LB process.

Acoustic emission↗