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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimization Methodology of Pebble Bed HTGR Start-Up and Running-in Strategy

In recent years interest in advanced reactor technologies has increased significantly. However, the methods used for analysis of traditional nuclear reactors are insufficient to consider all the different and varied advanced reactor designs without further development. One promising advanced reactor design is the pebble bed reactor (PBR). PBRs possess unique operational and fuel cycle features that require the development of specific analysis methodologies to adequately design and analyze the systems. There is a need in PBR research for a capability to analyze and optimize the process of transitioning from the start-up reactor core to the equilibrium core (known as the “running-in” of the reactor). The start-up of a PBR and the transition to the equilibrium core is a complex, multi-physics challenge that has not yet been well researched and has many opportunities for design, analysis, and optimization of the process. In this research, a methodology is defined to consider the potential strategies in PBR start-up and run-in to the equilibrium core. Multiple candidate software are considered, and their pros and cons are discussed for the PBR-specific application in the methodology. A preliminary software selection for the physics engine is made, and initial verification of key modules is performed. Software to support optimization of the reactor run-in strategies through reduced order modeling (ROM) and machine learning are considered. Challenges for a full implementation of the methodology are discussed. Additional code selections and verification of models relevant to the application are needed before full demonstration of the methodology can be achieved and an optimal strategy determined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Systematic analysis of melt pool dynamics in laser processing of mixed powder feedstocks

Functionally graded materials (FGMs) fabricated via additive manufacturing of blended powders offer the potential to spatially tailor properties for new technologies, such as fusion first-wall systems, turbine blades, and spacecraft. However, processing these materials is difficult due to the multiplicity of processing parameters to optimize, all of which must be changed as substrate material, powder feedstock compositions, and melt pool dynamics evolve. Here, this work systematically evaluates the qualitative and quantitative effects of these variables on the melt pool size, shape, composition, and particle distribution in an exemplar Ti-Ta system, and connects the experimental results to Marangoni flow behavior and phenomena observed in other systems. Increasing laser power linearly increases melt pool size and layer thickness, driving engineering considerations such as part/geometrical tolerances. Decreasing laser velocity changes the melt pool shape from lenticular to convex and reduces chemical homogeneity due to extreme thermal and compositional gradients between the melt pool center and boundaries. Thermophysical property differences between the powder feedstock and substrate material, as well as the directionality of the gradient, affect dilution and melt pool dynamics, which in turn affect the melt pool boundary characteristics, shape, and uniformity. Mixed powder feedstocks of intermediate compositions do not behave according to linear interpolations between single-material endpoints, instead building taller and wider melt pools. As such, it is recommended to quantify process maps for at least one intermediate composition in the FGM or multi-material system of interest to ensure optimized processing parameters, predictable melt pool sizes and shapes, and compositional and spatial precision.

Dissimilar↗
NASA NTRS1990

Image gathering, coding, and processing: End-to-end optimization for efficient and robust acquisition of visual information

Researchers are concerned with the end-to-end performance of image gathering, coding, and processing. The applications range from high-resolution television to vision-based robotics, wherever the resolution, efficiency and robustness of visual information acquisition and processing are critical. For the presentation at this workshop, it is convenient to divide research activities into the following two overlapping areas: The first is the development of focal-plane processing techniques and technology to effectively combine image gathering with coding, with an emphasis on low-level vision processing akin to the retinal processing in human vision. The approach includes the familiar Laplacian pyramid, the new intensity-dependent spatial summation, and parallel sensing/processing networks. Three-dimensional image gathering is attained by combining laser ranging with sensor-array imaging. The second is the rigorous extension of information theory and optimal filtering to visual information acquisition and processing. The goal is to provide a comprehensive methodology for quantitatively assessing the end-to-end performance of image gathering, coding, and processing.

Huck, Friedrich O.↗
DOE OSTI2025

Understanding Structural and Compositional Evolution during NMC Cathode Direct Recycling via Solid-State NMR

Recycling end-of-life lithium-ion batteries (LIBs) to recover high-value cathode materials such as LiNixMnyCozO2 (NMC) is driven by economical, geopolitical, and sustainability needs. There has been recent interest in direct recycling methods to improve efficiency and recovery of materials, including ionothermal, hydothermal, solid-state, or redox mediator methods. In conjunction with recycling process development, detailed structural characterization is necessary in order to understand the mechanisms and efficacy of cathode recycling steps. Solid-state nuclear magnetic resonance (NMR) spectroscopy is a unique tool that can probe Li coordination, bulk and surface environments, and transition metal ordering in recycled and upcycled NMC cathodes. Here, 6,7Li, 1H, and 19F NMR spectroscopy to probe structural and compositional changes as well as surface impurities that may form during each step in NMC direct recycling is utilized. During relithiation, Li reinsertion into the NMC lattice is observed. During upcycling, where the goal is to increase the Ni content in the NMC, incorporation of Ni-rich phases into the bulk Li environment is observed. Surface impurities formed during processing were also identified. These studies provide valuable information for optimizing recycling processes to reach targeted cathode composition and structure that can enable electrochemical performance comparable to or better than pristine materials.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Parametric Cost Analysis: A Design Function

Parametric cost analysis uses equations to map measurable system attributes into cost. The measures of the system attributes are called metrics. The equations are called cost estimating relationships (CER's), and are obtained by the analysis of cost and technical metric data of products analogous to those to be estimated. Examples of system metrics include mass, power, failure_rate, mean_time_to_repair, energy _consumed, payload_to_orbit, pointing_accuracy, manufacturing_complexity, number_of_fasteners, and percent_of_electronics_weight. The basic assumption is that a measurable relationship exists between system attributes and the cost of the system. If a function exists, the attributes are cost drivers. Candidates for metrics include system requirement metrics and engineering process metrics. Requirements are constraints on the engineering process. From optimization theory we know that any active constraint generates cost by not permitting full optimization of the objective. Thus, requirements are cost drivers. Engineering processes reflect a projection of the requirements onto the corporate culture, engineering technology, and system technology. Engineering processes are an indirect measure of the requirements and, hence, are cost drivers.

Dean, Edwin B.↗
NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗
NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗
NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗
DOE OSTI2023

A complementarity-based vapor-liquid equilibrium formulation for equation-oriented simulation and optimization

Vapor-Liquid Equilibrium (VLE) is a cornerstone of computer aided process engineering (CAPE). Embedded within process system models, VLE calculations are inherently procedural with non-smooth behavior that frequently require discrete decisions. Traditionally, these features resist the incorporation of VLE within efficient, large-scale equation-oriented (EO) process simulation and optimization strategies. On the other hand, recent reformulation of VLE models through the incorporation of complementarity constraints has broadened its scope to deal seamlessly with phase transitions and even supercritical excursions in process simulation and optimization. In this study, we extend these VLE complementarity models to EO frameworks where procedural thermodynamic property libraries are still required. Here, we develop an efficient, non-intrusive, and intuitive “square-flash” equation system that has been implemented within the IDAES Integrated Platform (IDAES-IP). Further, the effectiveness of this modular approach is demonstrated on case studies for non-ideal flash calculations and distillation optimization with disappearing phases and supercritical transitions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Automated Fiber Placement of PEEK/IM7 Composites with Film Interleaf Layers

The incorporation of thin discrete layers of resin between plies (interleafing) has been shown to improve fatigue and impact properties of structural composite materials. Furthermore, interleafing could be used to increase the barrier properties of composites used as structural materials for cryogenic propellant storage. In this work, robotic heated-head tape placement of PEEK/IM7 composites containing a PEEK polymer film interleaf was investigated. These experiments were carried out at the NASA Langley Research Center automated fiber placement facility. Using the robotic equipment, an optimal fabrication process was developed for the composite without the interleaf. Preliminary interleaf processing trials indicated that a two-stage process was necessary; the film had to be tacked to the partially-placed laminate then fully melted in a separate operation. Screening experiments determined the relative influence of the various robotic process variables on the peel strength of the film-composite interface. Optimization studies were performed in which peel specimens were fabricated at various compaction loads and roller temperatures at each of three film melt processing rates. The resulting data were fitted with quadratic response surfaces. Additional specimens were fabricated at placement parameters predicted by the response surface models to yield high peel strength in an attempt to gage the accuracy of the predicted response and assess the repeatability of the process. The overall results indicate that quality PEEK/lM7 laminates having film interleaves can be successfully and repeatability fabricated by heated head automated fiber placement.

Hulcher, A. Bruce↗
DOE OSTI2022

Research needs targeting direct air capture of carbon dioxide: Material & process performance characteristics under realistic environmental conditions

The extraction of CO 2 from ambient air, or direct air capture (DAC), is a crucial negative CO 2 emissions technology with great potential for contributing to the mitigation of global warming and climate change. Furthermore, nearly all published research on DAC has been conducted under indoor temperature conditions, i.e. 20 to 30 °C. In contrast, the future global implementation of DAC requires it to be operational across a wide expanse of geographical areas, of which the local temperatures can vary between -30 to 50 °C. Similarly, the absolute humidity can vary from ~0 to 84 g/m 3 in various locations. Due to the massive amount of air that would be processed, it may be impractical to preheat or dehumidify the air before the CO 2 separation. Therefore, it is important to develop DAC materials with good performance at realistic outdoor conditions, especially at sub-ambient conditions, i.e. -30 to 20 °C. In addition to material development, system-level studies at sub-ambient conditions are also needed for the DAC processes to reach optimal designs, which may be very different from those at ambient conditions. In this perspective article, we first assess the literature to identify the technical gaps that need to be filled for DAC to be applicable at realistic outdoor conditions. We then suggest additional research directions needed for DAC to be viable under varied conditions from the perspectives of materials and system designs. For materials, we discuss the expected physical and chemical property changes for the sorbents when the temperature or humidity reaches extremes within their range, and how that will impact performance. Similarly, for system design, we indicate how varied conditions will impact performance and how these changes will impact process optimization.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2001

Friction Stir Welding at MSFC: Kinematics

In 1991 The Welding Institute of the United Kingdom patented the Friction Stir Welding (FSW) process. In FSW a rotating pin-tool is inserted into a weld seam and literally stirs the faying surfaces together as it moves up the seam. By April 2000 the American Welding Society International Welding and Fabricating Exposition featured several exhibits of commercial FSW processes and the 81st Annual Convention devoted a technical session to the process. The FSW process is of interest to Marshall Space Flight Center (MSFC) as a means of avoiding hot-cracking problems presented by the 2195 aluminum-lithium alloy, which is the primary constituent of the Lightweight Space Shuttle External Tank. The process has been under development at MSFC for External Tank applications since the early 1990's. Early development of the FSW process proceeded by cut-and-try empirical methods. A substantial and complex body of data resulted. A theoretical model was wanted to deal with the complexity and reduce the data to concepts serviceable for process diagnostics, optimization, parameter selection, etc. A first step in understanding the FSW process is to determine the kinematics, i.e., the flow field in the metal in the vicinity of the pin-tool. Given the kinematics, the dynamics, i.e., the forces, can be targeted. Given a completed model of the FSW process, attempts at rational design of tools and selection of process parameters can be made.

Nunes, A. C., Jr.↗
DOE OSTI2025

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗
DOE OSTI2021

Exploring the structural uniformity and integrity of protonic ceramic thin film electrolyte using wet powder spraying

Thin protonic ceramic electrolyte contributes to lower ohmic resistance and enhances electrochemical performance of protonic ceramic electrochemical cells. However, manufacturing of large-scale thin electrolyte remains a challenge. Wet powder spraying is an attractive technique to deposit <10 µm thin electrolyte when advanced atomizing techniques and optimized spraying process are integrated. Here ultrasonic atomization is integrated in the wet powder spray technique to reduce the thickness of electrolyte. Moreover, a parametric study is conducted to optimize the wet powder spray process to deposit uniform and crack-free electrolyte film. It is illustrated that tuning of solid loading rates and spray passes can affect the morphology of the as-sprayed electrolyte film, enabling the structural compactness of the sintered electrolyte layer. To maintain chemical stability of the electrolyte layer during sintering, effect of sintering temperature is further investigated to produce a physically thin, structurally dense, and chemically homogeneous electrolyte layer. The protonic ceramic electrochemical cells fabricated with optimized spraying and sintering parameters demonstrate excellent performance under both fuel cell and electrolysis modes. In addition, the cells exhibit remarkable structural integrity during redox and long-term stability tests.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A mixed integer linear programming approach for the design of chemical process families

Tackling climate change goals requires widespread deployment of process technology variants across many decentralized sites with different geographical, environmental, and operational requirements. Conventional engineering approaches focus on unique designs for each installation (process variant), while missing opportunities for manufacturing standardization. Here, instead we seek to optimize a process platform of common unit designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed-integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus as part of CCSI2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring

Material produced via additive manufacturing (AM) continues to exhibit variable mechanical properties despite apparent optimization of processing parameters, inhibiting qualification efforts and limiting use in critical applications. Stochastic lack-of-fusion flaws may help explain this variability, but the origin of these seemingly random defects has to this point remained unclear. In this work, we show that spatter particles, material ejected from the laser melt pool, are directly responsible for generating stochastic lack-of-fusion in laser-based powder bed fusion components through the application of spatial statistics. Herein a statistically significant, causal relationship between spatter particles and stochastic lack-of-fusion is established, and the spatial and morphological relationships between spatter and internal flaws are investigated. The occurrence of spatter-induced lack-of-fusion in relation to the inert gas flow and laser trajectory direction is also investigated, and recommendations for mitigating the occurrence of spatter are evaluated.

36 MATERIALS SCIENCE↗