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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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NREL and NanoSpray will cooperate to leverage NREL's existing solar cell processing facilities at NREL's Atmospheric Processing Platform to determine optimal processing method using 6" by 6" CdTe modules.
Ultrasonics is used as a non-destructive evaluation method to improve our understanding of polymers. Coupling ultrasonics and FTIR can help determine which bonds are more relevant to the mechanical properties of the polymer. Modeling the cure kinetics can determine if the curing process of a sample deviates from the baseline. Cure kinetics influence the final chemical structure of the polymer. This method opens a new pathway to design and adapt polymers for high demanding applications.
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Polymer composites have properties such as high strength and stiffness, low weight, good thermal and chemical stability, as well as impact and abrasion resistance that make them ideal for high-performance applications. The chemistries of these materials are continuously improving, so determining their properties is vital for successfully producing them and achieving the desired results. Multiple methods can be employed to monitor characteristics such as heat flow, weight, dimension, and modulus as a function of time and temperature. By analyzing this information, models can be developed to predict outcomes of parameters not tested for. In one application, materials proposed for wet filament winding and the production of high pressure vessels can be analyzed to verify they will have the necessary low viscosity for good fiber wetting and long pot life for the extended handling inherent to this process. Such data about a prospective system provides valuable information on how that material could ultimately be processed to yield the desired part.
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This work demonstrates the IDAES process family framework and the computational benefits of these formulations on a family of transcritical CO2 refrigeration cycles to meet a range of design criteria over both cooling capacity and outside air temperature.
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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.
Wire-arc additive manufacturing (WAAM) enables building large near-net-shaped parts using fast deposition rates and is attractive due to the potential cost and schedule savings. Haynes® 282® is a Ni-based superalloy with wide application in advanced power generation systems for its superior high temperature mechanical properties. High-quality WAAM H282 parts are achieved through careful process optimization, hence, we have focused on systematically characterizing the processing-structure-properties relationships over a wide range of wire-feed and travel speeds using a standard Ar30He shielding gas. Here the influence of adding 0.25-3% of H2 and CO2 to Ar-30He standard on our findings is examined. Multi-bead tracks were used to screen 20+ gas combinations to examine impact on wettability, porosity, oxidation, and grain structures. Improved multi-tracks are observed for gases with up to 1% H2 and 0.25% CO2. WAAM H282 builds within this range are examined to reveal differences in microstructure as evaluated with CT, SEM-EDS, and EBSD.
Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.
A hyper-branched polymer (HBP) electrolyte is synthesized for rechargeable lithium-air (Li-air) battery cell and experimentally evaluated its performance in actual battery cell environment. Several real-world battery cells were fabricated with synthesized HBP electrolyte, pure lithium metal as anode and an oxygen permeable air cathode to evaluate reproducibility of the rechargeable Li-air battery cell. The effect of various conditions such as various HBP based electrolytes, discharge current -0.1~0.5 mA, cathode preparation processes and carbon contents on the battery cell performance were experimentally evaluated using the fabricated battery cells under dry air condition. Detailed HBP electrolyte synthesis procedures and experimental performance evaluation of Li-air battery cell for various conditions are presented. The experimental results showed that different conditions and processes significantly affect the Li-air battery performance. Upon taking into account the effect of different conditions and processes, optimized HBP electrolyte materials, cathode process and conditions were determined. Several Li-air battery cells were fabricated with optimized conditions and optimized battery cell materials to determine the reproducibility and performance consistency. Experimental results showed that over 55–65 h of discharge occurred over 2.5 V terminal cell voltage with all three optimized Li-air battery cells. It implied that the optimized Li-air battery cells were reproducible and were able to hold charge over 2.5 V for more than 2 days. Experimental results of the Li-air battery cell with further refined optimized materials revealed that the battery cell can discharge more than 10 days (i.e., more than 250 h) at or above 2.0 V. The experimental results also showed that the Li-air battery discharge time got shorter as the discharge-charge cycle increases due to increase in internal resistances of battery cell materials. The experimental results confirmed that the lithium-air battery cell can be reproduced without loss of performance and can hold charge more than 10 days at or over 2.0 V. The investigation results obtained may usher a pathway to manufacture a long-life rechargeable Li-air battery cell in the near future.
Increasing renewable carbon incorporation into conventional fuels through coprocessing with vacuum gas oil (VGO, a petroleum refining feedstock) is a critical step in biofuels development, scaling-up, adoption and associated GHG reduction. Optimization of the co-processing parameters maximizes incorporation of the renewable carbon in the fuel products. Quantitative determination of the renewable carbon content in the co-processed products provides direct evaluation of the parameters. The co-processing bio-oil with VGO through hydrocracking (HC) or fluid catalytic cracking (FCC) system resulted in carbon isotopic fractionation that prevented the direct use of the isotope mixing model for quantifying the renewable carbon. Here, we report an algorithm of using a stable carbon isotope approach to quantify the renewable carbon content in co-processing biofuel products through high-precision ?13C analysis. A controlled experiment carried out by blending a fossil diesel (-29.013‰) with a bio-diesel (-30.099‰) at various blending levels up to 98.0/2.0 wt% is presented and has demonstrated the applicability of this approach. The carbon isotope fractionation factors for the bio-oil co-processing were obtained by using a 14C-derived isotope-mixing model. The ?13C method was tested by co-processing 13C-labeled bio-crude and natural woody biomass-derived fast pyrolysis (FP) and catalytic fast pyrolysis (CFP) bio-oils with VGO. The results were verified by 14C accelerator mass spectrometry (AMS) method (ASTM-D6866) and compared with the yield mass balance (YMB) method. Strong agreement between d13C and 14C AMS methods demonstrated the applicability of the ?13C method to quantify renewable carbon content in co-processing fuel products and guide the co-processing optimization
The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.
Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.
This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. Further, the power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange.