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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Heat Exchanger Design for Waste Heat Recovery using Additive Manufacturing

With anticipated increases in global energy use and their associated emissions, there are many efforts to improve the emissions and efficiency of many industrial processes through waste heat recovery (WHR). To maximize improvements, the heat exchangers in WHR power cycles must be designed and optimized for a given heat source. Meanwhile, additive manufacturing (AM) is a single part manufacturing technique that provides the opportunity to develop unique and complex heat exchanger designs. Consequently, this work centers on the modeling, design, optimization, and testing of AM shell and tube heat exchangers for WHR. Specifically, a modified one-dimensional (1+1-D) and three-dimensional (3-D) heat exchanger models were developed and validated, with the 1+1-D model serving as the basis for design optimization. After modifications to the design to allow for AM, heat exchanger geometry optimizations were completed, targeting increased effectiveness across a range of physical parameters. Of the designs considered, nine were additively manufactured using Ti5553, and then tested using an external heat source and working fluid. Experimentally, the nineteen-tube, zero-baffle heat exchanger displayed the best overall performance, with the effects of dimensional accuracy, surface roughness, and baffles in the other designs also indicated. Next, the experimental results were used as inputs for the 1+1-D model. Although the original model was able to predict fluid outlet temperatures within 5% differences, larger variations in the heat exchanger performance parameters, temperature differences, and pressure drop necessitated optimizing the pressure drop and heat transfer calculations within the model. Thus, the resulting improved model was able to predict the heat transfer rates, heat source temperature difference, and effectiveness within 15% differences, while showing improvements in the pressure drops and working fluid temperature differences. The improved model serves as the foundation for future work to improve the design and performance of AM heat exchangers.

Spickler, Bailey Ann↗
DOE OSTI2024

Scientific Foundations and Approaches for Qualification of Additively Manufactured Structural Components

Additive manufacturing (AM) maintains a wide process window that enables complex designs otherwise unattainable via conventional production technologies. However, the lack of confidence in qualifying AM parts that leverage AM process–structure–property–performance (PSPP) relationships stymies design optimization and adoption of AM. While continuing efforts to map fundamental PSPP relationships that cover the potential design space, we first need pragmatic and then long-term solutions that overcome challenges associated with qualifying AM-designed parts. Two pragmatic solutions include: (1) AM material specifications to substantiate process reproducibility, and (2) component risk categorization to associate system risk relative to part performance and required part quality. A novel qualification paradigm under development involves efficient prediction of part performance over wide-ranging PSPP relationships through targeted testing and computational simulation. Here this paper describes projects at Sandia National Laboratories on PSPP relationship discovery, these pragmatic approaches, and the novel qualification approach.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design of an Additively Manufactured Recuperator With 800 °C Inlet Temperature for sCO2 Power Cycle Application

Abstract Recuperators with design temperatures at and above 800 °C can further increase the thermal efficiency of supercritical CO2 power cycles by enabling higher turbine exhaust temperatures. Mar-M247 is a well-suited nickel-based superalloy for high temperature service due to its high creep strength that prevents excessive material thickness being required for pressure containment. Additive manufacturing using a high-speed laser-directed energy deposition (L-DED) process presents a promising solution, with build trials demonstrating the ability to produce nonconventional flow channels for enhanced heat transfer. A design process is presented that includes aerothermal and mechanical evaluation to maximize performance within the constraints of the manufacturing process. A 2-D heat transfer network and pressure drop code allows prediction of flow distribution and its effect on overall thermal performance. Established literature correlations, along with CFD simulation, inform the prediction of heat transfer coefficients and friction factors for the flowpaths and enhancement features in the heat exchanger core. Mechanical evaluation using finite element analysis (FEA) modeling with the intent of the ASME Boiler & Pressure Vessel Code (BPVC) Section VIII, Div. 2 assesses the operational safety of the design. The detailed design features annular finned passages that take advantage of helical flow paths to distribute the flow from separated headers to shared heat transfer surfaces. Performance predictions for the recuperator at a 50 kW scale provide insights into the feasibility of the additively manufactured (AM) process to produce recuperators on a commercial scale that extend existing operating envelopes.

Engineering↗
DOE OSTI2026

High-Speed Layup and Forming of Automotive Composite Components

This Project is focused on the design and manufacture of automotive components that meet functional and environmental requirements of an existing automotive application at a cost of ≤ $\$$11.00 per kilogram weight reduction. This project fosters the development of composite material technologies suitable for high volume automotive processes and run rates as well as industry workforce development with these technologies. Current automotive manufacturing involves utilizing steel or aluminum in sheet form which is rapidly stamped into components at rates up to 3600 per hour. The metallic sheets are available in many different thicknesses, strength levels, and manufacturing rates are reasonable independent of part size. While composite materials are available for use in automotive applications, the material cost, labor to manufacture and the processing of the waste far exceed the cost compared to metallic designs. Typical composite layer by layer layup procedures don’t meet the desired 60 second layup time that current automotive processes require and are also restricted by part size. Due to these factors, composites have not yet made advances into today’s high volume automotive applications. Industry partners DURA, BASF, Ford, and IACMI core innovation partner MSU collaborated to develop a manufacturing process technology that is capable of manufacturing composite blanks at high volume and independent of part size. IACMI core innovation partner Purdue provided FEA analysis and cost modelling. The objective of this project was to demonstrate a composite sheet layup and consolidation process that can be commercialized for high volume requirements, identify potential layup equipment suppliers, and develop a process of 60 second layup, forming, and trimming of a continuous fiber automotive component for the mainstream market.

42 ENGINEERING↗
DOE OSTI2023

Evaluation of printability methodologies and feasibility to down select LBPF steel and other materials for nuclear applications

In this work, new research is underway to produce alloys beyond well-known alloys with modified compositions that are better suited to be produced with additive manufacturing. These alloys can only possess improved radiation tolerance but also improved high temperature strength, creep resistance while also using elements that reduce the amount of activation after irradiation. This work package supports the vision and goals of the Advanced Materials and Manufacturing Technology (AMMT) program relevant to accelerate the development and deployment of advanced manufacturing processes. This work focuses on evaluating the methodology for designing alloys best suited for AM processes based on “printability”, literature research to evaluate and down select steel alloy systems. In this work, microstructural characterization and testing activities have been performed on certain steel alloys to support the efforts from collaborating laboratories during the evaluation and printing of the test articles. The final recommendation will be performed by all four participating laboratories.

316L↗
DOE OSTI2024

Ensemble Manufacturing Techniques for Steam Turbine Components Across Length Scales

Faster design to manufacturing requirements were sought for steam turbine components to meet the changing demands of today’s power grid of improved efficiency through operating temperature increases and enhanced operational flexibility from baseload to cyclic operations. Advances in multiple advanced manufacturing (AM) process enabled redesign of turbine components for extreme environments with the potential to reduce cost. AM is of particular interest to improve component functionality, higher temperature capability, and superior durability in large scale steam turbine applications. AM methods have an immense potential to open-up the design space by working directly with the 3D model to produce near-net shapes, enable fast design-manufacturing iterations, thereby significantly reducing product cost and lead-time up to 25 % from current baseline. However, these benefits cannot be fully realized due to the potential for unknown AM processing defects and their resultant effect upon component performance in service. Siemens is partnering with Oak Ridge National Laboratory (ORNL), Electric Power Research Institute (EPRI), and Connecticut Center for Advanced Technology (CCAT) to advance the knowledge of complex process-material interactions for desired microstructures and properties that are closely interlinked to component geometries across different length scales. The proposed program utilized an ensemble of multidisciplinary technologies to accelerate the development of materials, high-throughput experiments for their qualification, and design flexibility/topology optimization for repair/redesign of components to address critical failure mechanisms for improved performance and increased reliability of existing power plant components. The proposed activities, if successfully demonstrated for identified components, will enable paradigm shift in customized manufacturing and accelerated qualification/certification towards increased steam turbine component durability, increased turbine efficiency, and reduced CO 2 emissions in load-following environments compared to today’s technology. Technology maturation is built into the project as successful research will include customized process-component down-selection enabling AM methodologies to be incorporated directly into the existing supply chain. The specific activities of the proposed effort are: 1. Topology optimization of down-selected steam turbine parts that are amenable to additive and hybrid manufacturing for cost/performance improvement. 2. Process-structure-property relationships for five AM processes for steam turbine materials of interest to compare with conventional materials. 3. Perform part/assembly build process using advanced additive/hybrid machine tools followed by quality inspection of the built components for insight into qualification for production scale-up. Steam turbine rig testing of printed components under targeted, well monitored and characterized environmental conditions of for performance comparison of baseline and redesigned components.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Implementation of Disruptive Designs for Gas Turbine Components Utilizing Additive Manufacturing

Electron beam melting (EBM) is an additive manufacturing process used to print metallic components with complex geometries for aerospace applications. A preheating characteristic enables this technology to avoid large accumulation of residual stress during printing. However, cracking mechanisms particularly in Ni-based superalloys are associated with not only residual stress but also with microstructure (i.e., solidification morphology, transient evolution of γ'). Siemens is committed to AM technology for gas turbine applications. The ability to novel predictive designs of turbine blades would reduce process development time and allow acquisition of key microstructure and performance data early in a design campaign. Siemens initiated a project to establish the design rules for the fabrication of non-printable Nibased superalloys. This phase 1 program demonstrates the feasibility for virtual fabrication of the actual blade geometries through EBM process.

42 ENGINEERING↗
DOE OSTI2024

Multi-Physics Topology Optimization for Dual Flow Heat Exchangers

Current heat exchanger (HX) designs are subjected to strict constraints pertaining to their manufacturability through conventional lengthy and expensive processes consisting of subtractive, forming and brazing processes. Therefore, existing HXs are typically restricted to the classical and rather simple geometries, such as plate-fin or shell-tube geometries, with lower efficiency and larger size. To increase HX efficiencies, novel designs are needed. Recent advances in topology optimization (TO) and additive manufacturing (AM) offer significant flexibility in design and fabrication of complex geometries, with potentially superior thermo-mechanical performance and high-power density. On the other hand, there is no commercially available software tool that addresses the combined coupling of structural, fluid dynamics, and thermal with multiple materials (hot and cold streams, solid, and void space) in TO of HXs. The goal of this project is to develop a multi-physics multi-phase TO approach to enhance the performance of HXs. The optimized design will be additively manufactured and tested in a physical environment to quantify its performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Scalable nanomanufacturing of chalcogenide inks: a case study on thermoelectric V–VI nanoplates

Solution-processed semiconducting main-group chalcogenides (MMCs) have attracted increasing research interest for next-generation device technologies owing to their unique nanostructures and superior properties. To achieve the full potential of MMCs, the development of highly universal, scalable, and sustainable synthesis and processing methods of chalcogenide particles is thus becoming progressively more important. Here we studied scalable factors for the synthesis of two-dimensional (2D) V–VI chalcogenide nanoplates (M 2 Q 3 : M = Sb, Bi; Q = Se, Te) and systematically investigated their colloidal behaviour and chemical stability. Based on a solvent engineering technique, we demonstrated scale-up syntheses of MMCs up to a 900% increase of batch size compared with conventional hydrazine-based gram-level syntheses, and such a scalable approach is highly applicable to various binary and ternary MMCs. Furthermore, we studied the stability of printable chalcogenide nanoparticle inks with several formulation factors including solvents, additives, and pH values, resulting in inks with high chemical stability (>4 months). As a proof of concept, we applied our solution-processed chalcogenide particles to multiple additive manufacturing methods, confirming the high printability and processability of MMC inks. Furthermore, the ability to combine the top-down designing freedom of additive manufacturing with bottom-up scalable synthesis of chalcogenide particles promises great opportunities for large-scale design and manufacturing of chalcogenide-based functional devices for broad application.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Genomic materials design: CALculation of PHAse Dynamics

The CALPHAD system of fundamental phase-level databases, now known as the Materials Genome, has enabled a mature technology of computational materials design and qualification that has already met the acceleration goals of the national Materials Genome Initiative. Here, as first commercialized by QuesTek Innovations, the methodology combines efficient genomic-level parametric design of new material composition and process specifications with multidisciplinary simulation-based forecasting of manufacturing variation, integrating efficient uncertainty management. Recent projects demonstrated under the multi-institutional CHiMaD Design Center notably include novel alloys designed specifically for additive manufacturing. With the proven success of the CALPHAD-based Materials Genome technology, current university research emphasizes new methodologies for affordable accelerated expansion of more accurate CALPHAD databases. Rapid adoption of these new capabilities by US apex corporations has compressed the materials design and development cycle to under 2 years, enabling a new “materials concurrency” integrated into a new level of concurrent engineering supporting an unprecedented level of manufacturing innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancements to Lab-on-a-Fish Technology: Final Report for I3T Project 81245

PNNL has engaged in discussions with several organizations regarding potential future collaborations. PNNL has developed the Lab-on-a-Fish prototypes and produced video instructions for an organization conducting research on shark eggs. Several organizations have expressed interest in using the Lab-on-a-Fish for their studies. We’ve also updated two versions of the Lab-on-a-Fish to expand its applications to a wider range of animal studies and accelerate its commercialization. The new design of the Lab-on-a-Fish, which incorporates PCB-based electrodes, has demonstrated the ability to capture clear ECG signals without the need for additional needle-shaped electrodes. This innovation eliminates the need for implanting electrodes beneath the fish's skin at specific locations, which is required with the current version of Lab-on-a-Fish. This change significantly simplifies the manufacturing and implantation process. For the Lab-on-a-Fish design that uses an optical pulse oximeter, the measurement results were primarily influenced by respiratory activity rather than heart rate. This occurred because the oximeter was placed beneath the operculum. Further research and development are needed to explore more suitable locations and methods for accurate pulse oximeter measurements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

OPERATOR 4.0 FOR HYBRID MANUFACTURING

Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

Fillingim, Blane↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
DOE OSTI2025

Evaluation of U10Mo Fuel Plate Performance Modeling Over Hot Isostatic Press and Hydraulic Bending for MURR DDE Plates

The United States High Performance Research Reactor Program’s objective is to reduce the amount of highly enriched uranium currently implemented in research reactors. The conversion of these research reactors requires designing a monolithic U10Mo plate fuel, with the fuel plate geometry being dependent on each research reactor. The process of forming the plates includes a hot isostatic pressing (HIP) to manufacture a prototypic plate. In the case of the Missouri University Research Reactor (MURR) design demonstration element (DDE) plate manufacture, plates that have been through HIP are then curved using dies and a hydraulic press to impart the desired curvature. Both fabrication processes impart residual stresses into each fuel plate region, with the curvature of the plates taking some regions of the fuel plate up to their material yield stresses, accompanied by plastic strain. The amount of plastic strain and stress imparted onto each MURR DDE plate is determined by the radius of curvature, thickness of each region, and overall width of the fuel plates. Furthermore, this work aims to predict the yield stresses and strain using ABAQUS to simulate the proposed fabrication process of the MURR DDE plates, accompanied by discussion over the stresses and strains as to their relation to nuclear fuel performance and the impact they will have during early irradiation.

ABAQUS↗
DOE OSTI2026

Truchas Overview

Truchas and Truchas-PBF are two sister codes for part-scale multi-physics modeling of manufacturing processes. Both programs are open source and made publicly available. They’re designed for efficient use of HPC resources and can be programmatically driven from Python packages. This enables automatic execution and analysis of ensembles of simulations, in some cases allowing 1000s of simulations to be evaluated in a day on HPC. Beyond just giving engineers a window into the concealed internal state of a system, the goal of Truchas is to provide a framework for developing novel manufacturing processes by understanding how the entire space of engineering inputs affects thermal state. It often is used to explore combinations of capabilities uncommon in commercial software, or to scale up analyses beyond the capabilities of commercial software.

97 MATHEMATICS AND COMPUTING↗