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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Primary Heat Transport System Design Considerations for Xcimer Energy’s Athena Fusion Pilot Plant

Fusion energy promises a reliable, carbon-free source of power; however, significant challenges remain before it can be deployed as an economical energy source. In addition to achieving fusion conditions, power plants must operate under extreme temperatures, radiation, and mechanical loads while maintaining high efficiency and availability. These requirements place strong demands on engineering design and plant operation. This work focuses on the engineering challenges associated with balance of plant analysis for inertial fusion energy systems. In particular, this paper examines the design considerations for primary heat transfer systems in fusion pilot plants employing molten fluoride salt coolants, with particular emphasis on system layout optimization and the balance between competing design objectives using the Xcimer Energy Athena inertial pilot plant design as a case study. Through systematic analysis of candidate system configurations and parametric sensitivity studies, we identify key engineering trade-offs governing salt inventory, pumping power requirements, and operational flexibility. The analysis employs system-level modeling tools to explore the design space and establish relationships between geometric parameters and system performance metrics.

Greenwood, Scott [ORNL] (ORCID:0000000333480736)↗
DOE OSTI2022

Particle Swarm Optimisation for group structure optimization for radiotherapy shielding

Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Alternatives to MARVEL Power Conversion – Comparison of Stirling Engine Thermal Efficiency and Design to other Power Conversion Cycles

The Microreactor Applications, Research, Validation, and Evaluation (MARVEL) Reactor is a small liquid-metal thermal reactor that will be built at the Idaho National Laboratory to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. Power conversion in the MARVEL design is provided by Stirling engines, which have disadvantages in nuclear environments. Compared to Stirling engine performance, some alternative power cycles can increase power production when coupled to a liquid-metal thermal reactor In this paper, the thermal efficiency of MARVEL’s power production with Stirling engines is compared to the thermal efficiency of power production with MARVEL and alternative power cycles. Those cycles include a superheated Rankine cycle, open and closed Brayton cycles, and a supercritical carbon dioxide cycle. All cycles (except the Stirling engines) were modeled with an intermediate helium loop to meet MARVEL’s principal design criteria. All models are simple designs with conservative assumptions for consistent comparison. Detailed optimization will depend largely on reactor location and application, and the relative merit of each cycle is discussed for different environmental conditions. The study informs significant early decisions on power cycle design and economic

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Biomaniac

Los Alamos National Laboratory researchers are developing a machine-learning based algorithm to revolutionize how biopolymers are designed and optimized. BioManIAC is a software platform that fills the gaps in data to gain insight into new potential polymer properties without making and testing them. Polymer engineers are able to use BioManIAC software to extrapolate and tune new biopolymer properties that allow the user to create small smart libraries, rather than largescale trial and error testing. This targeted design is expected to improve both time to market and R&D expenditure, as well as the resulting mechanical properties of biopolymers, enabling for more direct competition with traditional petroleum-based polymers. Los Alamos is seeking partners to further validate our software platform.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerating engineered microbe optimization through machine learning and multi-omics datasets

This project demonstrated the use of a combination of multi-omics data with deep learning and a high-throughput Design- Build-Test-Learn (DBTL) cycle to improve the production of malonic acid, a versatile product with a large market. The project leveraged the unique capabilities of both Lygos and the Agile BioFoundry (ABF): Lygos provided its expertise efficiently designing, building, and cultivating P. kudriavzevii strains; LBNL, PNNL, and NTESS provided multi-omics analysis in the Test phase, LBNL provided machine learning techniques in the Learn phase to analyze the -omics datasets and make recommendations so as to increase malonic acid production in the next DBTL cycle. This project is the first to use large amounts of multi-omics time-series data to feed deep learning models, creating around 80,000 data points in a single DBTL cycle. This project has 1) demonstrated the utility of combining deep learning and multi-omics data sets by improving the production of malonic acid two fold, 2) created a large time-series datasets to be released publicly for external development of new machine learning algorithms, and 3) shown that supply chain problems, strain building bottlenecks, and adaptation times for new ML approaches are key obstacles for fast DBTL cycle times.

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

Heavy Duty Natural Gas Single Cylinder Research Engine Installation, Commissioning, and Baseline Testing

Natural Gas (NG) Internal Combustion Engines (ICE) are a promising alternative to diesel engines for on-road heavy-duty applications to reduce greenhouse gas and harmful pollutant emissions. NG engines have not been widely adopted due to the lower thermal efficiency compared with diesel engine counterparts. To develop the base knowledge required to reach the desired efficiency, a Single Cylinder Engine (SCE) is the most effective platform to acquire reliable and repeatable data. A SCE test cell was developed using a Cummins 15-liter six-cylinder heavy-duty engine block modified to fire one cylinder (2.5-liter displacement). A Woodward Large Engine Control Module (LECM) is integrated to permit implementation of real-time advanced combustion control. Intake and exhaust characteristics, fuel composition, and exhaust gas recirculated substitution rate (EGR) are fully adjustable. A high-speed data acquisition system acquires in-cylinder, intake, and exhaust pressure for combustion analysis. The baseline testing shows reliable and consistent results for engine thermal efficiency, indicated mean effective pressure (IMEP), and coefficient of variance of the IMEP over a wide range of operating conditions while achieving effective control of all engine control and operation variables. This test cell will be used to conduct a research program to develop new and innovative control algorithms and CFD optimized combustion chamber designs, allowing ultra-high efficiency and low emissions for NG ICE heavy-duty on-road applications.

03 NATURAL GAS↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2020

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗
DOE OSTI2023

CrossLink: Advancements in Scalable Unstructured Mesh Generation [Slides]

Traditional mesh generation approaches are labor intensive and have limited robustness when applied to parametric design exploration and optimization of complex geometries. While automatic mesh generation approaches exist, they tend to generate tetrahedral or mixed-hybrid meshes which are generally unsuitable for physics applications with strong shock waves, thin boundary layers, and strong gradients. In addition, simulations sizes in the billions of cells are becoming more common with traditional mesh generation methods quickly reaching scalability limits. CrossLink offers a topology-based mesh generation approach with unstructured block-filling methods and a scalable mesh generation engine. In addition, CrossLink incorporates a python based API for seamless workflow integration and robust repeatability of the geometry handling and mesh generation process. This makes it ideal for parametric design study and optimization of complex geometries. Finally, future versions of CrossLink will offer a parametric mesh capability that optimizes a high-order mesh and enables reconstruction of the final mesh in memory by the physics solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Binder Effects on Processing, Mechanical Properties, and Performance of Thin Sulfide Solid‐State Electrolytes

All‐solid‐state batteries (ASSBs) offer enhanced safety and energy density compared to conventional lithium‐ion batteries by replacing flammable liquid electrolytes with solid‐state electrolytes (SSEs). Among SSEs, sulfide‐based electrolytes exhibit high ionic conductivity and mechanical deformability, making them promising candidates for next‐generation energy storage. However, their practical implementation is hindered by interfacial instability, mechanical brittleness, and challenges in fabricating ultrathin electrolyte membranes (<30 μm) with robust mechanical integrity. Here, this study systematically examines the influence of polymeric binders—polyisobutylene, hydrogenated nitrile butadiene rubber, and styrene–ethylene–butylene–styrene (SEBS)—on the structural, mechanical, and electrochemical performance of thin sulfide SSE membranes. Key findings reveal that SEBS enables the fabrication of ultrathin, uniform membranes, while binder elasticity significantly affects structural stability during cycling. Operando stack pressure measurements indicate that binder properties directly influence adhesion force for LPSCl particles, influencing their stabilizing cycling period. These results underscore the critical role of polymer binders beyond mechanical reinforcement, positioning them as essential design variables in sulfide SSE engineering. By linking binder chemistry to processability and electrochemical performance, this study provides insights into optimizing sulfide SSEs, advancing their commercial viability in ASSBs.

argyrodite sulfide electrolyte↗
DOE OSTI2025

Electro-activated indigos intensify ampere-level CO 2 reduction to CO on silver catalysts

The electrochemical reduction of carbon dioxide (CO 2 ) to carbon monoxide (CO) is challenged by a selectivity decline at high current densities. Here we report a class of indigo-based molecular promoters with redox-active CO 2 binding sites to enhance the high-rate conversion of CO 2 to CO on silver (Ag) catalysts. Theoretical calculations and in situ spectroscopy analyses demonstrate that the synergistic effect at the interface of indigo-derived compounds and Ag nanoparticles could activate CO 2 molecules and accelerate the formation of key intermediates (*CO 2 – and *COOH) in the CO pathway. Indigo derivatives with electron-withdrawing groups further reduce the overpotential for CO production upon optimizing the interfacial CO 2 binding affinity. By integrating the molecular design of redox-active centres with the defect engineering of Ag structures, we achieve a Faradaic efficiency for CO exceeding 90% across a current density range of 0.10 − 1.20 A cm –2 . The Ag mass activity toward CO increases to 174 A mg –1 Ag . This work showcases that employing redox-active CO 2 sorbents as surface modification agents is a highly effective strategy to intensify the reactivity of electrochemical CO 2 reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Co-introduction of precipitate hardening and TRIP in a TWIP high-entropy alloy using friction stir alloying

Tuning deformation mechanisms is imperative to overcome the well-known strength-ductility paradigm. Twinning-induced plasticity (TWIP), transformation-induced plasticity (TRIP) and precipitate hardening have been investigated separately and have been altered to achieve exceptional strength or ductility in several alloy systems. In this study, we use a novel solid-state alloying method—friction stir alloying (FSA)—to tune the microstructure, and a composition of a TWIP high-entropy alloy by adding Ti, and thus activating site-specific deformation mechanisms that occur concomitantly in a single alloy. During the FSA process, grains of the as-cast face-centered cubic matrix were refined by high-temperature severe plastic deformation and, subsequently, a new alloy composition was obtained by dissolving Ti into the matrix. After annealing the FSA specimen at 900 °C, hard Ni–Ti rich precipitates formed to strengthen the alloy. An additional result was a Ni-depleted region in the vicinity of newly-formed precipitates. The reduction in Ni locally reduced the stacking fault energy, thus inducing TRIP-based deformation while the remaining matrix still deformed as a result of TWIP. Our current approach presents a novel microstructural architecture to design alloys, an approach that combines and optimizes local compositions such that multiple deformation mechanisms can be activated to enhance engineering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,

Borowiec, Katarzyna↗
DOE OSTI2022

Developments and Challenges in the Design of the ITER DRGA

We report the ITER Diagnostic Residual Gas Analyzer (DRGA) will measure the distribution of gas species, i.e., deuterium (D), tritium (T), and impurities, in the divertor exhaust stream and in the plasma periphery, with time resolution relevant to fusion plasma–wall particle dynamics. The uniqueness of the DRGA, over previous implementations of plasma dynamics residual gas analysis, is an integrated approach, combining mass and low-temperature plasma-activated optical spectroscopy, in a differentially pumped analysis station. A further unique feature of the ITER divertor-specific DRGA is an ~8-m separation of the analysis station from the sampled pumping duct, while still maintaining a ~1-s response time for hydrogen isotopic concentrations. ITER DRGA final design activities are strongly benefiting from testing of prototypical DRGA components and methods on present fusion devices, most currently on JET and W7-X. DRGA systems are implemented on both these devices and include sensors (and pumping methods) that are directly relevant to the ITER DRGA design. The recent JET-DTE2 campaign has provided the first experience on operating the combined ITER DRGA sensors with D-T plasmas. While enhancing system design for ITER, this experience on operating devices has also revealed additional engineering challenges, which further guide the continuing final design project. Meanwhile, the recent determination that the ITER DRGA, with slight optimization, will resolve the helium isotopes well enough to support an ITER pre-DT, He-3-based heating scheme, has greatly increased ITER Research Program interest in the DRGA and its implementation well ahead of the DT phase.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Bayesian OED for Seismic Monitoring

SAND2024-13870O The Bayesian OED (Optimal Experiment Design) for Seismic Monitoring code provides the tools to analyze and optimize seismic monitoring networks using Bayesian OED. This method designs a utility function for experiments (network designs) using network analysis and network optimization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Catanach, Thomas↗
DOE OSTI2025

Design and Demonstration of a NH3-Fueled Two-Stroke Uniflow Engine for Greenhouse Gas Reduction

The maritime shipping industry is growing increasingly interested in both low and non-carbon-containing fuels to meet future greenhouse gas emission targets. Specifically of interest is ammonia, as it has a relatively high volumetric energy density compared to other future fuels, such as hydrogen, making it more economical to transport. The robust engine architecture of low-speed two-stroke marine engines makes them an ideal candidate for ammonia fuel, overcoming many of the issues surrounding its poor ignitability and low flame speed. If emissions and fueling system challenges can be addressed, retrofits of current low-speed two-stroke dual-fuel engines represent a viable pathway for bringing ammonia engines to market. This study explores these technical hurdles by describing the design, analysis, and experimental validation of a single cylinder research engine converted to operate on ammonia fuel. The engine is a reduced-scale uniflow two-stroke marine engine with two previous hardware configurations available – diesel and high-pressure CNG dual-fuel. A concept study was used to evaluate possible ammonia-fueled engine architectures and the associated tradeoffs and design considerations. With the chosen architecture, low-pressure dual fuel, 1D and 3D analysis tools were used to inform hardware selection and to determine hardware configurations which minimized ammonia-slip. In addition to these considerations the hardware and engine configuration were designed to provide a versatile and robust testing platform. This includes options to test both gaseous and liquid ammonia injection, as well as a wide range of performance parameters such as AFR, swirl, valve timing, SOI, and many others. Design constraints imposed by the existing engine hardware necessitated an iterative loop between design and analysis toolsets, ultimately converging on a final design for the ammonia-conversion hardware. The engine was rebuilt with the new hardware and evaluated in an engine test cell. A new control strategy developed and flashed onto a prototyping electronic control unit allowed for full control over all engine parameters. An initial calibration was developed, providing test data for validation of the engine 1D and 3D models. The impact of the design choices on engine operability and the ability to meet program targets is discussed as well as opportunities for further optimization of the ammonia-conversion hardware, informed by the validated models.

Kaul, Brian [ORNL] (ORCID:0000000184813620)↗
DOE OSTI2026

Engineering an Extremely Hybrid PKS for Adipic Acid Production

Polyketide synthases (PKSs) are modular enzymes with exceptional potential as biocatalysts for producing non-native compounds. Here, we report the first PKS-based pathway for adipic acid (AA), an industrial monomer for nylon production, by engineering one of the most extensively hybridized PKS systems to date. Using a retrobiosynthetic approach, we identified EtnB, a succinyl-CoA-loading module that uniquely retains the terminal carboxyl group, enabling access to dicarboxylic polyketide products, rarely produced by canonical PKSs. EtnB was coupled to a fully reducing extension module through an engineered communication linker, which improved ACP–KS interactions, enhanced titers, and demonstrated selective succinyl-CoA loading in vivo . This construct integrates genes from five organisms─with domains from seven PKS modules joined across six non-natural junctions─and functions in both Escherichia coli and Pseudomonas putida . Additional engineering that included AT domain exchanges, optimization of extender unit supply, and host strain metabolic rewiring further increased AA production. Together, this work demonstrates that highly chimeric PKSs can be rendered functional through rational design, expands the PKS toolkit with a carboxyl-retaining loading module, and establishes a versatile platform for engineering diacids and other noncanonical products through PKS pathways.

biomanufacturing↗
DOE OSTI2023

BEopt™ (Building Energy Optimization Tool) version 3.0.1 3/16/2023 [SWR-05-41]

The BEopt™ (Building Energy Optimization Tool) software provides capabilities to evaluate residential building designs and identify cost-optimal efficiency packages at various levels of whole-house energy savings along the path to zero net energy. The BEopt software can be used to analyze both new construction and existing home retrofits, as well as single-family detached and multi-family buildings, through evaluation of single building designs, parametric sweeps, and cost-based optimizations. The BEopt software provides detailed simulation-based analysis based on specific house characteristics, such as size, architecture, occupancy, vintage, location, and utility rates. Discrete envelope and equipment options, reflecting realistic construction materials and practices, are evaluated. The BEopt software uses EnergyPlus®, the Department of Energy's flagship simulation engine. Simulation assumptions are based on ANSI/RESNET/ICC Standards and the Building America Housing Simulation Protocols. The sequential search optimization technique used by the BEopt software: Finds minimum-cost building designs at different target energy-savings levels Identifies multiple near-optimal designs along the path, allowing for equivalent solutions based on builder or contractor preference. Download A BEopt™ Version 3.0.1 Beta release (Mar. 16, 2023) is now available. Download the BEopt 3.0.1 Beta setup file.

Christensen, Craig↗