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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗
DOE OSTI2022

3D Printed Carbon Aerogels for Polymer-Electrolyte Fuel Cells

Carbon aerogels (CA) are known for ultra-low density, very high surface area, fine porous structure, and excellent electrical conductivity. Recent advances in high resolution 3D printing at LLNL enable the manufacturing of complex structured CA electrodes which have been demonstrated in electrolysis, flow battery, and supercapacitor applications. Polymer-electrolyte fuel cells (PEFC) may be an excellent candidate for these materials/methods as designers seek to optimize membrane-electrode assemblies to achieve higher efficiencies. A major challenge to PEFC design is two-phase flow due to water production which causes pore flooding and leads to cell instability and degradation. To mitigate these effects, groups have investigated the creation of liquid specific pathways in the porous layers to alleviate liquid buildup and enhance gas diffusion. While some progress has been made, traditionally manufactured GDLs, which consist of randomly distributed carbon fibers, are limiting. 3D printed CAs are a promising solution as complex macroporous electrode structures can be manufactured out of micro and nano porous filaments with a high level of control. Additionally, the process may allow for the consolidation of multiple layers (MPL, GDL, & flow channels) into a single printed architecture. In this work various CA lattice structures were printed using a direct ink write (DIW) method and liquid breakthrough pressures were measured to demonstrate the ability to tune the water pathways to achieve specific pressure thresholds. Furthermore, a discussion of the prospects for printed CA electrodes in PEFC will focus on material properties, synthesis techniques, and current challenges.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Establishing substitution rules of functional groups for high-capacity organic anode materials in Na-ion batteries

Tailoring molecular structures of organic electrode materials (OEMs) can enhance their performance in Na-ion batteries, however, the substitution rules and the consequent effect on the specific capacity and working potential remain elusive. Herein, by examining three sodium carboxylates with selective N substitution or extended conjugation structure, we exploited the correlation between structure and performance to establish substitution rules for high-capacity OEMs. Our results show that substitution position and types of functional groups are essential to create active centers for uptake/removal of Na+ and thermodynamically stabilize organic structures. Furthermore, rational host design and electrolytes modulation were performed to extend the cycle life to 500 cycles. A full cell based on the optimal 2,2’-bipyridine-4,4’-dicarboxylic acid disodium salt anode and the polyaniline cathode is demonstrated to confirm the feasibility of achieving all-organic batteries. Lastly, this work provides a valuable guideline for the design principle of high-capacity and stable OEMs for sustainable energy storage.

25 ENERGY STORAGE↗
DOE OSTI2020

Performance simulation of the soft gamma-ray concentrator

The soft gamma-ray concentrator is a telescope mission concept utilizing a suitable arrangement of bent multilayer structures of alternating low- and high-density materials. This lens is able to channel gamma-ray photons via total external reflection and concentrate the incident radiation to a point. The channeling technique offers the potential for concentrating gamma rays with focal lengths <10 m and energies >100 keV, beyond the reach of current grazing-incidence hard x-ray mirrors. For the performance estimation of such an instrument, we have developed a flexible set of computer modeling tools to compute the optical properties of multilayer structures, predict the channeling efficiency for a given multilayer configuration, and aid in the optimization of potential gamma-ray concentrator-based telescope designs. This modeling includes the multilayer optical properties calculated by the IMD software, the ray tracing using an IDL code, and the focal plane detector simulation by MEGAlib. We illustrate the potential of this approach by presenting simulated astronomical observations from a balloon-borne platform. The final result, including simulated effective area, instrument sensitivity, and polarization performance, shows that the gamma-ray concentrator will provide greatly increased sensitivity for next-generation soft gamma-ray missions with modest cost and complexity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Gen 3 Particle Pilot Plant (G3P3) -- High-Temperature Particle System for Concentrating Solar Power (Phases 1 and 2)

The U.S. Department of Energy Solar Energy Technologies Office initiated the Generation 3 Concentrating Solar Power (CSP) program to achieve higher operating temperatures (>700 °C) to enable next-generation CSP high-temperature power cycles such as the supercritical CO 2 (sCO2) Brayton Cycle. Three teams were selected to pursue high-temperature gas, liquid, and solid pathways for the heat-transfer media. Phases 1 and 2, which lasted from 2018 – 2020, consisted of design, modeling, and testing activities to further de-risk each of the technologies and develop a design for construction, commissioning, and operation of a pilot-scale facility in Phase 3 (2021 – 2024). This report summarizes the activities in Phases 1 and 2 for the solid-particle pathway led by Sandia National Laboratories. In Phases 1 and 2, Sandia successfully de-risked key elements of the proposed Gen 3 Particle Pilot Plant (G3P3) by improving the design, operation, and performance of key particle component technologies including the receiver, storage bins, particle-to-sCO2 heat exchanger, particle lift, and data acquisition and controls. Modeling and testing of critical components have led to optimized designs that meet desired performance metrics. Detailed drawings, piping and instrumentation diagrams, and process flow diagrams were generated for the integrated system, and structural analyses of the assembled tower structure were performed to demonstrate compliance with relevant codes and standards. Instrumentation and control systems of key subsystems were also demonstrated. Together with Bridgers & Paxton, Bohannan Huston, and Sandia Facilities, we have completed a 100% G3P3 tower design package with stamped engineering drawings suitable for construction bid in Phase 3.

14 SOLAR ENERGY↗
DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗
DOE OSTI2024

Multiphysics Design Optimization and Additive Manufacturing of Nuclear Components (Final CRADA Report - Executive Summary)

Westinghouse Electric Company (WEC) actively participated in the advancement of the nuclear fuel and reactor design space and requested the help of Oak Ridge National Laboratory (ORNL) in the creation of a new design tool set. This report details the creation of a collection of software tool sets that are linked together to collectively assist WEC design engineers in developing novel ideas outside the normal scope of traditional nuclear fuel and reactor design formulas. Specifically, Siemens HEEDS, a design space exploration and parametric optimization software, monitored and changed parameters in a collection of softwares to meet the team’s objective. The HEEDS parametric optimization method, SHERPA, was developed to control the Siemens NX CAD platform to adjust the native CAD of a hexahedral spacer grid. This new geometry can be used to execute a topological design optimization by the NX Topology software add-in. The resulting geometry is additively manufacturable. This topological optimization occurred twice—once on the spacer grid’s spring, and once on the dimple geometry. These new geometries were imported by Siemens’ STAR-CCM+, a multiphysics structural and fluid dynamic computational solver in which the spring geometry is deflected to match the rod insertion configuration. Along with the dimple geometry, this new deflected spring was used to complete a hydraulic assessment of a single-unit cell comprising one rod, one spring, and two dimples. The HEEDS SHERPA algorithm ranks the design based on the final mass of the unit cell and the hydraulic pressure drop performance. The ORNL team demonstrated the ability to use this software and provided engineering judgement to apply modern aerospace aerodynamic design. The effort has been focused on thinking outside the conventional design space and redesigning a spacer grid to perform beyond the WEC set objectives. Furthermore, the ORNL team also demonstrated that the HEEDS optimization routine can independently develop a design that meets the WEC design goals. Although these designs were at a low technology readiness level, their demonstration confirmed the team’s capability to create novel advanced nuclear concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Design, Optimization, and In-Depth Understanding of Front and Rear Junction Screen-Printed Double-Side Passivated Contacts Solar Cells

In this work, detailed numerical modeling is performed for front junction (FJ) and rear junction (RJ) n-type Si solar cells with screen-printed double-side poly-Si based tunnel oxide passivated contacts (TOPCon). Fundamental understanding of both structures is crucial to explore factors that limit the efficiency potential of the two designs. By investigating several key parameters such as front poly sheet resistance and thickness, bulk material properties, and carrier transport in our simulation model, we determine and explain why RJ cells outperform FJ cells. Our findings reveal that FJ suffers from present technological limitations of p-poly based passivated contacts, namely, i) large recombination observed in textured p-TOPCon emitter and ii) low solid solubility and hole mobility in boron-doped poly-Si resulting in very high sheet resistance of front p-poly layers which must be made thin to reduce absorption losses. In contrast, RJ desensitized the cell efficiency to front sheet resistance allowing application of ultra-thin front n-poly layers and is therefore ideally suited for double-side TOPCon cells. Lastly, we have developed a roadmap for efficiency projections of commercial-type screen-printed RJ and FJ topologies reaching ~24% and >22.5% efficiencies respectively, quantifying and explaining the impact of technological innovations on the performance of each design.

14 SOLAR ENERGY↗
DOE OSTI2022

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

We present a benchmarking study of active learning (AL) schemes for designing planar multilayer nanophotonic metamaterials, where the design tasks are formulated as binary optimization problems. Different surrogate models, including factorization machine (FM), Gaussian process regression (GPR), and convolutional neural network (CNN), combined with different optimization methods, including exhaustive enumeration, discrete particle swarm optimization (DPSO), quantum annealing (QA), hybrid QA, and simulated annealing are studied. The benchmark cases investigated range from small problems with short binary lengths (N = 25) to large problems with N up to 100, focusing on the design of two classes of photonic structures, including antireflective coatings for the long-wavelength infrared region and transparent radiative coolers. For small problems, CNN coupled with DPSO in AL achieves the best performance. As N increases, FM with QA outperforms GPR and CNN. For FM-based AL, hybrid QA yields the best optimization results, particularly in high-dimensional cases (N = 100). These results demonstrate that the optimization method can significantly affect in AL performance as N increases, and that QA-based optimization can provide practical routes for mitigating the optimization bottleneck in high-dimensional problems.

Jung, Serang [Kyung Hee University, Korea]↗
DOE OSTI2020

Busbar Design and Optimization for Voltage Overshoot Mitigation of a Silicon Carbide High-Power Three-Phase T-Type Inverter

The silicon carbide (SiC) devices have faster switching speed than that of the conventional silicon (Si) devices, which however may cause excessive device voltage overshoot. Larger gate resistance can help to restrain the overshoot, it however slows down the switching speed and increases switching losses. There are other methods that can mitigate the voltage overshoot, e.g., using low-inductance busbars, adding snubber circuits, etc. In this article, the busbar design for a 250-kW SiC three-level T-type inverter is investigated. The current commutation loops (CCLs) are first analyzed using a single-phase equivalent circuit. Then the detailed busbar design methods, especially a 3-D busbar design concept, are proposed to select the optimal stacking order for the multilayer laminated busbar and to address constraints posed by the physical terminal arrangements of SiC modules and dc-link capacitors. The stray inductance in each CCL is extracted via a finite element analysis and validated on the actual inverter busbar prototypes using an impedance analyzer. To further minimize the busbar stray inductance, a hybrid busbar structure with printed circuit board based buffer circuit using high-frequency decoupling capacitors is designed and evaluated in this article. Lastly, the effectiveness of the designed busbars as well as the buffer circuit are validated using experimental studies.

42 ENGINEERING↗
DOE OSTI2024

Level-set topology optimization with PDE generated conformal meshes

This paper presents a level-set topology optimization approach that uses conformal meshes for the analysis of the displacement field. The structure’s boundary is represented by the iso-contour of a level-set field discretized on a fixed background design mesh. The conformal mesh is updated for each design iteration via a PDE based mesh morphing process that identifies the set of facets in the background mesh that are homeomorphic to the boundary and relaxes the homeomorphic mesh to conform to the structure’s boundary and ensure high element quality. The conformal mesh allows for a more accurate computation of the response versus density and some level-set based methods which interpolate material properties using the volume fraction. Numerical examples illustrate the proposed approach by optimizing linear-elastic two- and three-dimensional structures, wherein insight into the performance of the mesh morphing process is provided. The examples also highlight the scalability of the approach.

42 ENGINEERING↗
DOE OSTI2023

A Conceptual Design of the Reactor Cavity Cooling System for the Horizontal Compact High Temperature Gas Reactor (HC-HTGR)

Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multi-disciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed HC-HTGR in 3 years and support its commercialization as a safe, low-cost HTGR. Argonne National Laboratory (Argonne) is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. This report documents the design study to derive a conceptual design study of the RCCS for the HC-HTGR. It includes the identification of the functions and requirements of the HC-HTGR RCCS, design analyses including high-level design consideration and the calculations for optimizing design space of the system with supporting component-level analysis to inform the material selection and performance of the water panel, the description of the conceptual design of the HC-HTGR RCCS derived based on the analyses results, and performance evaluation of the conceptual RCCS for the HC-HTGR. A detailed concept of the RCCS has been identified and high-level system requirements has been developed for the HC-HTGR. Design space focusing on the natural circulation loop portion of the RCCS has been investigated to optimize the system performance. The initial baseline dimensions were firstly derived based on the scoping calculations. A component level design analysis was conducted for the water panel to inform the material selection and to assess its conduction performance. A preliminary system-level performance analysis was performed for the 1/8th of the compartment of the initial baseline design of the RCCS using RELAP5-3D. To improve the system thermal performance, the RCCS design has been updated by exploring various design options by design parametric analyses. Based on the results, the conceptual design of the RCCS for the HC-HTGR has been derived, which satisfies the target performance of ~1 MWt at the elevated vessel wall temperature conditions. Transient simulations were conducted for the conceptual RCCS design for the HC-HTGR under various operation modes and heat load conditions using RELAP5-3D. The system dynamics in different operating states was investigated and the system performance under transients of interest was evaluated. The results demonstrated the overall system feasibility that the RCCS design maintains structures temperatures lower than maximum allowable temperature with sufficient system inventory without any active heat removal in the design process with certain transients addressed. The HC-HTGR RCCS will have additional design updates of subsystems or optimization of the system components during the preliminary and final design phases. Since the entire plant has not been integrated yet, this delivered conceptual design is subject to changes for integration, that require additional conceptual design activities and Quality and Assurance implementation (Q&A). The performance assessment of the RCCS for the HC-HTGR will be then revisited and optimized to finalize the system design, and the RCCS integrated primary system analysis will be utilized to simulate selective accident scenarios of interest where efforts are currently undergoing in the project.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2023

On the 3D printing of polypropylene and post-processing optimization of thermomechanical properties

Polypropylene (PP) is a highly desirable polyolefin in various plastic industries due to its outstanding thermomechanical properties and chemical resistance. Therefore, the 3D printing of PP is an interesting avenue to explore in digitized manufacturing, where more freedom in structural designs is available for new and extended applications, such as high-performance engineering parts. Here, in this work, we 3D printed PP and studied the effect of printing parameters and post-processing conditions on the printed polymer’s thermomechanical behavior. Results showed that nozzle and bed temperatures of 220 and 100°C produced a high printing quality. Infill percentages between 80 and 90%, coupled with a 4-h annealing at 110ºC, also resulted in optimal printed properties. It is thought that PP can be potentially blended with polyethylene or other vinyl polymers for a more extended 3D printing utility and practical applications in rapid tooling and prototyping.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing Moderately‐Solvating Electrolytes for High‐Performance Lithium–Sulfur Batteries

New electrolytes are critical for high‐energy lithium (Li)–sulfur (S) batteries (LSBs) to ensure their stability against Li metal anode and polysulfides (PSs) shuttling which hinder the large‐scale application of LSBs. In this study, the design principle of moderately solvating electrolytes (MSEs) for LSBs is demonstrated by using a multiple‐solvent system comprising of a highly solvating solvent, a weakly solvating solvent, and a non‐solvating solvent to create a well‐balanced electrolyte system. This resulting electrolyte significantly improves the cycle life of LSBs, achieving 300 cycles, which is twice as long as that of similar cells with the conventional electrolyte and it also ensures stable calendar life for at least seven months. The optimal MSE forms robust passivation layers enhancing the structural integrity of both S and Li metal electrodes after cycling. These virtues effectively hinder parasitic side reactions and self‐discharge behavior of LSBs. This electrolyte design principle is versatile and can be applied to other battery chemistries, providing a potential path toward the development of a more efficient and stable battery system. By addressing key challenges such as the instability of electrodes and shuttling of polysulfides, this electrolyte approach offers promising solutions for advancing LSB technology.

25 ENERGY STORAGE↗