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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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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Thermal Analysis of a Solid Particle Light-Trapping Planar Cavity Receiver Using Computational Fluid Dynamics

Concentrated solar power (CSP) is one of the most effective ways of harnessing solar power to create efficient, durable, and resilient energy systems. This study entails thermal modeling and analysis of a novel central tower receiver configuration. This receiver uses solid particles as the heat transfer fluid (HTF), a promising option for third-generation CSP systems. The configuration considered here is the light-trapping planar cavity receiver (LTPCR) introduced by the National Renewable Energy Laboratory. While heat transfer studies of various LTPCR subsystems have been done, system-level thermal analysis of the LTPCR receiver has not been attempted. This study also presents important sensitivity analyses of the operating parameters of the CSP system, which can help guide the design of future central tower receivers. This study employs Ansys Fluent as a computational fluid dynamics (CFD) tool to model fluid dynamics and heat transfer in the receiver, intending to quantify its thermal performance. The model seamlessly integrates Monte Carlo ray tracing data, which generates absorbed solar flux profiles from the heliostat field design, with the heat transfer characteristics of the fluidized particle bed. This unified model is designed to accurately predict the thermal behavior of the LTPCR. Analysis of preliminary results reveals that the primary loss mechanisms are radiative and natural convective losses, in that order. Based on observations from a baseline case, several strategies are suggested and numerically tested. These solutions include selective cooling of high-temperature regions and manipulation of particle bed parameters. Selective cooling of high-temperature regions reduced the peak temperature by 151 degrees C and decreased thermal losses by 0.9%. Improving the particle-wall heat transfer coefficient (P-W HTC) of the particle bed decreased the thermal losses by 1.7% and decreased the peak temperatures by 57 degrees C. Decreasing the particle inlet temperature (PIT) also reduced thermal losses by 3.5% and decreased peak temperatures by 29 degrees C. Compounding these strategies improved the thermal losses of the receiver from 13.5% in the baseline case to 7.5%. Additionally, the study explores the variation in thermal performance across different locations of the receiver, where a variation of thermal losses from 12.9% to 17.3% is found. This allows a comprehensive evaluation of potential improvements in efficiency and temperature management.

computational fluid dynamics↗

DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Device-Centric Firmware Malware Detection for Smart Inverters using Deep Transfer Learning

Since future power grids are inverter-dominant grids and inverters are getting smarter by incorporating remote access and seamless firmware update, it is anticipated that malware attackers will directly target smart inverters. However, malware threats targeting smart inverters have been less studied yet. This paper explores potential malware attacks targeting smart inverters and proposes a deep transfer-learning (DTL)-based malware detection framework for smart inverters. The proposed DTL method can significantly reduce development time and efforts for an artificial intelligence-based malware detection algorithm while improving detection accuracy. The experimental result shows that the proposed method achieves 98% of firmware malware detection accuracy. Furthermore, this approach will be transformative to other smart grid devices enabling seamless firmware update.

artificial intelligence↗

DOE OSTI2020

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2002

Tetragonal Lysozyme, From Monomer to Crystal

The data now leads us to a comprehensive model for the process by which tetragonal lysozyme crystals are nucleated and subsequently grow. Lysozyme is typically desolubilized by addition of ionic salts. The salt anions bind to basic and other sites on the protein and promote protein-protein interactions, i.e., initiate the nucleation self assembly process. Formation of protein-protein interactions occurs at the expense of the protein-anion interactions, with the anions being released to the solution. The association follows a defined pattern, forming the "head to side" interactions of the crystal 4(3) helix. The presence of the high salt also promotes hydrophobic interactions between the protein molecules, further tightening their interaction. The solute assembly process persists after crystal nucleation, and the 4(3) helical structures form the subsequent growth units. AFM measurements show that the growth units follow the dimensions of these helices, and that those on the surface are more compact about the c-axis than in the bulk crystal, with adjacent helices riot being in contact. This further supports the role of hydrophobic interactions, as the surface is still in contact with the bulk solution. Once buried within the crystal the protein:salt ratio radically changes and the hydrophobic interactions relax to those measured crystallographically. Thus the crystal growth process recapitulates the initial stages of the nucleation process, and the two seamlessly merge. Experimental evidence, based upon face growth rate, AFM, and fluorescence energy transfer data, for a postulated model of the nucleation of tetragonal lysozyme crystals and how it transitions into crystal growth will be presented.

Pusey, Marc L.↗

DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Dispatch Manager for NEML2 Constitutive Model Calculations Embedded in MOOSE

This report describes the extended capabilities of the NEML2 constitutive modeling library, including a flexible and efficient work dispatching system designed to leverage both CPU and GPU resources. This enhancement addresses one of the primary computational challenges in large-scale simulations: the ability to distribute and execute batches of material model evaluations across heterogeneous computing devices. The new dispatch system introduces a modular set of dispatcher and scheduler classes that coordinate the flow of data and execution between devices. The dispatcher is responsible for efficiently packaging work, managing device-specific memory operations, and synchronizing results. This modularity allows for extensibility, making it straightforward to integrate additional computing backends in the future. From an implementation standpoint, the dispatcher system interfaces seamlessly with NEML2's existing models. They handle device-aware tensor operations, optimize memory transfers, and support asynchronous execution when applicable. This design ensures that batches of material points can be evaluated concurrently, substantially improving throughput compared to previous single-device or serial implementations. These improvements not only enhance the raw performance of NEML2 but also improve its usability in multiscale and high-fidelity simulations, where the simultaneous evaluation of large material point batches is critical. Benchmarks included in the report demonstrate the system’s scalability, highlighting its effectiveness when leveraging modern GPU architectures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1973

Thermal static bending of deployable interlocked booms

Metal ribbons processed with a heat-forming treatment are enabled to form tubelike structures when deployed from a roll. Deployable booms of this have been utilized for gravity-gradient stabilization on the RAE, ATS, and Nimbus D satellites. An experimental thermal-mechanics test apparatus was developed to measure the thermal static bending and twist of booms up to 3 meters long. The apparatus was calibrated by using the correlation between calculated and observed thermal bending of a seamless tube. Thermal static bending values of 16 interlocked deployable booms were observed to be within a factor of 2.5 of the values calculated from seamless-tube theory. Out-of-Sun-plane thermal bending was caused by complex heat transfer across the interlocked seam. Significant thermal static twisting was not observed.

Staugaitis, C. L.↗

DOE OSTI2024

Modeling and ZVS Operation of the Isolated Modular Multilevel DC–DC Converter With a Unified Trapezoidal Wave Modulation

Here, this article introduces a unified trapezoidal wave (UTW) modulation scheme for the isolated modular multilevel dc–dc (IM2dc) converter, which consolidates multiple existing modulation strategies for the IM2dc converter, including the quasi-square wave (QSW) modulation, trapezoidal wave modulation, and sinusoidal wave modulation, into a unified framework. Furthermore, this article introduces the harmonic state-space (HSS) equations to model the IM2dc converter based on the UTW modulation method. The HSS model operates in the frequency domain, enabling it to circumvent the complexities associated with time-domain analysis and seamlessly integrate with the UTW modulation. This article proceeds to analyze the real and reactive power transfer characteristics of the IM2dc converter, as well as the power factor, considering the influence of multiple modulation parameters. Subsequently, it delves into the examination of zero voltage switching (ZVS) conditions for the IM2dc converter based on the UTW modulation and the HSS model. The complete ZVS boundaries of the IM2dc converter, taking various voltage ratios into account, are derived. This article also illustrates the effects of including harmonic orders in the modeling process, modulation parameters, and internal harmonic ripples on the ZVS boundaries. Finally, experimental validation of the analyses is conducted on a down-scaled prototype.

42 ENGINEERING↗

NASA NTRSDate not supplied

Seeding Innovative Ideas and Partnering with the Ecosystem

Jenn Gustetic will talk about how NASA selects ideas and collaborates with the ecosystem to develop new technologies for NASA’s portfolio. She will explain how the organization encourages collaboration, co-invests to develop ideas from lab to market, and acts as the hub-and-spoke of the ecosystem. Jenn will also discuss NASA’s journey to break silos and work horizontally across programs. • Gathering and evaluating ideas internally and externally with diverse players – engaging communities, being the hub-and-spoke of the ecosystem, and encouraging collaboration through co-investments • Identify the best ideas, funding ideas from maturation to hand-off with fluctuating budgets, and managing IP and tech transfer • Balancing investments in projects that address NASA’s needs with higher risk ideas that may not have an obvious application • Transitioning ideas to the next phase – training people and teams and ensuring ownership • Developing a 3.0 innovation capacity strategy – disrupting silos, enabling the ecosystem to work seamlessly together, and taking people and leaders along

innovation↗

DOE OSTI2024

Interleaved Cuk Converter Wave Energy System With Advanced Control and Grid Support Functions for Seamless Integration: Preprint

This paper presents an innovative wave energy conversion system employing an interleaved Cuk converter and advanced nonlinear control for seamless integration with the grid. The proposed system utilizes the interleaved Cuk converter for efficient power extraction on the DC side, while an inverter facilitates power transfer to the grid or load on the AC side. A sophisticated nonlinear control architecture based on Lyapunov energy functions ensures stable and optimal operation under varying wave conditions. Case study results demonstrate the effectiveness of the proposed system, highlighting its ability to efficiently harness wave energy and seamlessly integrate with the grid, thus paving the way for sustainable and reliable renewable energy generation. The overall system is verified via computer simulations based on MATLAB/Simulink and various case study results are presented.

HYDRO ENERGY,TIDAL AND WAVE POWER↗

DOE OSTI2025

Photosynthetic Biohybrid System for Enhanced Abiotic N 2 -to-NH 3 Conversion under Ambient Conditions

Photosynthetic biohybrid systems (PBSs) offer an eco-friendly approach to transforming solar energy into value-added products by integrating biological entities with inorganic semiconductors. However, the chemical conversion capacity of most PBSs has inherent limitations, as whole-cell bacteria and isolated enzymes require fine-tuning of environmental conditions. Here, in this study, we report a new PBS developed by introducing free-standing ceria nanoparticles into the purple membrane (PM) of Halobacterium salinarum archaea, which can unidirectionally transfer charge carriers in response to incident photons, even after separation from living archaea at various conditions. Our microscopy, spectroscopy, and synchrotron X-ray scattering analyses confirm that the electrostatic assembly between ceria and PM creates seamless interfacial contact, thereby enhancing the photocatalytic capacity of ceria. Although the conversion of dinitrogen (N 2 ) to ammonia (NH 3 ) is thermodynamically challenging due to the triple bond in N 2 and a series of charge-transfer reactions, our PM–ceria (PMC) hybrid nanoparticle efficiently produces NH 3 by reducing N 2 using solar energy even under atmospheric pressure and room temperature while simultaneously converting glycerol into value-added derivatives. Additionally, our PMC nanoparticle involves neither toxic/precious metals nor bioengineering processes to achieve enhanced photocatalytic N 2 -to-NH 3 conversion. This study sheds light on the new aspect of PBSs by employing PM to potentially resolve the global energy and environmental challenges posed by the conventional Haber–Bosch process.

Jang, Jinhyeong [Argonne National Laboratory (ANL)↗

NASA NTRS1977

Aluminum transfer method for plating plastics

Electroless plating technique produces plate of uniform thickness. Hardness and abrasion resistance can be increased further by heat treatment. Method results in seamless coating over many materials, has low thermal conductivity, and is relatively inexpensive compared to conventional methods.

Goodrich, W. D.↗

NASA NTRS2014

JSC Search System Usability Case Study

The advanced nature of "search" has facilitated the movement from keyword match to the delivery of every conceivable information topic from career, commerce, entertainment, learning... the list is infinite. At NASA Johnson Space Center (JSC ) the Search interface is an important means of knowledge transfer. By indexing multiple sources between directorates and organizations, the system's potential is culture changing in that through search, knowledge of the unique accomplishments in engineering and science can be seamlessly passed between generations. This paper reports the findings of an initial survey, the first of a four part study to help determine user sentiment on the intranet, or local (JSC) enterprise search environment as well as the larger NASA enterprise. The survey is a means through which end users provide direction on the development and transfer of knowledge by way of the search experience. The ideal is to identify what is working and what needs to be improved from the users' vantage point by documenting: (1) Where users are satisfied/dissatisfied (2) Perceived value of interface components (3) Gaps which cause any disappointment in search experience. The near term goal is it to inform JSC search in order to improve users' ability to utilize existing services and infrastructure to perform tasks with a shortened life cycle. Continuing steps include an agency based focus with modified questions to accomplish a similar purpose

Meza, David↗

DOE OSTI2025

Data Management in the Continuum: Cross-facility Object-based Data Transfers

Scientific workflows are evolving from relying on a monolithic storage subsystem at a single High-Performance Computing (HPC) facility to using geographically distributed file systems, repositories, and cloud storage. As a result, storing, accessing, transferring, and managing scientific data have become highly complex and prone to performance inefficiencies. This paper delves into these challenges by exploring an optimized end-to-end interface designed to seamlessly connect various local and remote storage systems, enabling efficient data movement of objects across HPC–Cloud and HPC–HPC environments. We showcase this capability through an object-focused data management runtime system, discuss the effects of relaxed consistency semantics in distributed object scenarios, and illustrate its application in an earthquake simulation workflow. Besides reducing the amount of data by selectively transferring regions of interest, our facility-local results achieved a speedup of 45 × over an optimized HDF5 usage and 15 × over the HDF5 with caching by using the new interface in PDC-XF.

Bez, Jean Luca↗

DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Interleaved Cuk Converter Wave Energy System With Advanced Control and Grid Support Functions for Seamless Integration

This paper presents an innovative wave energy conversion system that integrates an interleaved Cuk converter with advanced nonlinear control for seamless grid integration. The system efficiently extracts power on the DC side using the interleaved Cuk converter, while an inverter manages power transfer to the grid or load on the AC side. A nonlinear control architecture, based on the Lyapunov energy function, ensures stable and optimal operation under varying wave conditions, effectively addressing the challenges of variability and unpredictability inherent in wave energy. The proposed system also incorporates features to enhance power quality and minimize losses, making it a robust solution for renewable energy integration. The system's effectiveness in harnessing wave energy and achieving smooth grid integration is validated through comprehensive computer simulations in MATLAB/Simulink, with case study results demonstrating its capabilities.

HYDRO ENERGY,TIDAL AND WAVE POWER↗

DOE OSTI2026

Passive Energy-Saving Solutions for Clothes Dryers: A Modeling and Experimental Study for Improved Efficiency and Affordability

Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗