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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system

Functionally graded materials have the potential to improve upon monolithic parts by locally tailoring compositions to surrounding environmental conditions. Difficulties arise when designing composition gradients as incompatible materials can result in detrimental phase formation and failure of the gradient joint. As many alloys are multi-component, designing a composition gradient free of detrimental phases is difficult due to the large composition space available to explore. A framework was developed that improves the path planning algorithm and surrogate models with adaptive sampling schemes specific to their problem definition. A cost function was created to minimize a property (such as cracking susceptibility) along a path. This framework was applied to the Mo-Nb-Ta-Ti system as a case study to showcase the efficiency in building the surrogate models and in iterating different optimal compositionally graded paths.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Finite Difference informed Random Walk solver for simulating radiation defect evolution in polycrystalline structures with strongly inhomogeneous diffusivity

Diffusivity of species and defects on grain boundaries is usually several orders of magnitude larger than that inside grains. Such strongly inhomogeneous diffusivity requires prohibitively high computational demands for modeling microstructural evolution. Here, this paper presents a highly-efficient numerical solver, combining the Finite Difference method and Random Walk model, designed for accurately modeling strongly inhomogeneous diffusion within polycrystalline structures. The proposed solver, termed Finite Difference informed Random Walk (FDiRW), integrates a customized Finite Difference (cFD) scheme tailored for fast diffusion along thin grain boundaries represented by a single-layer of nodes. Numerical experiments demonstrate that the FDiRW solver achieves an impressive efficiency gain of 1560x compared to traditional Finite Difference methods while maintaining accuracy, making it feasible for personal computer machines to handle diffusional systems with strongly inhomogeneous diffusivity across static polycrystalline microstructures. The model has been successfully applied to simulate radiation defect evolution, showcasing its scalability to engineering scales in both length and time dimensions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding interfacial crystallization dynamics on carbon fiber reinforced polypropylene composite manufacturing

Reinforcing polymers with discontinuous fibers improves mechanical properties, such as strength and stiffness, and in some cases achieve isotropic properties, rendering them suitable for various engineering applications. Matrix materials are generally highly engineered thermosets (e.g. crosslinked epoxies), bonded to the fiber periphery by proprietary surface and sizing chemistries. Semicrystalline thermoplastic matrices are less utilized due to poor fiber-matrix bonding resulting in inefficient interfacial load-transfer in reinforced composites. However, flexibility with melt-processing or molding conditions can be leveraged to promote non-covalent interfacial bonding between matrix and fiber via crystallization of the matrix onto fiber surface. In the present study, we utilize a co-mingle chopped carbon and isotactic polypropylene fibers to form isotropic composites, tailoring interfacial immobilized matrix or interphase morphology to optimize performance through precise control of thermal processing/molding windows. Calorimetry and optical microscopy were employed to investigate the impact of carbon fiber at various volume fractions (10, 20, and 30 %) on isotactic polypropylene crystallization and mechanical performance. Variations in mechanical properties correspond to the structural evolution of the interfacial region and are correlated to underlying microstructural attributes using wide-angle X-ray scattering, thermal analysis, and low-field nuclear magnetic resonance spectroscopy. These results provide a practical framework for the manufacturing of thermoplastic matrix composites. In conclusion, the results presented provide a guide for the strategic optimization of interphase design, showcasing tailorable tensile strengths which outperform any isotactic polypropylene carbon fiber composites previously reported in literature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An open control sequence specification to scale building demand flexibility via analytics software

For over two decades, researchers and practitioners have showcased the ability of large commercial buildings to provide grid services by shedding or shifting load. Various utility demand response (DR) and virtual power plant (VPP) programs throughout the United States are presently utilizing these demand-side resources. However, growth of these programs have been limited, in part due to the high cost necessary to integrate the DR control strategies into the building automation system (BAS). Implementing these strategies involves adjusting control sequences, necessitating dozens of hours of customized programming per building, limiting their adoption to large organizations and progressive owners. Recent efforts by researchers and industry have demonstrated the capability of energy management and information systems (EMIS), originally designed for fault detection and diagnostics, to interface with existing BAS and perform supervisory control to optimize building operations. While these approaches are quickly being adopted by industry, demand flexibility (DF) control strategies remain limited in product offerings. One of the challenges is the lack of documented best-practice DF sequences, despite the rich literature on field implementations. This paper develops a new open-specification for a zone-based temperature adjustment shed strategy for commercial building HVAC systems, describing the specification’s implementation in two EMIS tools in both experimental and field settings. Both implementations successfully reduced electric load by at least 40% on average during the called event, while maintaining temperature limits. This study’s detailed process from specification to deployment shows the potential for scalability as well as highlights challenges related to integration with heterogeneous BAS products.

Granderson, Jessica↗
DOE OSTI2025

Enhancing EnergyPlus capabilities to model dynamic building envelopes using python plugin

Nearly half of the energy consumption in the United States is related to buildings, resulting in an urgent need to develop innovative technologies to improve building energy efficiency. Dynamic building envelopes, comprising switchable insulation and thermal energy storage materials, have been proposed recently as a promising solution to reduce buildings' heating and cooling loads by thermally coupling the indoor environment with the ambient environment when beneficial while decoupling them when outdoor conditions are not favorable. Although various related technologies are still underway, the whole-building energy modeling tools, like EnergyPlus, do not have the capability to simulate the transient and dynamic nature of dynamic envelope materials and components to accurately predict their impact on building energy use. The objective of this study is to formulate a method in EnergyPlus simulation engine to model multilayer envelopes, comprising dynamic building materials with variable thermophysical properties, and discuss the changes made to the program using a Python plugin. Furthermore, the thermal performance of the dynamic envelopes using the proposed method is compared and verified with the results from a well-established commercial code, COMSOL Multiphysics. A parametric assessment is also conducted to evaluate the energy efficiency benefits of dynamic envelopes in a single-family residential building, demonstrating total annual energy savings up to 11.6 %, when a dynamic envelope operates alone, and up to 18.2 % when it is combined with a thin layer of phase change material as a thermal storage medium. Finally, a United States wide energy efficiency assessment is presented to showcase the geographical spread of the energy savings. The method designed and implemented in this study provides the researchers with the ability to implement their dynamic insulation methods in EnergyPlus and evaluate the whole building energy impact.

25 ENERGY STORAGE↗
DOE OSTI2025

Energy efficiency in industrial drying: A hybrid ultrasonic system with a novel dynamic optimization framework

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agrifood products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multiobjective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Dynamic optimization↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2024

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization

We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with parameters of the SWAT+ watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. Furthermore, these test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Multidisciplinary Design, Analysis, and Optimization (MDO) for Co-Designed Transmission & Distribution Electric Grid Planning

This paper describes early experiences and example use cases applying multi-disciplinary design analysis and optimization (MDO) to the integrated design of power grids. Adapted from aerospace, MDO enables combining multiple existing tools into a coordinated optimization. Here we use MDO to simultaneously capture integrated transmission-distribution and investment-engineering trade-offs in an automated framework. Example use cases showcase prototype interactions among existing grid models using MDO and hint at the types of integrated analyses enabled by this approach. In addition, we share experiences and thoughts on grid-specific challenges and opportunities to help advance further work in this area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8) for advanced open-source tritium transport and fuel cycle modeling

Tritium management is critical for the safety, sustainability, and economics of fusion energy systems, and advanced and reliable modeling tools help accelerate the development of tritium technologies. This paper presents the Tritium Migration Analysis Program, Version 8 (TMAP8), an open-source, MOOSE-based application developed to provide state-of-the-art tritium transport and fuel cycle modeling capabilities. TMAP8 aims to expand the capabilities of previous versions (i.e., TMAP4 and TMAP7) by leveraging modern computational techniques, ensuring high software quality assurance standards (key to building trust), and enabling multispecies, multiscale, and multiphysics simulations for integrated tritium transport modeling in complex geometries. This paper outlines TMAP8’s scope and rigorous development practices, emphasizing its transparency, accessibility, modularity, and reliability. We present the current suite of verification and validation cases based on those from TMAP4, demonstrating TMAP8’s accuracy and reliability against analytical solutions and experimental data. Additionally, the paper showcases TMAP8’s integrated fuel cycle modeling capabilities, highlighting its applicability at various scales and levels. The TMAP8 code and documentation are openly available, promoting collaborative development and widespread adoption within the fusion community. Future work will soon expand TMAP8’s verification and validation suite to include those from TMAP7 and other recent experimental studies for validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

3D interface size effects on slip transfer in Ti/Nb nanolaminates

Two-phase nanolaminates are well-renowned for achieving extraordinarily high strengths but at the sacrifice of reduced toughness and strain to failure. Recently ”thick” interfaces, or so called 3D interfaces, in Cu/Nb nanolaminates were experimentally shown to improve both of these mechanical properties. Here, in this work, we study the effect of 3D interfaces in the hexagonal close packed (HCP)/body centered cubic (BCC) Ti/Nb nanolaminate system. Nanoindentation hardness testing suggests increased strength with the introduction of a 3D Ti–Nb interface and a positive size effect with increases in 3D interface thickness from 5 nm to 20 nm. To understand this effect from a single dislocation perspective, we present a phase-field dislocation dynamics (PFDD) model for multi-phase HCP/BCC systems. We employ the model to simulate stress-driven transfer of single dislocations across 3D Ti/Nb interfaces of various thicknesses. Our results show that the critical stress for slip transfer increases with the thickness of the interface. This positive size effect is stronger for transfer from basal or prismatic dislocations in the Ti layer to 110$\langle$111$\rangle$ dislocations in the Nb layer than the reverse. For this Ti/Nb system, a critical thickness of 2 nm is identified at which the asymmetry in slip transfer is minimized. This work showcases 3D interfaces as a beneficial microstructure modification to strengthen as well as reduce anisotropy in nanocrystalline materials containing HCP phases.

Dislocations↗
DOE OSTI2024

A high efficiency rooftop air conditioning system using multi-speed compressors

This study delineates a meticulous exploration of technologies to enhance the energy efficiency of rooftop air conditioning units, employing the DOE/ORNL heat pump design model for comprehensive engineering design and optimization. A baseline rooftop air conditioning unit, featuring a 13 ton (45.7 kW) cooling capacity and a 17.9 integrated energy efficiency ratio, served as the point of departure for substantive efficiency enhancements. Key modifications included the consolidation of two refrigerant circuits into one, integrating three parallel 2-stage (dual-speed) compressors, fan replacements with high-efficiency substitutes. Notably, a lower global warming potential refrigerant, R452B, was evaluated as a substitute for R-410A, demonstrating better performance in the lab prototype. Further, the achieved measured integrated energy efficiency ratio of 21.4 in the lab prototype surpassed the baseline integrated energy efficiency ratio. Comparative evaluations between R410A and R452B indicated heightened efficiency with the latter, showcasing a lab-demonstrated integrated energy efficiency ratio of 22.4 at the rated capacity of 13.8 ton (48.5 kW) and 23.9 integrated energy efficiency ratio at the rated capacity of 10 ton (35.2 kW). This research underscores the successful development of a rigorous, energy efficient rooftop air conditioning unit prototype with noteworthy environmental and economic implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Structural evolution and magnetic hardness of (Sm,Zr)(Fe,Co,Ti) 12 alloy particles via reduction-diffusion

The quest for achieving high coercivity in Sm(Fe,Co,Ti) 12 alloys, despite their inherent strong magnetocrystalline anisotropy, has posed significant challenges. Recently, (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles have exhibited coercivity μ 0 H c > 1.2 T, showcasing promising prospects and significant potential for both manufacturing and research endeavors. This study delves into the structural evolution of (Sm,Zr)(Fe,Co,Ti) 12 (1:12) alloy particles made via the calciothermic reduction-diffusion synthesis process as influenced by the molar ratios of Ca atoms to O 2- ions (Ca/O), annealing time and annealing temperature. Critical insight that informs conditions to optimize the magnetic response is gained via systematic experimentation and advanced electron microscopy. Complex structural features, including core-shell morphologies and intricate multiphase compositions within individual particles, are unveiled. An optimal Ca/O ratio of 1.30 produces particles with a coercivity up to μ 0 H c = 1.63 T, while higher Ca/O ratios induce the formation of a Sm-rich TbCu 7 -type (1: 7 ) phase, which only partially transforms into the desired 1:12 phase during annealing. Persistent remnants of the 1:7 phase locally impact atomic structure, particle morphology, and coercivity. Furthermore, these findings underscore the complex interplay between synthesis parameters, resulting structures, and magnetic properties, informing the design and optimization of high-performance permanent magnets comprised of the (1:12) compound.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Structural interactions of TLP18.3 and Psb27-H1 to the luminal CP43 and Rubredoxin-ENH1 to the stromal side of Photosystem II in higher plants

TLP18.3 and Psb27 are known proteins on the luminal side of photosystem II. The structural locations of these two proteins are still absent in the currently available higher plant photosystem II cryo-EM structures. We interrogated the structural locations of these proteins using chemical cross-linking followed by liquid chromatography/tandem MS analysis. Structural mass spectrometry results then provided chemical restrains to direct structural modelling to determine the collective binding/stabilization of these two proteins to the luminal PSII CP43 protein. Using this pipeline, we also found the structural location of a Rubredoxin protein on the stromal side of PSII. Discovery of this redox active iron-sulfur protein in the vicinity of PSII subunit D1/D2 proteins, greatly showcases the importance of the redox processes that are potentially involved in PSII assembly or less known steady state functionality or photoprotection. This structural mass spectrometry platform high-lights its powerful applicability in protein complex discovery.

59 BASIC BIOLOGICAL SCIENCES↗