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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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The ZEV Ready Center provides a step-by-step process to plan for fleet electrification, design charging infrastructure, and support vehicle operators. This presentation describes how the ZEV Ready Center can be used by local communities and focuses particularly on the ZEV Planning and Charging (ZPAC) tool to identify good candidates for fleet electrification and charging station requirements.
Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).
Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.
Here, the goal of the present work was to provide the necessary reaction emulation information to enable detailed process simulation of a chemical looping H 2 production system from fossil fuels using CaFe 2 O 4 . This specifically pertained to the necessary kinetic data, reaction model development, and model rate parameters required for reaction emulation in both reducing and oxidizing environments. A logical methodology was defined, which included discretization of the reaction network, establishing a core model for reaction emulation that could be adapted based on the system phenomena, and development of a rate parameter regression tool designed around the core model. An extensive array of data sets was acquired by which parametric regressions were performed. The work presented and tabulated a comprehensive set of rate parameters for the reduction and oxidation reactions of CaFe 2 O 4 and descendent phases of Ca 2 Fe 2 O 5 , FeO, Fe 3 O 4 , Fe, and CaO to emulate reaction behavior in a looping-based process environment. This included direct reduction using CH 4 , H 2 , and CO, and direct oxidation reactions with steam, CO 2 and O 2 . Dynamic equilibrium was quantified for reactions that could utilize H 2 O and CO 2 as soft oxidants to re-saturate lattice oxygen in the depleted structure/phases. The kinetics associated with the oxidative mechanisms with the soft oxidants were quantified and compared to those of the reducing counterparts. The analysis provided critical insight to emulate reactions for a process that seeks to use natural gas (NG) or other fossil fuels as a direct reductant for the end goal of H 2 production.
In a recent work, we have reported outstanding strength and work hardening exhibited by a metastable high entropy alloy (HEA), Fe 42 Mn 28 Co 10 Cr 15 Si 5 (in at. %), undergoing the strain-induced martensitic transformation from metastable gamma austenite (γ) to stable epsilon martensite (ε). However, the alloy exhibited poor ductility, which was attributed to the presence of the brittle sigma (σ) phase in its microstructure. The present work reports the evolution of microstructure, strength, and ductility of a similar HEA, Fe 38.5 Mn 20 Co 20 Cr 15 Si 5 Cu 1.5 (in at. %), designed to suppress the formation of σ phase. A cast and then rolled plate of the alloy was processed into four conditions by annealing for 10 and 30 min at 1100 °C and by friction stir processing (FSP) at tool rotation rates of 150 and 400 revolutions per minute (RPM) to facilitate detailed examinations of variable initial grain structures. Neutron diffraction and electron microscopy were employed to characterize the microstructure and texture evolution. The initial materials had variable grain size but nearly 100% γ structure. Diffusionless strain induced γ→ε phase transformation took place under compression with higher rate initially and slower rate at the later stages of deformation, independent on the initial grain size. The transformation facilitated part of plastic strain accommodation and rapid strain hardening owing to a transformation-induced dynamic Hall-Petch-type barrier effect, increase in dislocation density, and texture. Here, the peak strength of nearly 2 GPa was achieved under compression using the structure created by double pass FSP (150 RPM followed by 150 RPM). Remarkably, the tensile elongation exhibited by the alloy was nearly 20% with fracture surfaces featuring a combination of ductile dimples and cleavage.
The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.
The tubular flow-through electrode (FTE) is a facile electroanalytical tool for investigating electrochemical reaction kinetics; however, its suitability for this purpose requires careful design and operation under conditions that guarantee uniform current distribution. In this perspective article, we provide a scaling analysis of the transport and reaction processes within a tubular FTE leading to quantitative guidelines for the FTE design and operation. Two dimensionless parameters (classical Wagner number Wa I and modified Wagner number incorporating the effect of mass transport Wa II ) are utilized to compare the magnitudes of the ohmic, activation and transport resistances of the tubular FTE, and to quantify its current distribution uniformity. With the aid of analytical modeling of the current distribution, optimal ranges for these two dimensionless parameters are defined so as to ascertain uniform current distribution. Application of these guidelines to a graphite tubular FTE is shown to enable the precise determination of the charge-transfer kinetics of the ferri/ferrocyanide redox reaction.
Collection of tools designed to parse documents, such as PDFs, and extract structured elements including URLs, citation contexts, tables, formulas, and figures. This toolset leverages AI-based text extraction and classification methods, providing robust solutions for various scholarly resources processing needs.
A production run of 5550 hermetically sealed cylinder assemblies with precision pressure relief burst discs will be assembled and laser welded at a manufacturing lab at Sandia National Labs. Production of these cylinder assemblies requires many steps, including piece part machining, geometric inspection, cleaning, subassembly, complete assembly via laser welding, and finally leak checking. While this production run is large enough to invest in process optimization and specialized tooling, it is not quite large enough to dedicate new lab space and specify equipment specifically for this job. This study will investigate process parameters and their effects on quality and process flow time, as well as a fixturing design study with the goal of reducing process time while maintaining quality requirements.
An increasing rate of material consumption and a growing population mean that the Earth's supply of natural resources is witnessing an unsustainable and unprecedented demand. This forces us to find solutions that ensure the availability of resources for sustenance of our society in the years to come. Actors from various backgrounds are addressing these challenges from their own perspectives: engineers are developing efficient manufacturing processes, designers are creating lightweight and durable products, educators are incorporating sustainable thinking in their curricula, policymakers are finding interdisciplinary solutions that benefit society at large, etc. Circular economy (CE) is one such approach to resource use that aims to move away from the linear material use framework of "take-make-waste" to a more circular and interdependent system where product, material and resource use is maximized to avoid unnecessary social, economic, and environmental costs. The purpose of this report is to explain how to approach evaluating the circular economy and to help researchers identify existing tools that can be used or that can serve as starting points for their research needs.
In this report a pore-scale model is presented to simulate the dynamic ion transport and adsorption processes in porous electrodes used for capacitive deionization (CDI). The Stokes equation governing water flow is solved using the lattice Boltzmann method and Nernst-Planck equation describing ion transport is solved using the finite volume method. The ion adsorption process is considered at the surface of carbon electrodes. After validation against analytical solutions and published results, the model is used to study the coupled water flow, ion transport and adsorption in both two-dimensional and three-dimensional porous CDI electrodes at the pore scale. The effect of electrode microstructure, electrical potential and flow velocity on the adsorption processes is quantitatively investigated, and the relative importance of various parameters is determined. The presented model can be a powerful numerical tool to quantitatively analyze ion transport and adsorption in porous electrodes, and may provide useful information for the design and optimization of CDI electrodes and operating conditions for desired desalination efficiency, water throughput and cost.
Here, the physics of astrophysical jets can be divided into three regimes: (i) engine and launch (ii) propagation and collimation, (iii) dissipation and particle acceleration. Since astrophysical jets comprise a huge range of scales and phenomena, practicality dictates that most studies of jets intentionally or inadvertently focus on one of these regimes, and even therein, one body of work may be simply boundary condition for another. We first discuss long standing persistent mysteries that pertain the physics of each of these regimes, independent of the method used to study them. This discussion makes contact with frontiers of plasma astrophysics more generally. While observations theory, and simulations, and have long been the main tools of the trade, what about laboratory experiments? Jet related experiments have offered controlled studies of specific principles, physical processes, and benchmarks for numerical and theoretical calculations. We discuss what has been accomplished on these fronts. Although experiments have indeed helped us to understand certain processes, proof of principle concepts, and benchmarked codes, they have yet to solved an astrophysical jet mystery on their own. A challenge is that experimental tools used for jet-related experiments so far, are typically not machines originally designed for that purpose, or designed with specific astrophysical mysteries in mind. This presents an opportunity for a different way of thinking about the development of future platforms: start with the astrophysical mystery and build an experiment to address it.
The increasing availability of high-resolution characterization of natural organic matter (OM) data has shifted the paradigm of lumped descriptions of OM components and potential microbial activities. Our recent development of a substrate-explicit thermodynamic model uniquely enables incorporating complex OM pools to formulate biogeochemical reaction models based on their elemental compositions. While this previous work facilitates prediction of aerobic respiration of complex OM, it is equally imperative to consider the role of non-oxygenic electron acceptors in regulating OM turnover and the fate of carbon. In this study, we significantly expand our previous model by flexibly incorporating both detailed OM chemistry and electron acceptors other than oxygen. Here, our modeling analysis has revealed substantial variations in the energy status of OM molecules across different soils, which drive the co-occurrence of different electron-accepting processes. We demonstrated the effectiveness of the proposed model using a consistency check with experimental data. Through systematic evaluation of the impact of diverse chemical inputs (both electron donors and acceptors) on OM decomposition, the new model also revealed how key microbial growth parameters such as carbon use efficiency (CUE) and reaction rates vary across different electron-accepting processes. Our model provides a unified framework integrating thermodynamic and kinetic constraints on microbial metabolic activities. It complements traditional kinetic models, which are often designed solely to capture mass fluxes. We conclude that thermodynamic modeling emerges as a powerful tool for describing the mechanisms underlying the interplay between microbial growth and OM chemistry and cycling across different electron acceptors, enhancing our ability to project complex ecosystem behaviors in dynamic environments.
The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.
A thermodynamic model was developed and validated to analyze a high-performance solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system for electric aviation. This study used a process simulation software package (ProMax) to study the role of SOFC design and operation on the feasibility and performance of the hybrid system. Standard modules, including compressor, turbine, heat exchanger, reforming reactor, and combustor were used from the ProMax tool suite while a custom module was created to simulate the SOFC stack. The model used an SOFC test data set as an input. Additional SOFC stack performance effects, such as pressure, temperature, and utilization of air and fuel, were added from open source data. System performance predictors were SOFC specific power, fuel-to-electricity conversion efficiency, and hybrid system efficiency. Using these input data and predictors, a static thermodynamic performance model was created that can be modified for different system configurations and operating conditions. Prior to creating the final aircraft performance model, initial demonstration models were developed to validate output results. We used the NASA SOFC model as a benchmark, which was created with their Numerical Propulsion System Simulator (NPSS) software framework. Our output results matched within 1% of both the NASA model and open source SOFC performance data. With confidence gained in the accuracy of this model, a 1-MW SOFC-GT hybrid power system was constructed for an aircraft propulsion concept. Overall hybrid system efficiencies of > 75% FTE were observed during standard 36,000 feet cruise flight conditions.
Continuous carbon fiber prepreg slit and cut into rectangular platelets has proven to be a useful material for net shape molding of semi-structural and structural components in the aerospace and automotive industries. Furthermore, to assist the designer in use of these prepreg platelet molding compounds, sometimes called carbon fiber sheet molding compounds, simulation tools are required that can predict the as-manufactured fiber orientation state which has a significant impact on the resulting performance. Herein, an analysis workflow for design-enabling predictions is demonstrated for a double dome geometry with two different initial charge configurations. Here, the workflow is validated through comparison with experimental short shots, orientation state, and stiffness trends. Significantly, to complete the validation, a method is proposed for determining the confidence bounds on measured orientation state enhancing the results of optical microscopy which can only produce a small sample of platelet orientations.
In this work, we introduce the design of an atomic layer deposition (ALD) reactor augmented with an AI interface for autonomous materials synthesis. Our modular design encapsulates the particularities of the hardware behind a Python interface that communicates with the ALD control software via transmission control protocol. This interface is compatible with model context protocol interfaces used in agentic frameworks. We have integrated our tool with a simple AI agent that leverages a large language model to transform user-supplied queries into ALD processes that are then run in our reactor. Our approach uses a JavaScript object notation schema to encode ALD processes. Our experimental results show that the AI interface does not impose a significant overhead to our control software, at least within our fastest 10 ms scale. We also carried out a detailed evaluation of the agent performance using leading models in two classes of tasks: basic instruction and process discovery tasks, where the agent is presented with a target material and needs to identify the correct ALD process compatible with the reactor configuration. Despite the simplicity of our agent design, we observed that most of the advanced models excelled at the instruction tasks. However, only recent models, such as o1, o3, GPT-5, and Claude Opus 4, performed well in process discovery tasks. We also observed significant variability in the response for the hardest challenges. While the results obtained are promising, we identify areas where AI research could improve the performance of agents for ALD.
CatCost™ enables catalyst researchers to develop comprehensive estimates of the cost to produce pre-commercial catalysts early in the catalyst development process. It includes both web browser and Excel-based tools, each with distinct strengths. The web tool includes powerful visualization capabilities, while the Excel tool allows for high degrees of control and customization by power users. The CatCost™ Python Data Tools are designed to facilitate the transfer of data from the Excel tool to the web tool, so that users can take advantage of both the powerful visualization in the web tool and the higher degree of customization available in the excel tool. Rather than painstakingly copying entries from estimates, the materials, equipment, and spent catalyst libraries from Excel to a JSON format for the web tool, the CatCost™ Data Tools provide an intuitive applet which allows them to simply choose which library or estimate they wish to transfer, and then returns files that integrate smoothly with the web version of the tool.