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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

BISON Robustness and Performance Improvements

BISON is a modern finite-element based nuclear fuel performance code that has been under development at the Idaho National Laboratory (USA) since 2009 [1]. The code is applicable to both steady and transient fuel behavior and can be used to analyze 1D (spherically symmetric), 2D (axisymmetric and generalized plane strain) or 3D geometries. BISON is the fuel performance code used within CASL for LWR fuel under both normal operating and accident conditions. BISON is built using the INL Multiphysics ObjectOriented Simulation Environment, or MOOSE [2, 3]. MOOSE is a massively parallel, finite element-based framework to solve systems of coupled non-linear partial differential equations using the Jacobian-Free Newton Krylov (JFNK) method [4]. This enables investigation of computationally large problems, for example a full stack of discrete pellets in a LWR fuel rod, or every rod in a full reactor core. MOOSE supports the use of complex two and three-dimensional meshes and uses implicit time integration, important for the widely varied time scale in nuclear fuel simulation. An object-oriented architecture is employed which greatly minimizes the programming effort required to add new material and behavioral models. The flexibility of the implicit and fully coupled multiphysics approach comes with a need for constructing suitable approximations for the Jacobian matrix of the coupled system used for either preconditioning a Krylov solve or in a direct Newton solve. Preconditioning options for Bison problems need to be revisited with new preconditioning methods becoming available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Operando study of mechanical integrity of high-volume expansion Li-ion battery anode materials coated by Al 2 O 3

Group IV elements and their oxides, such as Si, Ge, Sn and SiO have much higher theoretical capacity than commercial graphite anode. However, these materials undergo large volume change during cycling, resulting in severe structural degradation and capacity fading. Al 2 O 3 coating is considered an approach to improve the mechanical stability of high-capacity anode materials. To understand the effect of Al 2 O 3 coating directly, we monitored the morphology change of coated/uncoated Sn particles during cycling using operando focused ion beam–scanning electron microscopy. Here, the results indicate that the Al 2 O 3 coating provides local protection and reduces crack formation at the early stage of volume expansion. The 3 nm Al 2 O 3 coating layer provides better protection than the 10 and 30 nm coating layer. Nevertheless, the Al 2 O 3 coating is unable to prevent the pulverization at the later stage of cycling because of large volume expansion.

25 ENERGY STORAGE↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Demonstration of a Continuous Motion Direct Air Capture System

Global Thermostat (GT) has developed a process that addresses the primary technical challenges associated with direct air capture (DAC): the ability to process enormous volumetric flowrates of air and provide the energy for regeneration at acceptable cost. However, at current, there is opportunity for improvement in overall capture costs and especially in capital costs. While GT has advanced its base technology to TRL levels supporting commercial deployments, its research strategy includes alternative technology embodiments that enable substantial reductions in the cost per tonne of CO 2 removed. The objective of this project is to advance the most promising of these embodiments, where a DAC plant is operated in a continuous fashion rather than a discrete stepwise fashion (GT’s commercial scale technology approach). The Continuous Motion Direct Air Capture (cDAC) System’s advantages over the GT baseline stepwise DAC platform have been proposed primarily as a reduction in the complexity required for starting and stopping a movement system, relaxing requirements for other components designed for a rapid switching application, and a flattening of the sharp instantaneous fluid flowrates into steady-state mass & energy flows to enable smoother operation and easier heat integration. These advantages can lead to shorter cycle times, greater plant reliability, and lower capital expense. The primary objective of this project is the design, construction, commissioning, and operation of a field-test unit (FTU) at a scale in the range of ~200 tons CO 2 per year. Data generated from the operation campaign would then be used in a prescreening techno-economic analysis (TEA) and life cycle analysis (LCA), allowing this process to be compared to other carbon capture technologies.

42 ENGINEERING↗
DOE OSTI2024

An Electrical Resistance Diagnostic for Conductivity Monitoring in Laser Powder Bed Fusion

With the growing interest in metal additive manufacturing using laser powder bed fusion (LPBF), there is a need for advanced in-situ nondestructive evaluation (NDE) methods that can dynamically monitor manufacturing process-related variations, that can be used as a feedback mechanism to further improve the manufacturing process, leading to parts with improved microstructural properties and mechanical properties. Current NDE techniques either lack sensitivity beyond build layer, are costly or time-consuming, or are not compatible for in-situ integration. In this research, we develop an electrical resistance diagnostic for in-situ monitoring of powder fused regions during laser powder bed fusion printing. The technique relies on injecting current into the build plate and detecting voltage differences from conductive variations during printing using a simple, cheap four-point electrode array directly connected to the build plate. A computational model will be utilized to determine sensitivities of the approach, and preliminary experiments will be performed during the printing process to test the overall approach.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

3D printed materials, stress-strain behavior, mult↗
DOE OSTI2024

Model-based optimization strategies for direct hydrogenation of carbon dioxide to dimethyl ether

Here, this study discusses model-based optimization strategies for CO 2 hydrogenation to dimethyl ether (DME) over aCuZnZr (CZZ) and ferrite (FER) mixed catalyst system in a packed-bed reactor configuration. A two-dimensional axisymmetric, nonisothermal packed-bed reactor model was developed using COMSOL Multiphysics 6.2 software. The model solves two-dimensional (radial and axial) heat and mass transport equations in the packed-bed and integrates intraparticle diffusion and heat transfer in a 1D approach. This powerful feature differs from a traditional porous media approach and takes into account any heat and mass transfer limitations that may exist. Analysis shows that the heat transfer limitations are negligible, but strong internal mass transfer limitations were observed at 10 ≤ WHSV ≤ 90 h -1 on the FER catalyst and at 240 °C. The optimum catalyst composition (i.e., mixing ratio) varies depending on the operating regime. The FER catalyst weight in the mixture can be as low as 5 wt.%, but the ideal composition de-pends on the internal mass transfer limitation and its relationship with the operating regime (i.e., weight hourly space velocity, temperature). A catalyst composition of 80 wt.% CZZ and 20 wt.% FER was suggested; this composition can provide high CO 2 conversion and DME production rates at a wide range of temperatures and flow rates.

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

Strain-retardant coherent perovskite phase stabilized Ni-rich cathode

The use of state-of-the-art Ni-rich layered oxides (LiNi x Co y Mn 1-x-y O 2 , x > 0.5) as the cathode material for lithium-ion batteries can push the energy and power density to a higher level than is currently available. However, volume variation associated with anisotropic lattice strain and stress that is being developed during lithium (de) intercalation induces severe structural instability and electrochemical decay of the cathode materials, which is amplified further when the battery is operating at a high voltage (above 4.5 V), which is essential for unlocking its high energy. Even after much effort by the research community, an intrinsic strain-retardant method for directly alleviating the continuous accumulation of lattice strain remains elusive. By introducing a coherent perovskite phase into the layered structure functioning as a ‘rivet’, we significantly mitigate the pernicious structural evolutions by a pinning effect. The lattice strain evolution in every single cycle is markedly reduced by nearly 70% when compared with conventional materials, which significantly enhances morphological integrity leading to a notable improvement in battery cyclability. This strain-retardant approach broadens the perspective for lattice engineering to release the strain raised from lithium (de)intercalation and paves the way for the development of high-energy-density cathodes with long durability.

25 ENERGY STORAGE↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2022

Size Effect of Local Current-Voltage Characteristics of MX 2 Nanoflakes: Local Density of States Reconstruction from Scanning Tunneling Microscopy Experiments

Local current-voltage characteristics for low-dimensional transition-metal dichalcogenides (LDTMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments, are of fundamental interest and can be useful for advanced applications. Most of the existing models either have limited applicability for complex-shaped LDTMDs (e.g., those based on the Simmons approach) or require solving of an ill-defined integral equation to deconvolute the unknown LDOS (e.g., those based on the Tersoff approach). Using a serial expansion of the Tersoff formulas, we propose a flexible method to reconstruct the LDOS from local current-voltage characteristics measured in STM experiments. We establish a set of key physical parameters, which characterize the tunneling current of a STM-probe–sample contact and the sample LDOS expanded in Gaussian functions. Using a direct variational method coupled with probabilistic analysis, we determine these parameters from the STM experiments for MoS2 nanoflakes with different numbers of layers. The main result is the reconstruction of the LDOS in a relatively wide energy range around the Fermi level, which allows us to gain insight into the local band structure of LDTMDs. The reconstructed LDOS reveal pronounced size effects for the single-layer, two-layer, and three-layer MoS 2 nanoflakes, which we relate to the low dimensionality and strong bending or corrugation of the nanoflakes. We hope that the proposed elaboration of the Tersoff approach, allowing LDOS reconstruction, will be of critical interest for the quantitative description of STM experiments and also of use to better understand the microscopic physical aspects of the surface, strain, and bending contributions to the LDTMDs’ electronic properties.

42 ENGINEERING↗
DOE OSTI2022

Machine learning of consistent thermodynamic models using automatic differentiation

In this study, we propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Moving from Information Assurance to Functional Assurance with Engineered Controls

Cyber threats to operational technology demand more than traditional IT defenses—they require full-spectrum mission assurance. Cyber-Informed Engineering (CIE) is an approach that embeds engineered controls into system design to ensure critical functions remain safe and reliable, even under attack. Unlike conventional cybersecurity tools, engineered controls act directly on physical processes to prevent unacceptable outcomes such as equipment damage or mission failure. This session will outline the CIE framework and share examples of consequence-based design that deliver true resilience, not just fail-safe behaviors. Attendees will learn how to integrate these principles into the engineering lifecycle to support resilient-by-design architectures and inform emerging standards. This talk sets the stage for the panel discussion on advancing CIE across sectors as digital and physical systems converge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Mechanistic Insights into the Electrochemical Oxidation of 5-Hydroxymethylfurfural on a Thin-Film Ni Anode

The electrochemical oxidation of alcohols is being explored as a favorable substitute for the oxygen evolution reaction owing to its capability to generate high-value products and lower overpotentials. Herein, we present a systematic investigation into the electrochemical oxidation of 5-hydroxymethylfurfural (HMF), a model biomass platform chemical, on a thin-film nickel catalyst, aiming to investigate the underlying reaction mechanism and shed light on the role of the catalyst’s microenvironment and phase on activity and product selectivity. Utilizing a combined experimental and computational approach, we demonstrate that NiOOH is the active phase for HMF oxidation. Additionally, we find a substantial impact of the electrochemical environment, particularly the electrolyte pH, on the reaction. Under highly alkaline conditions (pH = 13), higher activity for HMF oxidation is observed, accompanied by an increased selectivity toward 2,5-furandicarboxylic acid (FDCA) production. Conversely, a less alkaline environment (pH = 11) results in diminished HMF oxidation activity and a higher preference for the partial oxidation product 2,5-diformylfuran (DFF). Mechanistic insights from DFT studies reveal that geminal diols that are present under highly alkaline conditions undergo hydride transfer via HMFCA, while a shift to an alkoxide route occurs at a lower pH, favoring the DFF pathway. Hydride transfer energetics are also strongly affected by the surface Ni oxidation state. Finally, this integrated approach, bridging experimental and computational insights, provides a general framework for investigating the electrochemical oxidation of aldehydes and alcohols, thereby advancing rational design strategies in electrocatalysts for alcohol electro-oxidation reactions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Integrating catalytic fractionation and microbial funneling to produce 2-pyrone-4,6-dicarboxylic acid and ethanol

Replacing biorefinery designs that use stepwise fractionation, depolymerization, and conversion processes with designed tandem process steps can greatly reduce a biorefinery's operating costs and environmental impact. Reductive Catalytic Fractionation (RCF) is a highly efficient lignin-first approach that combines biomass fractionation and lignin depolymerization to generate a hydrogenolysis oil and pulp. The oil is composed of a complex mixture of phenolic monomers, dimers, and oligomers. Intergrating this chemical deconstruction process with microbial funneling of phenolics can simplify the product mixture and make high-value products. We applied RCF to poplar biomass in a biomass-to-bioproduct processing chain in which the phenolics were funneled to 2-pyrone-4,6-dicarboxylic acid (PDC) by an engineered strain of Novosphingobium aromaticivorans DSM12444. The pulp was enzymatically digested and the glucose and xylose was funneled to ethanol by an engineered strain of Saccharomyces cerevisiae GLBRCY945. By combining biomass fractionation and lignin depolymerization we removed a costly processing step that directly translated into a 29% reduction in the minimum selling price of PDC. This work combines experimentation with process modeling of an integrated biorefinery design to show how utilizing tandem process steps can significantly reduce operating expenses and environmental impact of upgrading lignocellulosic biomass to a portfolio of high value products.

Sener, Canan [Great Lakes Bioenergy Research Cente↗
DOE OSTI2022

Elimination of human rabies in Goa, India through an integrated One Health approach

Dog-mediated rabies kills tens of thousands of people each year in India, representing one third of the estimated global rabies burden. Whilst the World Health Organization (WHO), World Organization for Animal Health (OIE) and the Food and Agriculture Organization of the United Nations (FAO) have set a target for global dog-mediated human rabies elimination by 2030, examples of large-scale dog vaccination programs demonstrating elimination remain limited in Africa and Asia. We describe the development of a data-driven rabies elimination program from 2013 to 2019 in Goa State, India, culminating in human rabies elimination and a 92% reduction in monthly canine rabies cases. Smartphone technology enabled systematic spatial direction of remote teams to vaccinate over 95,000 dogs at 70% vaccination coverage, and rabies education teams to reach 150,000 children annually. An estimated 2249 disability-adjusted life years (DALYs) were averted over the program period at 526 USD per DALY, making the intervention ‘very cost-effective’ by WHO definitions. This One Health program demonstrates that human rabies elimination is achievable at the state level in India.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Examples of State and Utility Actions on Proactive Planning and Investments

As states across the U.S. confront rising electricity demand, clean energy deployment, grid modernization imperatives, and the integration of large new loads, some regulators and utilities are shifting away from reactive, “just-in-time” investment approaches toward more proactive planning and investment frameworks. This report compiles examples of jurisdictional and utility actions that reshape planning processes, cost recovery mechanisms, and performance oversight to anticipate—rather than simply respond to—future grid needs. Several themes emerge from state actions examined in this report. First, legislatures and commissions are increasingly directing utilities to proactively upgrade their distribution and transmission systems, reflecting a shift toward a forward-looking system that aligns planning with state policy goals. Second, states are establishing long-term, iterative planning frameworks that often feature multi-year horizons, biannual or annual compliance reporting, structured opportunities for stakeholder engagement, and emphasis on collaboration among utilities, regulators, and stakeholders. Third, states are actively investigating innovative cost recovery mechanisms designed to support accelerated electrification and grid modernization, while balancing consumer advocates’ concerns regarding the ratepayer financial risks of premature investments. Fourth, performance metrics and reporting requirements are being developed to ensure transparency and accountability for proactive investments. Fifth, methodological improvements in planning—such as aligning load forecasting assumptions, incorporating sensitivities, and considering load management potential across building, vehicles, storage, and demand response—are recurring areas of stakeholder focus across jurisdictions. Overall, these developments signify a growing recognition among state regulators, utilities, and stakeholders that proactive planning—supported by clear definitions, consistent and transparent methodologies, robust performance metrics, and innovative cost recovery mechanisms—is a tool that can be used to address the scale and urgency of contemporary grid needs.

electricity market↗
DOE OSTI2025

Autonomous direct freeform fabrication strategy for multi-axis additive manufacturing

Multi-axis additive manufacturing (M-AM) enables precise material deposition along both planar and curved layers, eliminating the need for support structures through a continuous material deposition approach. In contrast to conventional 2-dimensional planar layers constrained to a fixed building orientation, the deposition on freeform layers demands the specification of guided curves to determine material deposition directions which are no longer to be fixed to a build direction. There are challenges that arise when fabricating components with multiple “build” directions, necessitating the decomposition of geometries and the specification of guided curves for the resulting volumes. Furthermore, multi-axis systems introduce heightened challenges due to an increased degree of freedom in motion. Consequently, the potential risks of collision between the deposited geometry and the motion platform become a notable concern. This research proposes a freeform layering algorithm to address the challenge of seamless transitions between planar and curved layers in the process planning of M-AM. The algorithm computes 3D “printable” layers by leveraging topological information derived from the geometry to be built and integrates collision-free manufacturability considerations into the computational process. These accumulated volumes serve as a “substrate” and support volumes for subsequent deposition, allowing later layers to be built without the need for additional support material. In conclusion, the proposed method successfully devises a freeform layering approach suitable for intricate models that demand substantial support, thus enabling the fabrication of diverse geometries in a manner previously unachievable.

36 MATERIALS SCIENCE↗