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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 271 records · Page 15

Enhancing Lithium-Ion Battery Aging Simulations: Coupling a High-Resolution, 3D, Grain-Scale Electromechanical Model to a Single-Particle Model

One of the main goals in modeling lithium-ion batteries is to improve/predict longevity and resilience of new chemistries. Unfortunately, this requires simulation of thousands of charge/discharge cycles, which can be rather time consuming depending on the fidelity of the simulation. The purpose of this talk is to discuss a new modeling framework that couples a high-resolution, continuous damage model (CDM) to a single particle model (SPM) resulting in a good combination of speed and fidelity. In previous work, a 3D, continuum-level chemo-mechanical model was developed to investigate cracking within a single cathode particle comprised of hundreds to thousands of randomly oriented grains. The CDM predicted that particle fracture is primarily due to non-ideal grain interactions with slight dependence on high-rate charge demands. Essentially, when neighboring grains were misaligned, they expanded different rates relative to one another leading to high stresses and ultimately the formation of intraparticle cracks. The model predicted that small particles with large grains develop significantly less damage than larger particles with small grains. Finally, the model predicted most of the chemo-mechanical damage accumulates in the first charge after formation. This chemo-mechanical "damage saturation" effect indicated that initial particle fracture occurs within the first few cycles, while long-term cathode degradation is not solely chemo-mechanically induced. This led to a need for simulating fatigue-like mechanism that degrade the battery over longer time scales. In order to reach the time scales, need to resolve fatigue-like degradation, the CDM needs to be complemented by a faster model. Therefore, recent efforts have been focused on using results from the CDM to inform parameters within the SPM. These parameters are homogenization factors that are associated with diffusion, particle radius, and/or exchange current density. By coupling the CDM to the SPM, the aging simulation is broken up into two domains: short-term and long-term degradation. The short-term degradation occurs over a single cycle and is handled by the CDM due of its high fidelity, but relatively expensive computational cost. Such mechanism include break-in crack caused by mismatches in grain orientation. The long-term degradation occurs over tens of cycles and is handled by the SPM due it's computational efficiency. Most of the fatigue-like mechanism fall into this category. The eventual goal of this modeling framework is to upscale to a psudo-2D model allowing for full electrode simulation, which are informed by high-fidelity grain-scale simulations.

electrochemistry↗

Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy

Abstract. The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models for the use case of wind energy development and operation. Several coupling methods and techniques for generating turbulence at the microscale that is subgrid to the mesoscale have been evaluated for a variety of cases. Case studies included flat-terrain, complex-terrain, and offshore environments. Methods were developed to bridge the terra incognita, which scales from about 100 m through the depth of the boundary layer. The team used wind-relevant metrics and archived code, case information, and assessment tools and is making those widely available. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy.

17 WIND ENERGY↗

An Advanced Meso-Scale Peridynamic Modeling Technology using High-Performance Computing for Cost-Effective Product Design and Testing of Carbon Fiber Reinforced Polymer Composites in Light-weight Vehicles

We study a peridynamic composite modeling technology based on the discontinuous Galerkin finite element method, implemented in the commercial LS-DYNA software, for modeling and prediction of failure in carbon fiber reinforced polymer composites. The proposed technology is developed for the material failure analysis at the meso-scale, which provides the prevailing fiber-matrix interaction mechanism, without adoption of the representative volume element method and thus avoiding complicated numerical calibration procedures. Three types of experimental tests—in-plane coupon test, out-of-plane coupon test, and a component crash test—are simulated in a high-performance computing environment to assess the performance of the proposed peridynamic composite modeling technology.

36 MATERIALS SCIENCE↗

Unsteady aerodynamics of large-scale floating offshore wind turbines in surge motion

Unsteady aerodynamic loads significantly influence the design and wake flow field of floating offshore wind turbines, especially due to wave- or vibration-induced tower top motions triggering various unsteady phenomena. Recent studies show that increasing turbine sizes amplify unsteady aerodynamic effects, as their impact typically grows with rotor diameter. This work combines recent findings from experiments and simulations on model-scale FOWT aerodynamics with new numerical analyses of large-scale rotors, providing a comprehensive understanding of unsteady phenomena occurrence and impact. Numerical analyses of the IEA 15-MW and 22-MW rotors undergoing surge motions characterise the combined influence of motion-induced unsteady phenomena on rotor thrust. Results indicate that unsteady effects can reduce thrust force variations by up to 40% at realistic surge periods. These findings contrast with prior model-scale rotor investigations, attributed to the specialised design of the model rotors. Comparisons between numerical methods - the dynamic blade element momentum method in OpenFAST and free vortex wake modules in panMARE and OpenFAST - reveal persistent differences in thrust predictions under both idealised and realistic conditions. This highlights that classical blade element momentum approaches require enhancement to accurately capture unsteady loads on large-scale floating offshore wind turbines at low wind speeds.

16 TIDAL AND WAVE POWER↗

Optimizing Strategies for Bio-Based Ethanol Production Using Genome-Scale Metabolic Modeling of the Hyperthermophilic Archaeon, Pyrococcus furiosus

The bio-based production of organic chemicals provides a sustainable alternative to fossil-based production in the face of today’s climate challenges. In this work, we present a genome-scale metabolic reconstruction of Pyrococcus furiosus , a well-established platform organism that has been engineered to produce a variety of chemicals and fuels.

Biotechnology & Applied Microbiology↗

Bringing Low- and Moderate-Income Solar Financing Models to Scale (Final Technical Report (FTR))

In this final report, the Clean Energy States Alliance(CESA) and its project partners describe their work to accelerate the development of solar projects for three distinct subsets of the low- to moderate-income(LMI) solar market: single-family homes, manufactured homes, and community institutions, including multifamily affordable housing. For each market sector, the project team undertook research, outreach, and market-building activities. By advancing promising LMI solar financing innovations for different housing types and spreading them to new locations, the Scaling Up Solar for Under-Resourced Communities project has built momentum to scale up LMI solar. Although the report describes some research pertaining to solar adoption in LMI communities, this project was original in that it focused on two specific models to tackle single-family homes and community institutions. Until this project, very little research dedicated to solar for manufactured homes existed. For each market sector, the project team undertook research, outreach, and market-building activities. For the single-family homes sector, the project encouraged states to adapt a program model based on a successful Connecticut initiative that has brought solar to thousands of LMI homeowners. For manufactured homes, the project analyzed the potential for using solar for that housing sector and worked with states, utilities, and other stakeholders to launch pilot projects or programs. For community institutions, the project team worked with foundations, lenders, and community service organizations to inventory, analyze, and communicate models for philanthropic investment that accelerate the deployment of solar and solar+storage in multifamily affordable housing, health centers, and other LMI-serving community institutions.

14 SOLAR ENERGY↗

Resolution matters when modeling climate change in headwaters of the Colorado River

The continued growth of Southwestern cities depends on reliable water export from Rocky Mountain headwaters, which provide ~85% of Colorado River Basin (CRB) streamflow. Despite being more sensitive to warming temperatures, alpine systems are simplified in the regional-scale models currently in use to plan for future water supply. We used an integrated hydrologic model that couples groundwater and surface water with snow and vegetation processes to examine the effect of topographic simplifications as a result of grid coarsening in a representative CRB headwater basin. High-resolution (100 m) simulations predicted headwater streamflow losses of 16% by 2050 while coarse-resolution (1 km) simulations predict only 12%, suggesting that regional-scale models (coarser than 1 km) likely overestimate future Colorado River Basin water supplies.

54 ENVIRONMENTAL SCIENCES↗

Numerical modeling of ion transport and adsorption in porous media: A pore-scale study for capacitive deionization desalination

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Non-standard neutrino interactions in light mediator models at reactor experiments

Compared to other neutrino sources, the huge anti-neutrino fluxes at nuclear reactor based experiments empower us to derive stronger bounds on non-standard interactions of neutrinos with electrons mediated by light scalar/vector mediators. At neutrino energy around 200 keV reactor anti-neutrino flux is at least an order of magnitude larger compared to the solar flux. The atomic and crystal form factors of the detector materials related to the details of the atomic structure becomes relevant at this energy scale as the momentum transfers would be small. Non-standard neutrino-electron interaction mediated by light scalar/vector mediator arises naturally in many low-scale models. We also propose one such new model with a light scalar mediator. Here, we investigate the parameter space of such low-scale models in reactor based neutrino experiments with low threshold Ge and Si detectors, and find the prospect of probing/ruling out the relevant parameter space by finding the projected sensitivity at 90% confidence level by performing a $χ$ 2 -analysis. We find that a detector capable of discriminating between electron recoil and nuclear recoil signal down to a very low threshold such as 5 eV placed in reactor based experiment would be able to probe a larger region in parameter space compared to the previously explored region. A Ge (Si) detector with 10 kg-yr exposure and 1 MW reactor anti-neutrino flux would be able to probe the scalar and vector mediators with masses below 1 keV for coupling products $\sqrt{g_vg_e}$ ~ 1 × 10 -6 (9.5 × 10 -7 ) and 1 × 10 -7 (8 × 10 -8 ), respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Reactor Scale-Up Methodology from Lab-Scale to Pilot-Scale Operations: Numerical Modeling of THFA Dehydration to DHP in Packed-Bed Reactors

This manuscript discusses developing a model-based scale-up methodology for a successful technology transfer of gas-phase catalytic reactors from a lab-scale to a pilot-scale operation. The manuscript demonstrates the methodology for gas-phase dehydration of tetrahydrofurfuryl alcohol (THFA) to dihydropyran (DHP) process over commercial Al 2 O 3 catalysts. A two-dimensional reactor model was developed using COMSOL Multiphysics 6.1 software. The model solves heat and mass transport equations in bed-scale and particle scales simultaneously. This powerful feature enables accurate prediction of the heat and mass transfer limitations in pilot-scale reactors, if any exists. Further, the model uses isothermal lab-scale experimental data to derive and validate the reaction chemistry, flow fields and boundary conditions. The model was then scaled-up to project conversion, selectivity, yield and formation rate of DHP in a pilot-scale reactor. The results highlight the complex nature of chemistry, heat, and mass transfer effects in lab-scale and pilot-scale reactors. The model results inform the possible operational limitations of the pilot-scale reactor and design strategies to improve process efficiency. Although the scale-up approach is explained through the THFA dehydration process, the methodology is applicable to any catalytic packed-bed reactor models for a successful process scale-up.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

Advances in engineering algae for biofuel production

While algae demonstrate potential as a sustainable fuel source, low productivities limit the economic realization of algal biofuels. High-throughput strain engineering, omics-informed genome-scale modeling, and microbiome engineering are key technologies for enabling algal biofuels. High-throughput strain engineering efforts generate improved traits, including high biomass productivity and lipid content, in diverse algal species. Genome-scale models, constructed with the aid of omics data, provide insight into metabolic limitations and guide rational algal strain engineering efforts. As outdoor cultivation systems introduce exogenous organisms, microbiome engineering seeks to eliminate harmful organisms and introduce beneficial species. In conclusion, optimizing algal biomass production and lipid content using these technologies may overcome the productivity barrier for the commercialization of algal biofuels.

09 BIOMASS FUELS↗

Direct numerical simulations for hybrid rocket boundary layers: Performance modeling and scaling

This paper presents a comprehensive performance and scaling analysis of direct numerical simulations for reacting boundary layers, focusing on slab burner configurations. Using a PETSc-based finite volume CFD framework, the study evaluates the scalability and computational cost of flow, chemistry, and radiation evaluations across 2D and 3D simulations. Polymethyl methacrylate (PMMA) is the fuel with pure O 2 as the oxidizer, modeled using a detailed chemical kinetics mechanism with 113 species and 660 reactions. A ray-tracing-based radiation solver, designed for distributed memory applications, is implemented to model radiation heat transfer. Parallel scalability is analyzed for the coupled flow, chemistry, and radiation heat transfer processes. Weak and strong scaling studies are conducted on up to 15,000 computational ranks, revealing robust performance when flow cells exceed 200 per rank. Chemistry evaluations dominate the computational cost in large 3D simulations, accounting for approximately 40% of the total runtime, while flow processes contribute around 35%, and radiation solver contributions remain below 10% due to reduced evaluation frequencies. GPU accelerated chemistry evaluation, implemented with Zero-RK, demonstrates significant promise, achieving up to a 4x speedup for workloads exceeding 30,000 cells per GPU. However, diminishing returns are observed for smaller workloads due to CPU-GPU communication overhead. This study identifies key challenges, including memory bottlenecks and the effects of domain partitioning on flow scalability, while highlighting the potential of GPU-accelerated chemistry to reduce computational costs. In conclusion, these findings provide realizable run configurations for 2D, 3D, and GPU-accelerated cases, offering insights for optimizing reactive flow solvers.

CFD Scalability↗

Harnessing autocatalytic reactions in polymerization and depolymerization

Abstract Autocatalysis and its relevance to various polymeric systems are discussed by taking inspiration from biology. A number of research directions related to synthesis, characterization, and multi-scale modeling are discussed in order to harness autocatalytic reactions in a useful manner for different applications ranging from chemical upcycling of polymers (depolymerization and reconstruction after depolymerization), self-generating micelles and vesicles, and polymer membranes. Overall, a concerted effort involving in situ experiments, multi-scale modeling, and machine learning algorithms is proposed to understand the mechanisms of physical and chemical autocatalysis. It is argued that a control of the autocatalytic behavior in polymeric systems can revolutionize areas such as kinetic control of the self-assembly of polymeric materials, synthesis of self-healing and self-immolative polymers, as next generation of materials for a sustainable circular economy. Graphic Abstract

36 MATERIALS SCIENCE↗

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗

Transmission Constraint Screening for Production Cost Modeling at Scale

Transmission constraint calculation and screening, for both unit commitment and economic dispatch, is a critical feature of a performant solution method, but one that is often overlooked in the literature. In this talk, we will discuss the transmission constraint calculation and screening algorithm implemented in the open-source Egret package for electrical grid optimization and compare it against the more straightforward approach Egret originally implemented. Finally, we discuss the implications for transmission constraint sharing within a production cost modeling simulation.

DCOPF↗

Insensitivity to propagation timing in a preview-enabled wind turbine control experiment

Lidar scanners are capable of taking measurements of a wind field upstream of a wind turbine. The wind turbine controller can use these measurements as a “preview” of future disturbances impacting the turbine. Such preview-enabled (or feedforward) controllers show superior performance to standard wind turbine control configurations based purely on a feedback architecture. To capitalize on the performance improvements that preview wind measurements can provide, feedforward control actions should be timed to coincide with the arrival of the wind field at the wind turbine location. However, the time of propagation of the wind field between the lidar measurement location and the wind turbine is not perfectly known. Moreover, the best time to take feedforward control action may not perfectly coincide with the true arrival time of the wind disturbance. This contribution presents results from an experiment where preview-enabled model predictive control was deployed on a fully-actuated, scaled model wind turbine operating in a wind tunnel testbed. In the study, we investigate the sensitivity of the controller performance to the assumed propagation delay using a range of wind input sequences. We find that the preview-enabled controller outperforms the feedback only case across a wide range of assumed propagation delays, demonstrating a level of robustness to the time alignment of the incoming disturbances.

17 WIND ENERGY↗