CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “Mathematics and Computing, Energy”
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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Capturing Surface Coverage Effects in Heterogeneous Catalysis
Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.
09 BIOMASS FUELS↗
Universal chemomechanical design rules for solid-ion conductors to prevent dendrite formation in lithium metal batteries
Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. Although high-shear-modulus ( G s ) solid-ion conductors (SICs) have been prioritized to resolve the pressure-driven instabilities that lead to dendrite propagation and cell shorting, it is unclear whether these or alternatives are needed to guide uniform lithium electrodeposition, which is intrinsically density-driven. Here, we show that SICs can be designed within a universal chemomechanical paradigm to access either pressure-driven dendrite-blocking or density-driven dendrite-suppressing properties, but not both. This dichotomy reflects the competing influence of the SIC’s mechanical properties and the partial molar volume of Li + $(V_{\mathrm{Li}^+})$ relative to those of the lithium anode ( G Li and V Li ) on plating outcomes. Within this paradigm, we explore SICs in a previously unrecognized dendrite-suppressing regime that are concomitantly ‘soft’, as is typical of polymer electrolytes, but feature an atypically low $(V_{\mathrm{Li}^+})$ that is more reminiscent of ‘hard’ ceramics. Li plating (1 mA cm -2 ; T = 20 °C) mediated by these SICs is uniform, as revealed using synchrotron hard X-ray microtomography. As a result, cell cycle life is extended, even when assembled with thin Li anodes (~30 µm) and either high-voltage NMC-622 cathodes (1.44 mAh cm -2 ) or high-capacity sulfur cathodes (3.02 mAh cm -2 ).
25 ENERGY STORAGE↗
Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-fidelity kinetic modeling of instabilities and gyromotion physics in nonuniform low-beta plasmas
A fourth-order accurate continuum kinetic Vlasov solver and a systematic method for constructing customizable kinetic equilibria are demonstrated to be powerful tools for the study of nonuniform collisionless low-beta plasmas. The noise-free methodology is applied to investigate two gradient-driven instabilities in 4D (x,y,vx,vy) phase space: the Kelvin–Helmholtz instability and the lower hybrid drift instability. Nonuniform two-species configurations where ion gyroradii are comparable to gradient scale lengths are explored. The approach sheds light on the evolution of the pressure tensor in Kelvin–Helmholtz instabilities and demonstrates that the associated stress tensor deviates significantly from the gyroviscous stress tensor. Even at high magnetization, first-order approximations to finite-gyromotion physics are shown to be inadequate for the Kelvin–Helmholtz instability, as shear scales evolve to become on par with gyromotion scales. The methodology facilitates exploring transport and energy partitioning properties associated with lower hybrid drift instabilities in low-beta plasma configurations. Distribution function features are captured in detail, including the formation of local extrema in the vicinity of particle-wave resonances. Finally, the approach enables detailed targeted investigations and advances kinetic simulation capability for plasmas in which gyromotion plays an important role.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Scalable Circuits for Preparing Ground States on Digital Quantum Computers: The Schwinger Model Vacuum on 100 Qubits
The vacuum of the lattice Schwinger model is prepared on up to 100 qubits of IBM’s Eagle-processor quantum computers. A new algorithm to prepare the ground state of a gapped translationally invariant system on a quantum computer is presented, which we call “scalable circuits ADAPT-VQE” (SC-ADAPT-VQE). This algorithm uses the exponential decay of correlations between distant regions of the ground state, together with ADAPT-VQE, to construct quantum circuits for state preparation that can be scaled to arbitrarily large systems. These scalable circuits can be determined with use of classical computers, avoiding the challenging task of optimizing parameterized circuits on a quantum computer. SC-ADAPT-VQE is applied to the Schwinger model, and is shown to be systematically improvable, with an accuracy that converges exponentially with circuit depth. Both the structure of the circuits and the deviations of prepared wave functions are found to become independent of the number of spatial sites, L . This allows a controlled extrapolation of the circuits, determined with use of small or modest-sized systems, to arbitrarily large L . The circuits for the Schwinger model are determined on lattices up to L = 14 (28 qubits) with the Qiskit classical simulator, and are subsequently scaled up to prepare the L = 50 (100 qubits) vacuum on IBM’s 127-superconducting-qubit quantum computers ibm_brisbane and ibm_cusco. After introduction of an improved error-mitigation technique, which we call “operator decoherence renormalization”, the chiral condensate and charge-charge correlators obtained from the quantum computers are found to be in good agreement with classical matrix product state simulations. Published by the American Physical Society 2024
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
HELICS: A Co-Simulation Framework for Scalable Multi-Domain Modeling and Analysis
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24 POWER TRANSMISSION AND DISTRIBUTION↗
Evaluation of Converter Performance Considering Static and Dynamic Device Part-to-Part Variability
This paper presents a methodology to incorporate and analyze the impact of semiconductor device part-to-part variation on power converter performance. By integrating extensive static and dynamic device characterization data with an automated compact model generation process that reflects manufacturing variability, device models with inherent variability features are utilized in converter simulations for a comprehensive assessment of performance impacts. The traditional converter performance evaluation process typically yields fixed efficiency values, often dismissing the inherent part-to-part variability caused by the manufacturing process of semiconductor devices. To address this limitation, a large population of devices was characterized to capture variations in static parameters-such as transfer, output, and capacitance characteristics-as well as dynamic behaviors, including switching losses. This data-driven approach enables the development of individual compact models, which were then integrated into converter simulations to evaluate efficiency ranges rather than single point estimated values. The converter simulation results show that part-to-part component variation can lead to significant efficiency deviations, exceeding several percentage points in high-power conversion applications. By offering a more accurate representation of converter behavior under real-world manufacturing conditions, this methodology enables designers to anticipate performance variability, improving the robustness of power converter designs.
device characterization↗
Scalable Multiphysics Block Preconditioning for Low Mach Number Compressible Resistive MHD with Application to Magnetic Confinement Fusion
This study investigates multiphysics block preconditioners that are critical in devising scalable Newton–Krylov iterative solvers for longer time-scale fully implicit fluid plasma models. The specific model of interest is the visco-resistive, low Mach number, compressible magnetohydrodynamics (MHD) model. This model describes the dynamics of conducting fluids in the presence of electromagnetic fields and can be used to study aspects of astrophysical phenomena, important science and technology applications, and basic plasma physics. The specific application of interest that motivates this study is the macroscopic simulation of longer time-scale stability and disruptions of magnetic confinement fusion devices, specifically the ITER Tokamak. The computational solution of the governing balance equations for mass, momentum, heat transfer, and magnetic induction for resistive MHD systems can be extremely challenging. These difficulties arise from both the strong nonlinear, nonsymmetric coupling of fluid and electromagnetic phenomena as well as the significant range of time and length scales that the interactions of these physical mechanisms produce. To handle the range of time and spatial scales of interest, a fully implicit unstructured variational multiscale finite element formulation is employed. For the scalable solution of the Newton linearized systems, fully coupled block preconditioners are designed to leverage algebraic multigrid subsolves. In conclusion, results are presented for the strong and weak scaling of the method as well as the robustness of these techniques for a large range of Lundquist numbers.
97 MATHEMATICS AND COMPUTING↗
Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA
Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.
15 GEOTHERMAL ENERGY↗
Application-Driven Creation of Building Metadata Models with Semantic Sufficiency
Semantic metadata models such as Brick, RealEstateCore, Project Haystack, and BOT promise to simplify and lower the cost of developing software for smart buildings, enabling the widespread deployment of energy efficiency applications. However, creating these models remains a challenge. Despite recent advances in creating models from existing digital representations like point labels and architectural models, there is still no feedback mechanism to ensure that the human input to these methods results in a model that can actually support the desired software. In this paper, we introduce the notion of semantic sufficiency, a practical principle for semantic metadata model creation that asserts that a model is "finished" when it contains the metadata necessary to support a given set of applications. To support semantic sufficiency, we design a standard representation for capturing application metadata requirements and a templating system for generating common metadata model components with limited user input. We then construct an iterative model creation workflow that integrates metadata requirements to direct the model creation effort, and present several novel optimizations that increase the model utility while minimizing the effort by a human operator. These new abstractions for model creation and validation lower model development costs and ensure the utility of the resulting model, thus facilitating the adoption of intelligent building applications.
applications↗
Impact of Tropical and Extratropical Cyclones on Future U.S. Offshore Wind Energy
Over 60 participants, including government officials, regulators, certification bodies, national laboratory researchers, academia, and industry representatives, gathered in person twice for a comprehensive discussion on the impacts of extreme weather on large-scale deployment of offshore wind energy for the U.S. The dialogue focused on addressing modeling challenges, the need for detailed observational data, refining risk assessment methodologies, and understanding the implications of climate change.
14 SOLAR ENERGY↗
Improved Representation of Horizontal Variability and Turbulence in Mesoscale Simulations of an Extended Cold-Air Pool Event
Abstract Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored. Significance Statement In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.
17 WIND ENERGY↗
Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics
Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.
Energy - Wind↗
A Moving-Wave Implementation in WRF to Study the Impact of Surface Water Waves on the Atmospheric Boundary Layer
Abstract While numerous modeling studies have focused on the interaction of ocean surface waves with the atmospheric boundary layer, most employ idealized waves that are either monochromatic or synthetically generated from a theoretical wave spectrum, and the atmospheric solvers are typically incompressible. To study wind–wave coupling in real-world scenarios, a model that can simulate both realistic meteorological and wave conditions is necessary. In this paper we describe the implementation of a moving bottom boundary condition into the Weather Research and Forecasting Model for large-eddy simulation applications. We first describe the moving bottom boundary conditions within WRF’s pressure-based vertical coordinate system. We then validate our code with idealized test cases that have analytical solutions, including flow over a monochromatic wave with and without viscosity. Finally, we present results from turbulent flows over a moving monochromatic wave with different wave ages, and demonstrate satisfactory agreement of the wave growth rate with results from the literature. We also compare atmospheric stress and wind parameters from two physically equivalent cases. The first specifies a wind moving in the same direction as a propagating wave, while the second involves a stationary wave with the wind adjusted such that the wind relative to the wave is the same as in the first case. Results indicate that the velocity and Reynolds stress profiles for the two cases match, further validating the moving bottom implementation.
17 WIND ENERGY↗
Energy Storage Valuation: A Review of Use Cases and Modeling Tools
An enticing prospect that drives adoption of energy storage systems (ESS) is its ability to be used in a diverse set of use cases and the potential to take advantage of multiple unique value streams. The Energy Storage Grand Challenge (ESGC) technology development pathways for storage technologies draw from a set of use cases in the electrical power system, each with their own specific cost and performance needs. In addition to the need for cost and performance improvements for storage technologies, there a need for robust valuation methods to enable effective policy, investment, business models, and resource planning. There are numerous storage valuation tools available to the public, many of which can analyze the value of an ESS project with inputs and characteristics that reflect a specific storage use case. To effectively reach ESS stakeholders that may be interested in learning about valuation models, this report will draw from publicly available tools developed by the Department of Energy (DOE) and frame their functionalities and capabilities within the context of three distinct use case families. This report examines three of the ESGC use case families in depth and provides a methodology in which interested stakeholders can determine which DOE modeling tool is best suited to value ESS for their specific case. The high-level objectives for this report include: (1) Provide specific sub use-cases for each use case family for further characterization; (2) Provide technical parameters and relevant data for three example use cases that could be used in a valuation tool; (3) Identify a list of publicly available DOE tools that can provide energy storage valuation insights for ESS use case stakeholders; (4) Provide information on the capabilities and different options in each modeling tool; (5) Make conclusions on which are best suited for valuing certain functional/performance requirements and which tools might be applicable to other use cases; and (6) Show the methodology that informs a Model Selection Platform (MSP) framework that educates stakeholders on different DOE models and provides a streamlined way to choose the right model that most closely matches their needs.
25 ENERGY STORAGE↗
Evaluating the 3D PBL scheme for wind energy applications in the complex terrain of Altamont Pass, CA
In this study, the 3D PBL scheme, developed and tested during previous WETO projects, was used to simulate daily speedup flows in the Altamont Pass Wind Resource Area of California. These flows were observed as part of the WFIP2 companion experiment HilFlowS, which examined potential power output from taller turbines in the Altamont region. The regularity of the observed speedup events during the summer months, combined with the importance of terrain-induced wind acceleration, makes them a useful case study for evaluating mesoscale forecast models, which often underperform in regions of complex terrain. Compared to a traditional one-dimensional PBL scheme, the 3D PBL scheme shows evidence of improved wind speed predictions, including an ability to capture atypical wind speed profiles and reduce model overestimates of wind speed across the turbine rotor region. Such improvements will be necessary for wind farm siting and power forecasting, especially as taller turbines are deployed in complex terrain.
17 WIND ENERGY↗
Developing the Proof of Concept for the SERI QC Flag Translation
SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the second of six in the Data Quality and Uncertainty Integration project, develops the proof of concept, including programming in C and a collection of spreadsheet tools, for estimating the operational uncertainty from three-component solar irradiance measurements using information available from SERI QC. The report establishes a field reference for determining the operational uncertainty for each irradiance component based on the SERI QC coupling. Results demonstrate that the operational uncertainty estimates based on global irradiance best represent the collective results for all sky conditions.
14 SOLAR ENERGY↗