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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 181 records · Page 10

Linear and non-linear perturbations in dark energy models

In this work we discuss observational aspects of three time-dependent parameterisations of the dark energy equation of state w ( z ). In order to determine the dynamics associated with these models, we calculate their background evolution and perturbations in a scalar field representation. After performing a complete treatment of linear perturbations, we also show that the non-linear contribution of the selected w ( z ) parameterisations to the matter power spectra is almost the same for all scales, with no significant difference from the predictions of the standard ΛCDM model.

79 ASTRONOMY AND ASTROPHYSICS↗

Modeling energy flow and nutrient cycling in natural semiarid grassland ecosystems with the aid of thematic mapper data

Energy flow and nutrient cycling were modeled as affected by herbivory on selected intensive sites along gradients of precipitation and soils, validating the model output by monitoring selected parameters with data derived from the Thematic Mapper (TM). Herbivore production was modeled along the gradient of soils and herbivory, and validated with data derived from TM in a spatial data base.

Lewis, James K.↗

Wind Energy Modeling and Simulation, Volume 1: Atmosphere and Plant

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 1 covers the computing challenges in full turbine modelling, then discusses bridging scales in the atmosphere and turbulence modelling, wind forecasting, wind plant flow, and plant level controller design.

17 WIND ENERGY↗

A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock

Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Constraints on interacting dark energy models from Planck 2015 and redshift-space distortion data

We investigate phenomenological interactions between dark matter and dark energy and constrain these models by employing the most recent cosmological data including the cosmic microwave background radiation anisotropies from Planck 2015, Type Ia supernovae, baryon acoustic oscillations, the Hubble constant and redshift-space distortions. We find that the interaction in the dark sector parameterized as an energy transfer from dark matter to dark energy is strongly suppressed by the whole updated cosmological data. On the other hand, an interaction between dark sectors with the energy flow from dark energy to dark matter is proved in better agreement with the available cosmological observations. This coupling between dark sectors is needed to alleviate the coincidence problem.

79 ASTRONOMY AND ASTROPHYSICS↗

From Safer Space Travel to Improved Fisheries Monitoring: Wave Energy Modeling Tool Helps Design Tomorrow's Tech

What does wave energy have to do with space exploration? It turns out, plenty. The National Aeronautics and Space Administration (NASA) is working toward launching the Artemis I mission, an uncrewed lunar test flight, in 2021. It will be the maiden flight of the Space Launch System heavy-lift launch vehicle, as well as the Orion crew module (CM). Orion will orbit the moon for multiple days before returning to Earth and landing in the Pacific Ocean.

Tannen S Vanzwieten↗

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗

A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock: Preprint

Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.

building stock↗

Machine learning assisted derivation of minimal low-energy models for metallic magnets

Abstract We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.

Sharma, Vikram (ORCID:0000000189105519)↗

Non-ergodic extended phase of the Quantum Random Energy model

The concept of non-ergodicity in quantum many body systems can be discussed in the context of the wave functions of the many body system or as a property of the dynamical observables, such as time-dependent spin correlators. In the former approach the non-ergodic delocalized state is defined as the one in which the wave functions occupy a volume that scales as a non-trivial power of the full phase space. In this work we study the simplest spin glass model and find that in the delocalized non-ergodic regime the spin–spin correlators decay with the characteristic time that scales as non-trivial power of the full Hilbert space volume. The long time limit of this correlator also scales as a power of the full Hilbert space volume. We identify this phase with the glass phase whilst the many body localized phase corresponds to a ’hyperglass’ in which dynamics is practically absent. We discuss the implications of these findings to quantum information problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint

Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.

buildings↗

Radiation dose rate distributions of spent fuel dry casks estimated with MAVRIC based on detailed geometry and continuous-energy models

This study presents a detailed comparison of the dose rate distributions of a dry TN–32 fuel cask with two geometry models and two cross-sectional datasets. The accuracies of radiation dose rate estimation and computational efficiencies of each geometry model with two cross-sectional data-sets are compared. The use of automated variance reduction techniques can significantly improve the computational efficiency of such a realistic, deep penetration problem that involves radiation transport from different volumetric sources, thereby eliciting only a small statistical error. Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) is a computational sequence within the SCALE 6.2 code package based on consistent adjoint driven importance sampling (CADIS), a type of automated variance reduction technique. Homogenous and full fuel assembly models are built herein, and two nuclear cross-section libraries (V7–200N47G and continuous energy) are applied in this work. Based on the detailed comparisons, we found that neutron dose rate estimation is more dependent on geometry modeling than on cross-section data. For neutron-induced gamma rays, the dose rate distribution depends on both the spatial self-shielding effect and the cross-section library. The primary gamma rays respectively contribute to the total dose rate by ~91% and ~ 99% on side and top surfaces, and the dose rate accuracy is more dependent on the cross-section library than on geometry modeling. In terms of the computation efficiency and efforts spent on geometry modeling, the homogenous fuel assembly model with the MG library can produce an acceptable dose rate distribution, but the detailed fuel assembly model with the continuous-energy library is required for more precise dose rate estimation.

61 RADIATION PROTECTION AND DOSIMETRY↗

HPC Node Performance and Energy Modeling with the Co-Location of Applications

Multicore processors have become an integral part of modern large-scale and high-performance parallel and distributed computing systems. This advance became necessary as the demand for increased system performance has exceeded the limits that single core processors can provide. Unfortunately, applications co-located on multicore processors can suffer from decreased performance and increased dynamic energy use as a result of access to shared resources, such as memory. Consequently, it is increasingly important to characterize the performance of applications that execute on these architectures. This work investigates some of the disadvantages of co-location, and presents a methodology for building models capable of utilizing varying amounts of information about a target application and its co-located applications to make predictions about the target application’s execution time and the system’s energy use under arbitrary co-locations of a wide range of application types. The proposed methodology is validated on three different server class Intel Xeon multicore processors using eleven applications from two scientific benchmark suites. The model’s utility for scheduling is also demonstrated in a simulated large-scale high-performance computing environment through the creation of a co-location aware scheduling heuristic. This heuristic demonstrates that scheduling using information generated with the proposed modeling methodology is capable of making significant improvements over a scheduling heuristic that is naive to co-location interference.

performance modeling, energy modeling, resource ma↗

Status Update: Modeling Energy Balance in NIF Hohlraums

We have developed a standardized methodology to model hohlraum drive in NIF experiments. We compare simulation results to experiments by 1) comparing hohlraum xray fluxes and 2) comparing capsule metrics, such as bang times. Long-pulse, high gas-fill hohlraums require a 20-28% reduction in simulated drive and inclusion of ~15% backscatter to match experiment through (1) and (2). Short-pulse, low fill or near-vacuum hohlraums require a 10% reduction in simulated drive to match experiment through (2); no reduction through (1). Ongoing work focuses on physical model modifications to improve these matches.

42 ENGINEERING↗

Optimal design of a model energy conversion device

Fuel cells, batteries, and thermochemical and other energy conversion devices involve the transport of a number of (electro-) chemical species through distinct materials so that they can meet and react at specified multi-material interfaces. Therefore, morphology or arrangement of these different materials can be critical in the performance of an energy conversion device. In this paper, we study a model problem motivated by a solar-driven thermochemical conversion device that splits water into hydrogen and oxygen. We formulate the problem as a system of coupled multi-material reaction-diffusion equations where each species diffuses selectively through a given material and where the reaction occurs at multi-material interfaces. In conclusion, we introduce a phase-field formulation of the optimal design problem and numerically study selected examples.

30 DIRECT ENERGY CONVERSION↗