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Earth system models are complex integrated models of atmosphere, ocean, sea ice, and land surface. Coupling the components can be a significant challenge due to the difference in physics, temporal, and spatial scales. Further, this study explores multirate partitioned Runge-Kutta methods for the fluid-fluid interaction problem and demonstrates its parallel performance by using the PETSc library. We consider compressible Navier-Stokes equations with gravity coupled through a rigid-lid interface. Our large-scale numerical experiments reveal that multirate partitioned Runge-Kutta coupling schemes (1) can conserve total mass; (2) have second-order accuracy in time; and (3) provide favorable strong- and weak-scaling performance on modern computing architectures. We also show that the speedup factors of multirate partitioned Runge-Kutta methods match theoretical expectations over their base (single-rate) method.
The boundary conditions for thermal modeling of fenestration systems assume an adiabatic condition between the fenestration system installed and the opaque envelope system. This theoretical adiabatic boundary condition may not be appropriate owing to heat transfer at the interfaces, particularly for aluminum- framed windows affixed to metal- framed walls. In such scenarios, the heat transfer at the interface may increase the discrepancy between real world thermal indices and laboratory measured or calculated indices based on NFRC Rating System.This paper discusses the development of window-wall Interface Correction Factors (ICF) to improve energy impacts of building envelope systems
In this paper we explore the trend in net biome productivity (NBP) over India for the period 1980–2012 and quantify the impact of different environmental factors, including atmospheric CO 2 concentrations ([CO 2 ]), land use and land cover change, climate, and nitrogen deposition on carbon fluxes using a land surface model, Integrated Science Assessment Model. Results show that terrestrial ecosystems of India have been a carbon sink for this period. Driven by a strong CO 2 fertilization effect, magnitude of NBP increased from 27.17 TgC/yr in the 1980s to 34.39 TgC/yr in the 1990s but decreased to 23.70 TgC/yr in the 2000s due to change in climate. Adoption of forest conservation, management, and reforestation policies in the past decade has promoted carbon sequestration in the ecosystems, but this effect has been offset by loss of carbon from ecosystems due to rising temperatures and decrease in precipitation.
Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.
This report summarizes work over the course of a three-year project (2012-2015, with one year no-cost extension to 2016). The full proposal detailed six tasks: Task 1: Population projection model Task 2: Household model Task 3: Spatial population model Task 4: Integrated model development Task 5: Population projections for Shared Socio-economic Pathways (SSPs) Task 6: Population exposure to climate extremes We report on all six tasks, provide details on papers that have appeared or been submitted as a result of this project, and list selected key presentations that have been made within the university community and at professional meetings.
We demonstrate and approximate the effects of bromide ions on hydrogen peroxide generation under conditions of low oxygen concentration and over pressure of hydrogen. As has been shown these are conditions for which hydrogen peroxide generation is strongly reduced. We find that this reduction is strongly canceled by the presence of even small concentrations of bromide. The full Radiolysis Models seems to be required to accurately predict the effect, but we offer an empirical conservative model for use in the ANL Mixed Potential Model applicable to the prediction of UO 2 degradation.
In this project, we developed a techno-economic analysis (TEA) model that can be used to evaluate the viability of a proposed Geological Thermal Energy Storage (GeoTES) design. This MATLAB-based model integrates distinct subsystem models for the reservoir, wells, power cycle, and solar field to capture their distinct characteristics. It applies this approach in simulating GeoTES storage and dispatch operations for durations ranging from hourly to seasonal. Using cases studies based on GeoTES designs provided by industry partners - Premier Resource Management (PRM) and EarthBridge Energy - we validated the TEA model estimations of system performance and costs (such as thermal and electrical power/energy inflow and outflow, capital costs, and levelized costs of energy and storage) for both concentrating solar thermal (CST) and Carnot Battery (CB) pairings with GeoTES (CST-GeoTES and CB-GeoTES). For the CST-GeoTES case, the model was validated against the proposed system designed by PRM. It showed good agreement with PRM's estimations when well and pump costs derived from PRM's estimations were used. When GETEM-based costs were used, there was a slight overprediction due to GETEM's project/site agnostic assumption of these costs. From a sensitivity analysis perspective, the levelized cost of electricity (LCOE) of the CST-GeoTES case was most sensitive to well flow rate and the charging temperature. An optimal design scenario resulted in an LCOE of 0.11 $\$$/kWhe. CST-GeoTES can also provide a source of heat to meet seasonal demands. With 12-hour and 24-hour levelized cost of heat (LCOH) of 0.018 $\$$/kWhth and 0.022 $\$$/kWhth, respectively, CST-GeoTES could be competitive in the California market with an average industrial price of natural gas in California between 0.041-0.047 $\$$/kWhth. The levelized cost of storage (LCOS) for CST-GeoTES depends on the energy storage duration. Although the LCOS is relatively higher for shorter durations (e.g., ~0.50 $\$$/kWhe for 1 hour of storage), it is an order of magnitude lower (0.06 $\$$/kWhe) for longer storage durations and competitive with lithium-ion batteries (beyond 12 hours of storage) and molten-salt thermal energy storage (beyond 32 hours). Energy. Three options were explored and applied to the EarthBridge case study: (1) A Carnot Battery design using R125 working fluid with both hot and cold storage; (2) A Carnot Battery design using R125 working fluid with only hot storage; (3) A Carnot Battery using a commercially available heat pump with carbon dioxide (CO2) working fluid and hot storage only. The CB-GeoTES with cold storage only had a slight (round-trip) efficiency advantage over the system without (43.4% vs. 42.8%). This is because the cold storage is limited by the freezing point of water, so the cold storage is not much colder than the environment. The system using commercially available technologies was the least efficient - partly because different cycles were used in the heat pump (CO2) and heat engine (binary cycle) which leads to some inefficiencies. Using the commercially available design, the levelized cost of energy (LCOS) from the model (0.10 $\$$/kWhe) was higher than that estimated by EarthBridge (0.068 $\$$/kWhe). This is because of the low round-trip (38.7%) efficiency of the commercially available design. Sensitivity analysis reveals that the model is most sensitive to electricity price. Including electricity price in the TEA for CB-GeoTES leads to an increase in LCOS from the base value to 0.25 $\$$/kWhe. To determine storage sites suitable for GeoTES, we gathered and analyzed geological, petrophysical, and geophysical data of oil and gas reservoir and aquifers in California and Texas. We down-selected possible sites based on cut-off values for site characteristics (e.g., reservoir temperature, formation thickness, permeability, porosity, depth, and brine salinity) and preliminary costs. Using this approach, the Carrizo-Wilcox, Yegua-Jackson, and Dockum brackish aquifers in Texas were identified as having the highest suitability. Similarly, in the central California region, the White Wolf, Belridge South Tulare, and Belridge South Reef Ridge were the most suitable. Going further, we assessed the storage potential in the selected sites. To do this we developed distributions of reservoir characteristic data and applied a Monte Carlo-based analysis to account for intrinsic uncertainty in the acquired data. The analysis revealed that the Carrizo-Wilcox aquifer had the highest storage potential with a mean capacity of 554 TWhth (i.e., 63 TWhe). The estimated capacity serves as an upper limit of storage potential given that not all fields in the basin will be developed. We participated in multiple outreach activities including conference presentations, panel session discussions, and the facilitation of a GeoTES workshop at the NREL Golden campus.
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The Laser Interferometer Space Antenna (LISA) mission, a space based gravitational wave detector, uses laser metrology to measure distance fluctuations between proof masses aboard three spacecraft. The total acceleration disturbance to each proof mass is required to be below 3 x 10(exp 15) m/sq square root of Hz. Self-gravity noise due to spacecraft distortion and spacecraft motion is expected to be a significant contributor to the acceleration noise budget. To minimize these effects, the gravitational field at each proof mass must be kept as small, flat, and constant as possible. It is estimated that the static field must be kept below 5 x 10(exp -10) m/sq s with a gradient below 3 x 10(exp -8)/sq s in order to meet the required noise levels. Most likely it will not be possible to directly verify by measurements that the LISA spacecraft meets these requirements; they must be verified by models. The LISA Integrated Modeling team developed a new self-gravity tool that calculates the gravitational forces and moments on the proof masses to aid in the design and verification of the LISA spacecraft. We present here an overview of&e tool and the latest self-gravity results calculated using the current baseline design of LISA. We also present results of a self-gravity analysis of the ST-7 DRS package that will fly on the LISA Pathfinder mission.
Geospatial Catalogue Web Service is a vital service for sharing and interoperating volumes of distributed heterogeneous geospatial resources, such as data, services, applications, and their replicas over the web. Based on the Grid technology and the Open Geospatial Consortium (0GC) s Catalogue Service - Web Information Model, this paper proposes a new information model for Geospatial Catalogue Web Service, named as GCWS which can securely provides Grid-based publishing, managing and querying geospatial data and services, and the transparent access to the replica data and related services under the Grid environment. This information model integrates the information model of the Grid Replica Location Service (RLS)/Monitoring & Discovery Service (MDS) with the information model of OGC Catalogue Service (CSW), and refers to the geospatial data metadata standards from IS0 19115, FGDC and NASA EOS Core System and service metadata standards from IS0 191 19 to extend itself for expressing geospatial resources. Using GCWS, any valid geospatial user, who belongs to an authorized Virtual Organization (VO), can securely publish and manage geospatial resources, especially query on-demand data in the virtual community and get back it through the data-related services which provide functions such as subsetting, reformatting, reprojection etc. This work facilitates the geospatial resources sharing and interoperating under the Grid environment, and implements geospatial resources Grid enabled and Grid technologies geospatial enabled. It 2!so makes researcher to focus on science, 2nd not cn issues with computing ability, data locztic~, processir,g and management. GCWS also is a key component for workflow-based virtual geospatial data producing.
The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.
The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes
Lawrence Livermore National Laboratory (LLNL) is proposing a new Laser Driven Fusion Integration Research and Science Test Facility (LD-FIRST) with the goal of providing an experimental testbed for future Inertial Fusion Energy (IFE) systems. However, IFE systems require detailed and accurate multiphysics modeling to quantify material damage, thermal loading, and tritium breeding within complex chamber environments. This article presents the first step in an integrated multiphysics framework that couples meshed CAD-based geometry within Monte Carlo neutronic simulations to enable high-fidelity analysis of IFE chamber concepts, with future coupling to external codes. The neutronics workflow utilizes OpenMC and its third-party capability to use CAD-based geometries through DAGMC and tally on unstructured meshes with Libmesh to evaluate neutron transport behavior, geometric fidelity, and material performance under reactor-relevant conditions. The use of tailored tallies on unstructured meshes in this framework allows direct transfer without interpolating to CFD simulation tools. Two IFE chambers were evaluated, both conceived by LLNL: HYLIFE-II and Laser IFE (LIFE). This work produced high-fidelity conformal surface and volumetric meshes of the HYLIFE-II and LIFE chambers with mapped spatial insight into material damage, thermal loading, and tritium breeding. The HYLIFE-II model was built utilizing available resources and used as a test case to verify that the neutronics framework can handle complex geometries. The LIFE chamber CAD was provided by LLNL and was the main focus of this work. This work analyzes multiple ternary alloy breeding materials for the LIFE chamber, across different 6 Li enrichments to produce data relevant to the LD-FIRST project. This work also investigates the level of model fidelity for the LIFE chamber, and results show that inclusion of detailed first wall and coolant structures increased the predicted tritium breeding ratio (TBR) by ~30%, highlighting the sensitivity of tritium breeding and the need for a high-fidelity simulation framework for IFE chambers. These developments provide a scalable toolset for the design and optimization of next-generation IFE chambers, forming a solid foundation for future coupled multiphysics analysis.
This project is developing a multiscale, multi-physics modeling framework for probing the effects of materials microstructure and device architecture on ion transport within 3D ceramic solid-state battery materials, with the goal of enhancing performance and reliability. The project has three primary objectives: (1) integrate multi physics and multiscale model components; (2) understand interface- and microstructure-derived limitations on ion transport; and (3) derive key structure-performance relations for enabling future optimization.
Forests are becoming severely fragmented as a result of land development. South Korea has responded to changing community concerns about environmental issues. The nation has developed and is extending a broad range of tools for use in environmental management. Although legally mandated environmental compliance requirements in South Korea have been implemented to predict and evaluate the impacts of land-development projects, these legal instruments are often insufficient to assess the subsequent impact of development on the surrounding forests. It is especially difficult to examine impacts on multiple (e.g., regional and local) scales in detail. Forest configuration and size, including forest fragmentation by land development, are considered on a regional scale. Moreover, forest structure and composition, including biodiversity, are considered on a local scale in the Environmental Impact Assessment process. Recently, the government amended the Environmental Impact Assessment Act, including the SEA, EIA, and small-scale EIA, to require an integrated approach. Therefore, the purpose of this study was to establish an impact assessment system that minimizes the impacts of land development using an approach that is integrated across multiple scales. This study focused on forest fragmentation due to residential development and road construction sites in selected Congestion Restraint Zones (CRZs) in the Greater Seoul Area of South Korea. Based on a review of multiple-scale impacts, this paper integrates models that assess the impacts of land development on forest ecosystems. The applicability of the integrated model for assessing impacts on forest ecosystems through the SEIA process is considered. On a regional scale, it is possible to evaluate the location and size of a land-development project by considering aspects of forest fragmentation, such as the stability of the forest structure and the degree of fragmentation. On a local scale, land-development projects should consider the distances at which impacts occur in the vicinity of the forest ecosystem, and these considerations should include the impacts on forest vegetation and bird species. Impacts can be mitigated by considering the distances at which these influences occur. In particular, this paper presents an integrated environmental impact assessment system to be applied in the SEIA process. The integrated assessment system permits the assessment of the cumulative impacts of land development on multiple scales. -- Highlights: • The model is to assess the impact of forest fragmentation across multiple scales. • The paper suggests the type of forest fragmentation on a regional scale. • The type can be used to evaluate the location and size of a land development. • The paper shows the influence distance of land development on a local scale. • The distance can be used to mitigate the impact at an EIA process.
Simulating complex astrophysical reacting flows is computationally expensive—reactions are stiff and typically require implicit integration methods. The reaction update is often the most expensive part of a simulation, which motivates the exploration of more economical methods. In this research note, we investigate how the explicit Runge–Kutta–Chebyshev (RKC) method performs compared to an implicit method when applied to astrophysical reactive flows. These integrators are applied to simulations of X-ray bursts arising from unstable thermonuclear burning of accreted fuel on the surface of neutron stars. We show that the RKC method performs with similar accuracy to our traditional implicit integrator, but is more computationally efficient when run on CPUs.
The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often needed and is performed manually and on an as-needed basis. The integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data model contains objects that make up a given domain along with their attributes and relationships and could be used as a standard for exchange between applications for simple integration and data aggregation. After a survey of existing models, it was determined that a new model was required to meet the scope of this problem. DIAMOND has been developed in the form of an ontology to meet these needs. An ontology is simply a data model that also acts as a taxonomy for the domain, organizing the objects in a hierarchy with the most general objects at the top.