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During this thirteen month award, we focused on two main tasks relevant to the development of the CMB-S4 project: (1) The refinement of sensitivity models and use of those to optimize the instrument design, and (2) the final development and testing a cryogenic blackbody calibrator prototype, of which we then produced three copies for CMB-S4's detector testing systems at FNAL, SLAC and UIUC.
The Geothermal Jobs and Economic Development Impact (JEDI) model, developed through the National Renewable Energy Laboratory (NREL), is an Excel-based user-friendly tools that estimates the economic impacts of constructing and operating hydrothermal and Enhanced Geothermal System (EGS) power generation projects at the local level for a range of conventional and renewable energy technologies. The JEDI Model Geothermal User Reference Guide was developed to assist users in using and understanding the model. This guide provides information on the model's underlying methodology, as well as the parameters and references used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features, operation of the model, and a discussion of how the results should be interpreted.
Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.
Execution of a Global Cloud Resolving Model (GCRM) at target resolutions of 2-4 km will generate, at a minimum, 10s of Gigabytes of data per variable per snapshot. Writing this data to disk without creating a serious bottleneck in the execution of the GCRM code while also supporting efficient post-execution data analysis is a significant challenge. This paper discusses an Input/Output (IO) application programmer interface (API) for the GCRM that efficiently moves data from the model to disk while maintaining support for community standard formats, avoiding the creation of very large numbers of files, and supporting efficient analysis. Several aspects of the API will be discussed in detail. First, we discuss the output data layout which linearizes the data in a consistent way that is independent of the number of processors used to run the simulation and provides a convenient format for subsequent analyses of the data. Second, we discuss the flexible API interface that enables modelers to easily add variables to the output stream by specifying where in the GCRM code these variables are located and to flexibly configure the choice of outputs and distribution of data across files. The flexibility of the API is designed to allow model developers to add new data fields to the output as the model develops and new physics is added and also provides a mechanism for allowing users of the GCRM code itself to adjust the output frequency and the number of fields written depending on the needs of individual calculations. Third, we describe the mapping to the NetCDF data model with an emphasis on the grid description. Fourth, we describe our messaging algorithms and IO aggregation strategies that are used to achieve high bandwidth while simultaneously writing concurrently from many processors to shared files. We conclude with initial performance results.
Nonlinear ultrasonic technique is one of promising NDE methods for monitoring defect evolution in irradiated nuclear materials. In this work, we have studied the effect of distributed defects on nonlinear ultrasonic response, in particular, the nonlinear parameter ß from the 2nd harmonic wave. A three-dimensional microstructure-based model has been developed for investigating the dynamic interaction between distributed defects and a propagating longitudinal sound wave. Both linear and nonlinear materials are considered. It is known that the 2nd harmonic wave is generated when a monochromatic wave propagates in a nonlinear elastic material. The results show that second phase particles with elastic heterogeneity and lattice mismatch have strong impact on the nonlinear parameter ß. Lattice mismatches have more effect on the nonlinear parameter no matter they are expansion or contraction. The developed model enables one to explore the contributions of different types of defects to the nonlinear ultrasonic signals and shed light on the development of nonlinear ultrasound nondestructive detection of material defects in nuclear reactors.
This project team analyzed supplemental heat rejection/recovery (SHR) devices or systems that could be used in hybrid ground source heat pump (HGHP) systems located in arid or semi-arid regions in southwestern U.S. Identification of effective SHR solutions would enhance the deployment of ground source heat pumps (GHP) in these regions. In a parallel effort, the team developed integrated GHP models that coupled the building load, heat pump, and ground loop subsystems and which could be applied to residential and commercial office buildings. Then GHP and HGHP performances could be compared in terms of operational performance and life-cycle costs. Several potential SHR devices were analyzed by applying two strategies: 1) to remove heat directly from the water in the ground loop before it enters the ground and 2) to remove heat in the refrigerant loop of the vapor compression cycle (VCC) of the heat pump so less heat is transferred to the water loop at the condenser of the VCC. Cooling towers, adsorption coolers, and thermoelectric liquid coolers were included in strategy 1, and expanded desuperheaters, thermosyphons, and an optimized VCC were included in strategy 2. Of all SHR devices analyzed, only the cooling tower provided a cost-effective performance enhancement. For the integrated GHP model, the project team selected the building load model HAMBASE and its powerful computational Simulink/MatLab platform, empirical performance map models of the heat pumps based upon manufacturers’ performance data, and a ground loop model developed by Oklahoma State University and rewritten for this project in Simulink/MatLab. The design process used GLHEPRO, also from Oklahoma State University, to size the borehole fields. The building load and ground loop models were compared with simulations from eQuest, ASHRAE 140-2008 standards, EnergyPlus, and GLHEPRO and were found to predict those subsystems’ performance well. The integrated GHP model was applied to a 195m 2 (2100ft 2 ) residential building and a 4,982m 2 (53,628ft 2 ) three-story commercial office building, and it ran 10-15 year simulations. The integrated GHP model and its Simulink platform provided residential data, ranging from seconds to years, and commercial office building data, ranging from minutes to years. A cooling tower model was coupled to the base case integrated GHP model for the residential building and the resulting HGHP system provided a cost-effective solution for the Austin, TX location. Simulations for both the residential and commercial building models were run with varying degrees of SHR (device/system not identified) and the results were found to significantly decrease installation costs, increase heat pump efficiency (lower entering water temperature), and prolong the lifetime of the borehole field. Lifetime cycle costs were estimated from the simulation results. Sensitivity studies on system operating performance and lifetime costs were performed on design parameters, such as construction materials, borehole length, borehole configuration and spacing, grout conductivity, and effects of SHR. While some of the results are intuitive, these studies provided quantitative estimates of improved performance and cost. One of the most important results of this sensitivity study is that overall system performance is very sensitive to these design parameters and that modeling and simulation are essential tools to design cost-effective systems.
Evaluating a climate model’s fidelity (ability to simulate observed climate) is a critical step in establishing confidence in the model’s suitability for future climate projections, and in tuning climate model parameters. Model developers use their judgement in determining which trade-offs between different aspects of model fidelity are acceptable. However, little is known about the degree of consensus in these evaluations, and whether experts use the same criteria when different scientific objectives are defined. Here, we report on results from a broad community survey studying expert assessments of the relative importance of different output variables when evaluating a global atmospheric model’s mean climate. We find that experts adjust their ratings of variable importance in response to the scientific objective, for instance, scientists rate surface wind stress as significantly more important for Southern Ocean climate than for the water cycle in the Asian watershed. There is greater consensus on the importance of certain variables (e.g., shortwave cloud forcing) than others (e.g., aerosol optical depth). We find few differences in expert consensus between respondents with greater or less climate modeling experience, and no statistically significant differences between the responses of climate model developers and users. In conclusion, the concise variable lists and community ratings reported here provide baseline descriptive data on current expert understanding of certain aspects of model evaluation, and can serve as a starting point for further investigation, as well as developing more sophisticated evaluation and scoring criteria with respect to specific scientific objectives.
Most material models used in numerical simulations are based on heuristics and empirically calibrated to experimental data. For a specific model, key questions are determining its domain of applicability and assessing its relative merits compared to other models. Answering these questions should be a part of model verification and validation (V and V). Here, we focus on V and V of high explosive models. Typically, model developers implemented their model in their own hydro code and use different sets of experiments to calibrate model parameters. Rarely can one find in the literature simulation results for different models of the same experiment. Consequently, it is difficult to assess objectively the relative merits of different models. This situation results in part from the fact that experimental data is scattered through the literature (articles in journals and conference proceedings) and that the printed literature does not allow the reader to obtain data from a figure in electronic form needed to make detailed comparisons among experiments and simulations. In addition, it is very time consuming to set up and run simulations to compare different models over sufficiently many experiments to cover the range of phenomena of interest. The first difficulty could be overcome if the research community were to support an online web based database. The second difficulty can be greatly reduced by automating procedures to set up and run simulations of similar types of experiments. Moreover, automated testing would be greatly facilitated if the data files obtained from a database were in a standard format that contained key experimental parameters as meta-data in a header to the data file. To illustrate our approach to V and V, we have developed a high explosive database (HED) at LANL. It now contains a large number of shock initiation experiments. Utilizing the header information in a data file from HED, we have written scripts to generate an input file for a hydro code, run a simulation, and generate a comparison plot showing simulated and experimental velocity gauge data. These scripts are then applied to several series of experiments and to several HE burn models. The same systematic approach is applicable to other types of material models; for example, equations of state models and material strength models.
Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.
This paper presents a regional climate system model RCM-CLM-CN-DV and its validation over Tropical Africa. The model development involves the initial coupling between the ICTP regional climate model RegCM4.3.4 (RCM) and the Community Land Model version 4 (CLM4) including models of carbon-nitrogen dynamics (CN) and vegetation dynamics (DV), and further improvements of the models. Model improvements derive from the new parameterization from CLM4.5 that addresses the well documented overestimation of gross primary production (GPP), a refinement of stress deciduous phenology scheme in CN that addresses a spurious LAI fluctuation for drought-deciduous plants, and the incorporation of a survival rule into the DV model to prevent tropical broadleaf evergreens trees from growing in areas with a prolonged drought season. The impact of the modifications on model results is documented based on numerical experiments using various subcomponents of the model. The performance of the coupled model is then validated against observational data based on three configurations with increasing capacity: RCM-CLM with prescribed leaf area index and fractional coverage of different plant functional types (PFTs); RCM-CLM-CN with prescribed PFTs coverage but prognostic plant phenology; RCM-CLM-CN-DV in which both the plant phenology and PFTs coverage are simulated by the model. Results from these three models are compared against the FLUXNET up-scaled GPP and ET data, LAI and PFT coverages from remote sensing data including MODIS and GIMMS, University of Delaware precipitation and temperature data, and surface radiation data from MVIRI and SRB. Our results indicate that the models perform well in reproducing the physical climate and surface radiative budgets in the domain of interest. However, PFTs coverage is significantly underestimated by the model over arid and semi-arid regions of Tropical Africa, caused by an underestimation of LAI in these regions by the CN model that gets exacerbated through vegetation dynamics in RCM-CLM-CN-DV.
A physics-based model is developed to predict multiaxial creep behavior in Alloy 709 (A709), an advanced austenitic stainless steel intended for high-temperature applications such as Sodium Fast Reactors (SFRs). Compared to conventional stainless steels like 316H, A709 offers superior high-temperature performance; however, comprehensive data on its multiaxial creep response remain limited. To address this gap, a crystal plasticity finite element (CPFE) framework is used to simulate the deformation and failure mechanisms of A709 under multiaxial loading conditions. The model incorporates an extended Hu-Cocks dislocation creep formulation that accounts for precipitation effects, along with the Sham–Needleman model to capture grain boundary cavitation-driven failure. These advanced constitutive models enable a detailed understanding of the interplay between microstructural evolution and macroscopic creep response. Furthermore, the study evaluates the predictive accuracy of various effective stress measures in estimating creep rupture life, leveraging simulated multiaxial creep data. The findings provide critical insights into the applicability of different stress measures for engineering design and life prediction of A709 components operating under complex loading conditions. This work contributes to improving the reliability of high-temperature structural components by advancing predictive modeling capabilities for advanced austenitic steels.
Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to understand the impact of corn stover moisture content and grinder speed on grind physical properties; and (2) develop response surface models and optimize these models using a hybrid genetic algorithm. The response surface models developed were used to draw surface plots to understand the interaction effects of the corn stover grind moisture content and grinder speed on the grind physical properties and specific energy consumption. The surface plots indicated that a higher corn stover grind moisture content and grinder speed had a positive effect on the bulk and tapped density. The final grind moisture content was highly influenced by the initial moisture content of the corn stover grind. Optimization of the response surface models using the hybrid genetic algorithm indicated that moisture content in the range of 17 to 19% (w.b.) and a grinder speed of 47 to 49 Hz maximized the bulk and tapped density and minimized the geomantic mean particle length. The specific energy consumption was minimized when the grinder speed was about 20 Hz and the corn stover grind moisture content was about 10% (w.b.).
The International Atomic Energy Agency (IAEA) launched the Modelling and Data for Radiological Impact Assessments (MODARIA) II Programme in October. As part of the IAEA effort to enhance the capabilities of its Member States in assessing radiological impacts of radionuclides released to the environment from sites that were not operated in accordance with current standards, and to ensure protection of the public and the environment, the MODARIA II Programme aims to: (1) test the performance of models developed for predicting the transfer of radionuclides in the environment and assessing radiological impacts to the public and the environment; (2) develop and improve models for particular environments and, where appropriate, to agree on data sets that are generally applicable for use in testing environmental transfer models; and (3) provide an international forum for the exchange of experiences, ideas and research information. The Programme will run for 3 years from 2016 to 2019. The Programme comprises seven working groups covering a wide range of topics (https://wwwns.iaea.org/projects/modaria/modaria2.asp?s=8 and l=129). MODARIA II Working Group 1 (WG1) is focused on dose assessment and decision making regarding existing exposure situations due to naturally occurring radioactive material (NORM) and nuclear legacy sites. WG1 was formed as a combination of two MODARIA I WGs: WG1 focused on application of decision-aiding tools in remediation; and WG3 focused on risk assessment for NORM and radioactively contaminated legacy sites (https://wwwns.iaea.org/projects/modaria/default.asp?s=8 and l=129). It was recognized that the decision-making needs will inform the scope of risk assessments, and the risk insights and structured decision analyses will aid in informed decision-making. Therefore, MODARIA II WG1 is focused on developing methods and toolsets for risk-informed decision-making for NORM and nuclear legacy sites, including demonstration of decision-making processes and tools through case studies. MODARIA II WG1 has 91 registered participants from 33 Member States and IAEA (as of November 2017), some 30 of whom are actively engaged in various collaborative research and development activities within WG1. Members of WG1 participated in the First Technical Meeting of MODARIA II, which was held in Vienna, Austria from 31 October to 4 November 2016. The group then held its First Interim Meeting in Brussels, Belgium from 26 to 30 June 2017, which included a field trip to the legacy Tessenderlo Chemie (TCH) Phosphate Processing Site. At this meeting, the TCH Site and the Pridneprovsky Chemical Plant (PChP) in Ukraine were selected for case studies. The Los Alamos National Laboratory (LANL) Material Disposal Area B in the USA and the Beaverlodge Uranium Mill and Mill Site in northern Canada were selected as examples for sharing of good practices and lessons learned in risk-informed decision-making for environmental remediation and land conveyance. At the Second Technical Meeting held in Vienna on 30 October to 3 November 2017, an additional NORM site for tin processing in Brazil was added to the case studies, with another NORM site in Yunnan, China included for consideration at the Second Interim Meeting of WG1. The Second Interim Meeting will be held in in early to mid-May, 2018. The group has also decided to prepare an Interim Report by October 2018 to document the WG1 discussions and analyses for the period of 2016-2018, which will be finalized at the conclusion of MODARIA II in October 2019. Initial assignments have been discussed for IM1 participants to collect data, conduct modelling and analyses, and prepare draft sections for the Interim Report. This paper will provide an overview of the work plans for WG1. It will also highlight some near-term activities that the group is currently working on. (authors)
The DC-DC Modular Multilevel Converter (MMC), which originated from the AC-DC MMC, is an attractive converter topology for interconnection of medium-/high-voltage DC grids. This paper presents design considerations for the DC-DC MMC to achieve high efficiency and reduced component sizes. A steady-state mathematical model of the DC-DC MMC in the phasor-domain is developed. Based on the developed model, a design approach is proposed to size the components and to select the operating frequency of the converter to satisfy a set of design constraints while achieving high efficiency. The design approach includes sizing of the arm inductor, Sub-Module (SM) capacitor, and phase filtering inductor along with the selection of AC operating frequency of the converter. The accuracy of the developed model and the effectiveness of the design approach are validated based on the simulation studies in the PSCAD/EMTDC software environment. The analysis and developments of this paper can be used as a guideline for design of the DC-DC MMC.
Each step in the scientific process generates contextual information about the data that is important to consider when performing data integration, developing models of biological process, or training AI models. We will develop a flexible, template-driven tool that will log biological samples, capture metadata about those samples, and track the type(s) of analysis being performed by researchers providing samples for analysis by mass spectrometry.
Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring. We have deployed strainmeters and then conducted injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters (30 to 40m depth) was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 n/d over a few days. The signal at two strainmeters at shallow depth were consistent, although the magnitude of the horizontal strains were different reflecting the different radial directions from the well. The signal at a deep strainmeter deployed at reservoir depth was much different, with tensile vertical strains and compressive horizontal strains. These data can be interpreted by inverting poroelastic forward models developed using numerical and analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. This model is fast and can be inverted to estimate reservoir stiffness and geometry. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. The proxy model is periodically updated and refined using the finite element model to ensure accuracy. This approach significantly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. We have shown that the strain tensor in the caprock is sensitive to pressure in the reservoir, boundaries in the reservoir, and pressure in the caprock caused by leaks. These results indicate that coupling strain tensor data with inversion has the potential to help evaluate reservoirs during initial characterization, and to monitor them during the CO2 injection and storage process.
Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to its negative effect on chemical durability. Developing models to accurately predict nepheline precipitation from compositions is important to increase waste loading since existing models can be overly conservative. In this study, an expanded dataset containing 955 glasses was compiled from literature data, where 355 glasses are for high-level waste (HLW). Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. Nine machine learning (ML) algorithms (e.g., k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability of predicting nepheline precipitation from compositions. Model accuracy, precision, recall/sensitivity, and F1 score were systemically compared between different ML algorithms and modeling protocols. Good model prediction with an accuracy ~0.9 (misclassification rate of ~10%) was observed with different algorithms under certain protocol. This study evaluated various ML models to predict nepheline precipitations in waste glasses, highlighting the importance of data preparation, modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitations.
It remains a challenge for climate models to correctly capture the relationship between precipitation and ENSO. This studyexamines the linkage between the simulated precipitation climatology and ENSO-related precipitation anomaly during borealwinter based on the multi-model ensemble from the Atmospheric Model Intercomparison Project Phase 5 (AMIP5) and perturbedparameter ensemble (PPE) with the Beijing Climate Center (BCC) atmospheric model. The AMIP5 models have large biases insimulating the tropical precipitation anomaly during El Niño, such as the shifts of the Inter-Tropical Convergence Zone (ITCZ)and South Pacific Convergence Zone (SPCZ). The inter-model differences show that the precipitation change in response to seasurface temperature (SST) change increases with enhanced precipitation climatology. The ENSO-related precipitation anomalycan also be related to the spatial distribution of the mean-state precipitation. The simulated ITCZ/SPCZ displacements aresignificantly correlated with the spatial precipitation-SST relationship in the mean state. Models with stronger mean-stateprecipitation-SST relationship also produce stronger SPCZ/ITCZ displacements. The mean-state tropical precipitation also hasstrong impacts on the ENSO-related precipitation anomalies over East Asia. In the BCC PPE, the connection between the mean-state precipitation and ENSO-related precipitation anomaly is overall consistent with that in AMIP5. Parameters associated withthe low-cloud and deep convection processes are the most influential ones for the precipitation simulations in BCC. Comparedwith the version in AMIP5, the new BCC model can better simulate the precipitation climatology and the relationship betweenENSO and precipitation over southern China. These results have important implications for model development and model-basedclimate predictions.