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Adapting the CROPGRO model to simulate growth and production of Brassica carinata, a bio-fuel crop

GLOBAL CHANGE BIOLOGY BIOENERGY 2021年第7期13卷 1134-1148页

作者： Boote, Kenneth J. Seepaul, Ramdeo Mulvaney, Michael J. Hagan, Austin K. Bashyal, Mahesh George, Sheeja Small, Ian Wright, David L. Univ Florida Dept Agr & Biol Engn 1741 Museum Rd Gainesville FL 32611 USA Univ Florida North Florida Res & Educ Ctr Quincy FL USA Univ Florida West Florida Res & Educ Ctr Jay FL USA Auburn Univ Auburn AL 36849 USA

Carinata (Brassica carinata) is an oilseed crop which, because of its non-edible oil composition and favorable fatty acid profile, is proposed as a "green" sustainable aviation fuel. It can be grown as a winter crop in the southeastern USA or as a summer annual crop in northern latitudes. No crop models exist for carinata because it is a relatively new crop. The CROPGRO model is a mechanistic crop simulation of daily crop growth and development as a function of daily weather, soil properties, crop management, and species parameters. We adapted the CROPGRO model to simulate carinata based on growth analysis data collected over two seasons at three sites: Quincy, FL, Jay, FL, and Shorter, AL. The adaptation process required literature knowledge as well as optimization against field observations. The parameterization of model sensitivities to climatic factors is presented. The adapted model gave good simulations of carinata growth dynamics compared to observed growth during different seasons and locations and in response to N fertilization. While additional testing is appropriate, the model is sufficiently ready to be used for various applications. An example application is presented for the effect of sowing date on carinata yield and maturity over long-term weather in the Southeastern USA.

关键词： Brassica carinata crop simulation model growth analyses jet fuel model parameterization N response

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EVALUATION OF FOUR parameterization STRATEGIES FOR THE APEX model

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TRANSACTIONS OF THE ASABE 2018年第5期61卷 1603-1617页

作者： Senaviratne, G. M. M. M. A. Baffaut, C. Lory, J. A. Udawatta, R. P. Nelson, N. O. Bhandari, A. B. Univ Missouri Div Plant Sci Columbia MO USA USDA ARS Cropping Syst & Water Qual Res Unit Columbia MO USA Univ Missouri Ctr Agroforestry Columbia MO USA Kansas State Univ Dept Agron Manhattan KS 66506 USA Ft Hays State Univ Dept Agr Hays KS 67601 USA

State-of-the-art model parameterization consists of calibrating and validating the model using monitored data. When data are not available, many studies have relied on alternate strategies, including parameter sets obtained for eco-hydrologically similar watersheds and best professional judgment (BPJ). The objectives of this study were to (1) test the accuracy of four APEX parameterization strategies to predict runoff quantity and quality in a small agricultural watershed and (2) compare the effects of the different parameter sets on relative and absolute water quality outcomes for different conservation practice scenarios. A BPJ and three parameter sets obtained on nearby and more distant sites, including two fully calibrated parameter sets, i.e., for runoff, sediment, and total phosphorus (TP), and one partially calibrated parameter set, i.e., for runoff and TP only, were evaluated based on edge-of-field runoff, sediment loss, and TP loss, as well as for long-term annual predictions and relative changes for six conservation practice scenarios. Only the parameter sets that were fully calibrated met the model performance criteria on the test watershed and produced similar 30-year average annual predictions for the conservation practices. Relative changes in conservation outcomes were similar for the fully and partially calibrated parameter sets. They were different for the BPJ parameter set for at least one conservation practice. In the absence of site-specific data, the best parameterization strategy was to use parameter sets from a model calibrated for runoff, sediment, and nutrient losses from an eco-hydrologically similar site. Partial calibration of the model was sufficient to obtain consistent relative effects of the conservation practices.

关键词： Conservation practices Hydrologic modeling model parameterization Phosphorus Sediment

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Stochastic Pix2pix: A New Machine Learning Method for Geophysical and Well Conditioning of Rule-Based Channel Reservoir models

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NATURAL RESOURCES RESEARCH 2021年第2期30卷 1319-1345页

作者： Pan, Wen Torres-Verdin, Carlos Pyrcz, Michael J. Univ Texas Austin Hildebrand Dept Petr & Geosyst Austin TX 78712 USA Univ Texas Austin Jackson Sch Geosci Austin TX 78712 USA

Constructing subsurface models that accurately reproduce geological heterogeneity and their associated uncertainty is critical to many geoscience and engineering applications. For hydrocarbon reservoir modeling and forecasting, for example, spatial variability must be consistent with geological processes, geophysical measurements, and time records of fluid production measurements. Generating such subsurface models can be time-consuming;conditioning them to different types of measurements is even more computationally intensive and technically challenging. Using too many free variables can cause overfitting of the data, thereby decreasing the predictive ability of the model;high dimensionality also slows convergence during history matching of fluid production measurements. To contend with these problems, we introduce here a new machine learning approach, referred to as the stochastic pix2pix method, which parameterizes high-dimensional, stochastic reservoir models into low-dimensional Gaussian random variables in latent space. Many of the world's significant hydrocarbon fields originate from fluvial or turbidite deposits, with sedimentary processes and spatial distribution of rock facies significantly influencing their flow behavior. Rule- or object-based methods are commonly used to model geostatistically these types of reservoirs. Here, we introduce a new and efficient machine learning-based reservoir modeling workflow capable of generating 2D fluvial reservoir models that account for the available field data and the geometries of different facies. By constraining subsurface model realizations to available geophysical and petrophysical interpretations, multiphysics inversion can be greatly accelerated, thereby requiring only production history matching. Although our models are 2D, an extension to 3D can be readily implemented through zone-by-zone (i.e., reservoir unit) modeling and conditioning. The proposed method and workflow also partially solve the common proble

关键词： Channel reservoir modeling Rule-based fluvial reservoir model conditioning Machine learning Geostatistics model parameterization Reservoir characterization

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Reparameterizing Litter Decomposition Using a Simplified Monte Carlo Method Improves Litter Decay Simulated by a Microbial model and Alters Bioenergy Soil Carbon Estimates

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JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 2024年第3期129卷 e2023JG007625-e2023JG007625页

作者： Juice, S. M. Ridgeway, J. R. Hartman, M. D. Parton, W. J. Berardi, D. M. Sulman, B. N. Allen, K. E. Brzostek, E. R. West Virginia Univ Dept Biol Morgantown WV 26506 USA Univ Illinois Champaign Urbana Ctr Adv Bioenergy & Bioprod Innovat Urbana 61820 IL USA Colorado State Univ Nat Resource Ecol Lab Ft Collins CO USA Univ Idaho Dept Forest Rangeland & Fire Sci Moscow ID USA Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Manaaki Whenua Landcare Res Lincoln New Zealand

Litter decomposition determines soil organic matter (SOM) formation and plant-available nutrient cycles. Therefore, accurate model representation of litter decomposition is critical to improving soil carbon (C) projections of bioenergy feedstocks. Soil C models that simulate microbial physiology (i.e., microbial models) are new to bioenergy agriculture, and their parameterization is often based on small datasets or manual calibration to reach benchmarks. Here, we reparameterized litter decomposition in a microbial soil C model (CORPSE - Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) using the continental-scale Long-term Inter-site Decomposition Experiment Team (LIDET) dataset which documents decomposition across a range of litter qualities over a decade. We conducted a simplified Monte Carlo simulation that constrained parameter values to reduce computational costs. The LIDET-derived parameters improved modeled C and nitrogen (N) remaining, decomposition rates, and litter mean residence times as compared to Baseline parameters. We applied the LIDET litter decomposition parameters to a microbial bioenergy model (Fixation and Uptake of Nitrogen - Bioenergy Carbon, Rhizosphere, Organisms, and Protection) to examine soil C estimates generated by Baseline and LIDET parameters. LIDET parameters increased estimated soil C in bioenergy feedstocks, with even greater increases under elevated plant inputs (i.e., by increasing residue, N fertilization). This was due to the integrated effects of plant litter quantity, quality, and agricultural practices (tillage, fertilization). Collectively, we developed a simple framework for using large-scale datasets to inform the parameterization of microbial models that impacts projections of soil C for bioenergy feedstocks. Decomposition breaks down organic matter like leaves and roots, creating soil organic material and releasing essential nutrients for plant and microbial growth. Soil creation and nutrient relea

关键词： model parameterization microbial soil model bioenergy soil carbon modified Monte Carlo

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Convergence in simulating global soil organic carbon by structurally different models after data assimilation

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GLOBAL CHANGE BIOLOGY 2024年第5期30卷 e17297-e17297页

作者： Tao, Feng Houlton, Benjamin Z. Huang, Yuanyuan Wang, Ying-Ping Manzoni, Stefano Ahrens, Bernhard Mishra, Umakant Jiang, Lifen Huang, Xiaomeng Luo, Yiqi Cornell Univ Dept Ecol & Evolutionary Biol Ithaca NY 14850 USA Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist EducKey Lab Earth Syst Modelling Beijing 100084 Peoples R China Cornell Univ Dept Global Dev Ithaca NY USA Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosyst Network Observat & Modeling Beijing Peoples R China CSIRO Environm Clayton Vic Australia Stockholm Univ Bolin Ctr Climate Res Dept Phys Geog Stockholm Sweden Max Planck Inst Biogeochem Jena Germany Sandia Natl Labs Computat Biol & Biophys Livermore CA USA Lawrence Berkeley Natl Lab Joint BioEnergy Inst Emeryville CA USA Cornell Univ Sch Integrat Plant Sci Soil & Crop Sci Sect Ithaca NY USA

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions. Our study demonstrates the critical role of observational data in reducing uncertainties of global soil organic carbon (SOC) simulations by structurally different models. Two process-based models structurally featuring distinctive carbon pools, decomposition

关键词： big data assimilation deep learning inter-model uncertainty model parameterization model structure soil organic carbon

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parameterization and Validation of an Electrochemical Thermal model of a Lithium-Ion Battery

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BATTERIES-BASEL 2019年第3期5卷 62页

作者： Liebig, Gerd Gupta, Gaurav Kirstein, Ulf Schuldt, Frank Agert, Carsten DLR Inst Networked Energy Syst D-26123 Oldenburg Germany

The key challenge in developing a physico-chemical model is the model parameterization. The paper presents a strategic model parameterization procedure, parameter values, and a developed model that allows simulating electrochemical and thermal behavior of a commercial lithium-ion battery with high accuracy. Steps taken are the analysis of geometry details by opening a battery cell under argon atmosphere, building upon reference data of similar material compositions, incorporating cell balancing by a quasi-open-circuit-voltage experiment, and adapting the battery models reaction kinetics behavior by comparing experiment and simulation of an electrochemical impedance spectroscopy and hybrid pulse power characterization. The electrochemical-thermal coupled model is established based on COMSOL Multiphysics (R) platform (Stockholm, Sweden) and validated via experimental methods. The parameterized model was adopted to analyze the heat dissipation sources based on the internal states of the battery at different operation modes. Simulation in the field of thermal management for lithium-ion batteries highly depends on state of charge-related thermal issues of the incorporated cell composition. The electrode balancing is an essential step to be performed in order to address the internal battery states realistically. The individual contribution of the cell components heat dissipation has significant influence on the temperature distribution pattern based on the kinetic and thermodynamic properties.

关键词： lithium-ion battery electrochemical-thermal model model parameterization model validation electrochemical impedance spectroscopy hybrid pulse power characterization internal resistance heat dissipation reversible and irreversible heat

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Phytoplankton thermal trait parameterization alters community structure and biogeochemical processes in a modeled ocean

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GLOBAL CHANGE BIOLOGY 2024年第1期30卷 e17093-e17093页

作者： Anderson, Stephanie I. Fronda, Clara Barton, Andrew D. Clayton, Sophie Rynearson, Tatiana A. Dutkiewicz, Stephanie MIT Dept Earth Atmospher & Planetary Sci Cambridge MA 02139 USA Ecole Normale Super Lab Phys Paris France Scripps Inst Oceanog San Diego CA USA Dept Ecol Behav & Evolut San Diego CA USA Old Dominion Univ Dept Ocean & Earth Sci Norfolk VA USA Univ Rhode Isl Grad Sch Oceanog Narragansett RI USA Natl Oceanog Ctr Ocean BioGeosci Southampton Hants England

Phytoplankton exhibit diverse physiological responses to temperature which influence their fitness in the environment and consequently alter their community structure. Here, we explored the sensitivity of phytoplankton community structure to thermal response parameterization in a modelled marine phytoplankton community. Using published empirical data, we evaluated the maximum thermal growth rates (mu (max)) and temperature coefficients (Q(10);the rate at which growth scales with temperature) of six key Phytoplankton Functional Types (PFTs): coccolithophores, cyanobacteria, diatoms, diazotrophs, dinoflagellates, and green algae. Following three well-documented methods, PFTs were either assumed to have (1) the same mu (max) and the same Q(10) (as in to Eppley, 1972), (2) a unique mu (max) but the same Q(10) (similar to Kremer et al., 2017), or (3) a unique mu (max) and a unique Q(10) (following Anderson et al., 2021). These trait values were then implemented within the Massachusetts Institute of Technology biogeochemistry and ecosystem model (called Darwin) for each PFT under a control and climate change scenario. Our results suggest that applying a mu (max) and Q(10) universally across PFTs (as in Eppley, 1972) leads to unrealistic phytoplankton communities, which lack diatoms globally. Additionally, we find that accounting for differences in the Q(10) between PFTs can significantly impact each PFT's competitive ability, especially at high latitudes, leading to altered modeled phytoplankton community structures in our control and climate change simulations. This then impacts estimates of biogeochemical processes, with, for example, estimates of export production varying by ~10% in the Southern Ocean depending on the parameterization. Our results indicate that the diversity of thermal response traits in phytoplankton not only shape community composition in the historical and future, warmer ocean, but that these traits have significant feedbacks on global biogeochemica

关键词： export processes global change marine biogeochemistry marine ecology model parameterization phytoplankton thermal traits

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Learning Performance models Automatically 18th

Learning Performance Models Automatically

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18th International Conference on Service-Oriented Computing (ICSOC)

作者： Wang, Runan Imperial Coll London Dept Comp London England

ISBN: (纸本)9783030763527;9783030763510

To ensure the quality of frequent releases in DevOps context, performance models enable system performance simulation and prediction. However, building performance models for microservice or serverless-based applications in DevOps is costly and error-prone. Thus, we propose to employ model discovery learning for performance models automatically. To generate basic models to represent the application, we first introduce performance-related TOSCA models as architectural models. Then we transform TOSCA models into layered queueing network models. A main challenge of performance model generation is model parametrization. We propose to learn parametric dependencies from monitoring data and systems analysis to capture the relationship between input data and resource demand. With frequent releases of new features, we consider employing detecting parametric dependencies incrementally to keep updating performance models in each iteration.

关键词： model discovery Performance models model parameterization Parametric dependencies

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model Development of Diesel Generator using Volts/Hertz Limiter and Comparing Governor models for Remote Islanded Microgrids 47

Model Development of Diesel Generator using Volts/Hertz Limi...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Shah, Chinmay Cicilio, Phylicia Shirazi, Mariko Light, David Broderson, Dayne Wies, Richard Rauniyar, Manisha Tonkoski, Reinaldo Hansen, Timothy M. Univ Alaska Fairbanks Fairbanks AK 99775 USA South Dakota State Univ Brookings SD 57007 USA

ISBN: (纸本)9781665435543

Diesel generators are an integral component of remote islanded microgrids in rural Alaska. As inverter-based generation is integrated in these microgrids, adequate transient modeling will be necessary as dynamic and transient stability issues can arise with significant contributions of inverter-based generation. A complete transient model of a diesel generator includes models of the machine, exciter, governor, and any relays or other limiting components. Few studies compare the adequacy of various types of diesel generator governor or exciter models or include a volts/hertz (V/Hz) limiting functionality commonly found in diesel generators deployed in such remote islanded microgrids. This paper introduces and compares diesel generator models in response to two load steps against responses from the ACEP PSI lab 400 kVA diesel generator which represents a realistic diesel generator found in remote islanded Alaskan microgrids. A simplified governor model is introduced and compared to the traditional DEGOV governor model, and the functionality of a V/Hz limiter added to a DC4B exciter is demonstrated.

关键词： Diesel generators model parameterization microgrid sensitivity analysis

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Numerical Solution of the Inverse Stefan Problem in the Analysis of the Artificial Freezing of a Rock Mass

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Mathematical models and Computer Simulations 2021年第5期13卷 877-886页

作者： Semin, M.A. Zaitsev, A.V. Levin, L. Yu. Mining Institute Ural Branch Russian Academy of Sciences Perm Russian Federation

Abstract: The problem of parametrization of a heat transfer model in a rock mass in the conditions of its artificial freezing is considered. To clarify the parameters of the model according to the data of measurements of the temperature of the rock mass in the control-thermal wells, it is proposed to solve Stefan’s coefficient inverse problem. In this study, the formulation of the inverse problem is considered and a numerical algorithm for its solution is proposed and implemented. The numerical algorithm is based on the iterative minimization of the smoothing functional of the mismatch between the measured and calculated temperatures in the control-thermal wells. The properties of the smoothing functional in the phase space of the thermophysical properties of the rock mass and the features of the choice of the parameters of the smoothing functional are investigated. © 2021, Pleiades Publishing, Ltd.

关键词： artificial ground freezing frozen wall inverse Stefan problem mathematical model model parameterization Tikhonov regularization

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