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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A bespoke model of Arctic river basins based on hillslope delineation: Model Archive

This dataset is a model archive of the paper A bespoke model of Arctic river basins based on hillslope delineation (in prep), which introduces a watershed decomposition and parameterization method for large scale permafrost hydrology simulation. With this dataset, this study aims to address the research question: whether a computationally efficient hillslope-based modeling framework can reliably simulate discharge at Arctic river-basin scales. This dataset contains model input and output data for five modeling scenarios at a study site located in the Sagavanirktok River basin. The five modeling scenarios include three modeling cases under temperate conditions using full 3D, decomposed 3D, and decomposed 2D modeling strategies; and two modeling cases under actual Arctic conditions with permafrost using full 3D and decomposed 2D modeling strategies. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.6 for three temperate scenarios and v1.5 for two Arctic scenarios), a physics-rich integrated surface–subsurface hydrologic model with cryo-hydrology features. For the three temperate models, simulations were conducted for the period of 10/01/1993 - 09/30/2002; and for the two Arctic models, simulations were conducted for the period of 01/01/1994 - 12/31/2002. To facilitate reproducibility of simulations, all datasets are organized hierarchically. The dataset contains: (1) Mesh files (.exo) for full 3D model, decomposed 3D models, and decomposed 2D models, located in huc/190604020802_gauge15906000/mesh/. Mesh files can be visualized through Paraview or read by Python. (2) Climate forcings (.h5) for full 3D model and decomposed 3D/2D models are located in huc/190604020802_gauge15906000/daymet_onePiece/, and huc/190604020802_gauge15906000/vp_pr_revised_daymet_1980_2006_with_wind/ separately. Accessible by Python. (3) Raw measured gage discharge (.csv) from USGS, located in huc/190604020802_gauge15906000/gaged_basin15906000_discharge_usgs/. Accessible by Python. (4) Delineated subdomain raster (.tif) and shape files (.shp), and the final parameterized results (.npy) for decomposed models, located in huc/190604020802_gauge15906000/data_preprocessed-meshing. Accessible by Python. (5) Temperate models are located in nonpermaf_huc190604020802_gauge15906000/, which includes three cases: decomposed 2D models (inside model_0*-hillslope_*), decomposed 3D models (inside model_1*-subcatchment_*), and full 3D model (inside model_2*-onepiece_*). Two step spin-up results (checkpoint_final.h5) are located in model_*1-*_spinup_steadystate and model_*2-*_spinup_cycle, separately, which are used to initialize real transient models. The input files (.xml) and output results (.dat) of the real transient models are located in model_*3-*_transient/. Especially, for two example hillslope models (ID=-11 and 11), additional h5py files are included in model_03-hillslope_transient/hillslope-11/, model_03-hillslope_transient/hillslope11, model_13-subcatchment_transient/subcatchment-11/, model_13-subcatchment_transient/subcatchment/11, respectively, which are used to plot the saturation figure (Figure 5) in the manuscript. Accessible by Python. (6) Arctic models are located in huc190604020802_gauge15906000/, which includes two cases: decomposed 2D models (inside model_04-hillslope_transient), and full 3D model (inside model_05-onepiece_transient_mannp1_ra). Three step spin-up results (checkpoint_final.h5) are located in model_01-column_freezeup/, model_02-column_spinup/, model_03-hillslope_spinup/, respectively, which are used to initialize real 2D transient hillslope models. The input files (.xml) and output results (.dat) of transient 2D hillslope models are located in model_04-hillslope_transient/. The input files (.xml) and output results (.dat) of the full 3D transient model is located in model_05-onepiece_transient_mannp1_ra/. The full 3D transient model is initialized by model_02-column_spinup/. Accessible by Python. (7) The MOSART routed discharge results (.csv) under Arctic conditions is located in huc190604020802_gauge15906000/MOSART/. Accessible by Python. (8) All Python codes (.py) used to parameterize full 3D model to decomposed 2D models are located in script/. These codes fit with watershed workflow (a watershed delineation tool) v1.4 under the branch gaob/v1.4 from https://github.com/gaobhub/watershed-workflow.git.

EARTH SCIENCE > CRYOSPHERE↗
DOE OSTI2021

Grey-box modeling and application for building energy simulations - A critical review

Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. Additionally, it has been widely applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer

Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trained with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: An intertechnique and interinstitutional study

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution. Methods: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol. Results: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct. Conclusions: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results. The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Modification of the TASMIP x-ray spectral model for the simulation of microfocus x-ray sources

Purpose: The availability of accurate and simple models for the estimation of x-ray spectra is of great importance for system simulation, optimization, or inclusion of photon energy information into data processing. There is a variety of publicly available tools for estimation of x-ray spectra in radiology and mammography. However, most of these models cannot be used directly for modeling microfocus x-ray sources due to differences in inherent filtration, energy range and/or anode material. For this reason the authors propose in this work a new model for the simulation of microfocus spectra based on existing models for mammography and radiology, modified to compensate for the effects of inherent filtration and energy range. Methods: The authors used the radiology and mammography versions of an existing empirical model [tungsten anode spectral model interpolating polynomials (TASMIP)] as the basis of the microfocus model. First, the authors estimated the inherent filtration included in the radiology model by comparing the shape of the spectra with spectra from the mammography model. Afterwards, the authors built a unified spectra dataset by combining both models and, finally, they estimated the parameters of the new version of TASMIP for microfocus sources by calibrating against experimental exposure data from a microfocus x-ray source. The model was validated by comparing estimated and experimental exposure and attenuation data for different attenuating materials and x-ray beam peak energy values, using two different x-ray tubes. Results: Inherent filtration for the radiology spectra from TASMIP was found to be equivalent to 1.68 mm Al, as compared to spectra obtained from the mammography model. To match the experimentally measured exposure data the combined dataset required to apply a negative filtration of about 0.21 mm Al and an anode roughness of 0.003 mm W. The validation of the model against real acquired data showed errors in exposure and attenuation in line with those reported for other models for radiology or mammography. Conclusions: A new version of the TASMIP model for the estimation of x-ray spectra in microfocus x-ray sources has been developed and validated experimentally. Similarly to other versions of TASMIP, the estimation of spectra is very simple, involving only the evaluation of polynomial expressions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.

Dai, Heng↗
NASA NTRSDate not supplied

Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

crop models↗
DOE OSTI2020

Developing a common approach for classifying building stock energy models

Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, here we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2015

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Abstract Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial‐based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial‐enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal‐spatial scales. The dormancy model consistently produced better match with field‐observed heterotrophic soil CO 2 efflux ( R H ) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil R H (7.5 ± 2.4 Pg C yr −1 ). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil R H with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon‐to‐nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale‐dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial‐based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

He, Yujie↗
DOE OSTI2014

Effect of burst and recombination models for Monte Carlo transport of interacting carriers in a-Se x-ray detectors on Swank noise

Purpose: The authors describe the modification to a previously developed Monte Carlo model of semiconductor direct x-ray detector required for studying the effect of burst and recombination algorithms on detector performance. This work provides insight into the effect of different charge generation models for a-Se detectors on Swank noise and recombination fraction. Methods: The proposed burst and recombination models are implemented in the Monte Carlo simulation package, ARTEMIS, developed byFang et al. [“Spatiotemporal Monte Carlo transport methods in x-ray semiconductor detectors: Application to pulse-height spectroscopy in a-Se,” Med. Phys. 39(1), 308–319 (2012)]. The burst model generates a cloud of electron-hole pairs based on electron velocity, energy deposition, and material parameters distributed within a spherical uniform volume (SUV) or on a spherical surface area (SSA). A simple first-hit (FH) and a more detailed but computationally expensive nearest-neighbor (NN) recombination algorithms are also described and compared. Results: Simulated recombination fractions for a single electron-hole pair show good agreement with Onsager model for a wide range of electric field, thermalization distance, and temperature. The recombination fraction and Swank noise exhibit a dependence on the burst model for generation of many electron-hole pairs from a single x ray. The Swank noise decreased for the SSA compared to the SUV model at 4 V/μm, while the recombination fraction decreased for SSA compared to the SUV model at 30 V/μm. The NN and FH recombination results were comparable. Conclusions: Results obtained with the ARTEMIS Monte Carlo transport model incorporating drift and diffusion are validated with the Onsager model for a single electron-hole pair as a function of electric field, thermalization distance, and temperature. For x-ray interactions, the authors demonstrate that the choice of burst model can affect the simulation results for the generation of many electron-hole pairs. The SSA model is more sensitive to the effect of electric field compared to the SUV model and that the NN and FH recombination algorithms did not significantly affect simulation results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Guiding Principles for Geochemical/Thermodynamic Model Development and Validation in Nuclear Waste Disposal: A Close Examination of Recent Thermodynamic Models for H + —Nd 3+ —NO 3 - (—Oxalate) Systems

Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Use of Physics to Improve Solar Forecast: Part II, Machine Learning and Model Interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.

interpretability↗
DOE OSTI2015

Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally, limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.

58 GEOSCIENCES↗
NASA NTRS2006

Radiation Environment Modeling for Spacecraft Design: New Model Developments

A viewgraph presentation on various new space radiation environment models for spacecraft design is described. The topics include: 1) The Space Radiatio Environment; 2) Effects of Space Environments on Systems; 3) Space Radiatio Environment Model Use During Space Mission Development and Operations; 4) Space Radiation Hazards for Humans; 5) "Standard" Space Radiation Environment Models; 6) Concerns about Standard Models; 7) Inadequacies of Current Models; 8) Development of New Models; 9) New Model Developments: Proton Belt Models; 10) Coverage of New Proton Models; 11) Comparison of TPM-1, PSB97, AP-8; 12) New Model Developments: Electron Belt Models; 13) Coverage of New Electron Models; 14) Comparison of "Worst Case" POLE, CRESELE, and FLUMIC Models with the AE-8 Model; 15) New Model Developments: Galactic Cosmic Ray Model; 16) Comparison of NASA, MSU, CIT Models with ACE Instrument Data; 17) New Model Developmemts: Solar Proton Model; 18) Comparison of ESP, JPL91, KIng/Stassinopoulos, and PSYCHIC Models; 19) New Model Developments: Solar Heavy Ion Model; 20) Comparison of CREME96 to CREDO Measurements During 2000 and 2002; 21) PSYCHIC Heavy ion Model; 22) Model Standardization; 23) Working Group Meeting on New Standard Radiation Belt and Space Plasma Models; and 24) Summary.

Barth, Janet↗
DOE OSTI2016

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Croplands are extremely productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

An equivalent dissipation rate model for capturing history effects in non-premixed flames

The effects of strain rate history on turbulent flames have been studied in the. past decades with 1D counter flow diffusion flame (CFDF) configurations subjected to oscillating strain rates. In this work, these unsteady effects are studied for complex hydrocarbon fuel surrogates at engine relevant conditions with unsteady strain rates experienced by flamelets in a typical spray flame. Tabulated combustion models are based on a steady scalar dissipation rate (SDR) assumption and hence cannot capture these unsteady strain effects; even though they can capture the unsteady chemistry. In this work, 1D CFDF with varying strain rates are simulated using two different modeling approaches: steady SDR assumption and unsteady flamelet model. Comparative studies show that the history effects due to unsteady SDR are directly proportional to the temporal gradient of the SDR. A new equivalent SDR model based on the history of a flamelet is proposed. An averaging procedure is constructed such that the most recent histories are given higher weights. This equivalent SDR is then used with the steady SDR assumption in 1D flamelets. Results show a good agreement between tabulated flamelet solution and the unsteady flamelet results. This equivalent SDR concept is further implemented and compared against 3D spray flames (Engine Combustion Network Spray A). Tabulated models based on steady SDR assumption under-predict autoignition and flame lift-off when compared with an unsteady Representative Interactive Flamelet (RIF) model. However, equivalent SDR model coupled with the tabulated model predicted autoignition and flame lift-off very close to those reported by the RIF model. This model is further validated for a range of injection pressures for Spray A flames. The new modeling framework now enables tabulated models with significantly lower computational cost to account for unsteady history effects. (C) 2016 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Kundu, Prithwish↗