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DOE OSTI2014

SU-E-T-532: Validation and Implementation of Model-Based Patient Specific Quality Assurance Using Mobius3D and MobiusFX in a Clinical Setting

Purpose: This work carries out the commissioning and validation of the Mobius3D and MobiusFX software tools, which can replace the time-consuming measurement-based patient specific quality assurance (PSQA). Methods: The beam model supplied by Mobius3D was validated against a 21EX linac's beam measured data. Complex patient (VMAT) plans using Eclipse treatment planning system (TPS) was used to test the consistency between Mobius3D (calculates dose using patient image and field data) and MobiusFx (calculates dose using treatment dynalog files). Dose difference and gamma analysis (3%/3mm) between Mobius3D and MobiusFx were used to assess treatment plan and treatment delivery consistency. An end-to-end test was performed to validate Mobius3D and MobiusFx against ion chamber measurements. Effect of the dosimetric leaf gap (DLG) on Mobius3D dose calculation was additionally investigated. Results: Mobius3D beam model parameters matched within 1%-3% with our beam measured data. A comparison of Mobius3D and MobiusFx dose matrices for VMAT planned prostate cases showed (0.33±0.07)% mean dose difference with gamma values above 95%. The end-to-end test showed dose differences of 1% between Mobius3D and MobiusFx. Dependence of Mobius3D dose calculation upon DLG was explored by introducing a ±0.5 mm change in the default value for DLG. This change resulted in agreement differences above 2% Conclusion: Use of reference beam data would appear to speed up commissioning process for the clinical implementation of Mobius3D. However, careful consideration is required when comparing the information provided by the software, since large dose variations can be seen when the proper parameters are not optimized. The plan and delivered dose were in good agreement; hence MobiusFx has the potential to significantly speed up the PSQA process and at the same time helps to verify treatment parameters that are not possible with measurement-based PSQA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

The CONTIN algorithm and its application to determine the size distribution of microgel suspensions

We review a powerful regularization method, known as CONTIN, for obtaining the size distribution of colloidal suspensions from dynamic light scattering data. We show that together with the so-called L-curve criterion for selecting the optimal regularization parameter, the method correctly describes the average size and size distribution of microgel suspensions independently characterized using small-angle neutron scattering. In contrast, we find that when using the default regularization process, where the regularizer is selected via the “probability to reject” method, the results are not as satisfactory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Bayesian optimization of PYTHIA 8 tunes

A new tune (set of model parameters) is found for the six most important parameters of the PYTHIA 8 final state parton shower and hadronization model using Bayesian optimization. The tune fits the Large Electron-Positron collider (LEPI) data from ALEPH better than the default tune in PYTHIA 8. To the best of our knowledge, we present the most comprehensive application of Bayesian optimization to the tuning of a parton shower and hadronization model using the LEPI data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Improved Simulation of the QBO in E3SMv1

The quasi-biennial oscillation (QBO) of the zonal mean wind is a prominent feature of the tropical lower stratosphere and has been shown to influence stratospheric and tropospheric variability. Although observed for many decades, the QBO is difficult to be accurately captured in Earth system models. The newly developed U.S. Department of Energy Energy Exascale Earth System Model, Version 1 (E3SMv1) is capable of resolving stratospheric dynamics as it has a top at 0.1 hPa and 72 levels. The tropical oscillation of the zonal mean zonal wind in the default version of the model is too fast and too strong compared to the observed QBO. Here we evaluate the current simulation of the QBO and present simulations with modified convective gravity wave parameterization parameters that make the oscillation more realistic, especially in terms of the period. Although improvements in the QBO are obtained through modifications to the parameterized gravity waves, we find that the forcing of the QBO from Kelvin and mixed-Rossby gravity waves is small due to weak sources in the troposphere. The changes to the parameterized convective gravity waves (and QBO) are primarily limited to the tropical stratosphere. Small changes to mean wind and temperature are also found in Northern Hemisphere winter in the uppermost troposphere between 10°N and 60°N. Modifications to parameterized convectively generated gravity waves result in changes in interannual variability, primarily in the Southern Hemisphere, and modest changes in sea level pressure variability over the Pacific are also noted.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Improving a Multilevel Turbulence Closure Model for a Shallow Lake in Comparison With Other 1-D Models

Lakes differ from lands in water availability, heat capacity, albedo, and roughness, which affect local surface-atmospheric interactions. This study modified a multilevel upper ocean model (UOM) for lake applications and evaluated its performance in Lake Taihu (China) with comprehensive measurements against three popular one-dimensional (1-D) lake models. These models were based on different concepts, including the self-similarity (FLake), the wind-driven eddy diffusion (LISSS), the k-ε turbulence closure (SIMSTRAT), and a simplified turbulence closure (UOM). The surface flux scheme in these models was unified to exclude the discrepancies in representing air-lake exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature (LST) diurnal range. For each model, these deficiencies were significantly reduced by incorporating new physics schemes or calibrated tunable parameters based on systematic sensitivity tests. The primary modifications for UOM included (1) a new scheme of decreased surface roughness lengths to better characterize the shallow lake, (2) a solar radiation penetration scheme with increased light extinction coefficient and surface absorption fraction to account for the high water turbidity, and (3) turbulent Prandtl number increased by a factor of 20 to reduce the turbulent vertical mixing. All other models were improved in these three aspects (roughness, extinction, and mixing) within their original formulations. Given these improvements, UOM showed superior performance to other models in capturing LST diurnal cycle and daily to seasonal variations, as well as summer-autumn vertical stratification changes. The new UOM is well suited for application in shallow lakes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗

DOE OSTI2019

Modeling reactivity insertion experiments of TRISO particles in NSRR using BISON

This research presents BISON simulations of stress, temperature, and failure probability of tristructural isotropic (TRISO) fuel particles subjected to transient power pulse conditions in the Nuclear Safety Research Reactor (NSRR). We report that by modifying the default elastic properties of the PyC and the UO 2 coefficient of thermal expansion correlation, BISON was found to produce suitable agreement with the observed and independently simulated results when appropriate shape and scale Weibull parameters for SiC failure were chosen. The Weibull parameter effects were also explored within the range of SiC failure and were applied using experimental data from hemispherical crush testing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

An Uncertainty-Informed and High-Fidelity Performance Forecasting Framework for Heliostat Fields

Concentrating Solar Thermal (CST) tower systems employ heliostat fields to direct solar energy to a central receiver, which then transfers the heat either directly to a thermal process (e.g., steam production) or to a thermal energy storage system for future use. Heliostat fields compose a significant proportion of the project costs of a CST tower system and the performance of the heliostats determines a plant's productivity at a given location. While CST characterization tools such as SolarPILOT and System Advisor Model (SAM) include a large collection of inputs that influence the performance of a CST tower system, many are uncertain prior to the development of the project and may have a significant impact on the overall energy delivery and profitability of a project; moreover, the fidelity of these models under default conditions may be insufficient to determine the value of component improvements such as those under development in the Heliostat Consortium. This work introduces a Monte Carlo simulation framework that incorporates uncertainty in key performance parameters to generate confidence intervals and percentile estimates for a CST solar field's energy delivery.

14 SOLAR ENERGY↗

DOE OSTI2018

Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0

Abstract. Improving global-scale model representations of near-surface soil moisture and groundwater hydrology is important for accurately simulating terrestrial processes and predicting climate change effects on water resources. Most existing land surface models, including the default E3SM Land Model (ELMv0), which we modify here, routinely employ different formulations for water transport in the vadose and phreatic zones. Clark et al. (2015) identified a variably saturated Richards equation flow model as an important capability for improving simulation of coupled soil moisture and shallow groundwater dynamics. In this work, we developed the Variably Saturated Flow Model (VSFM) in ELMv1 to unify the treatment of soil hydrologic processes in the unsaturated and saturated zones. VSFM was tested on three benchmark problems and results were evaluated against observations and an existing benchmark model (PFLOTRAN). The ELMv1-VSFM's subsurface drainage parameter, fd, was calibrated to match an observationally constrained and spatially explicit global water table depth (WTD) product. Optimal spatially explicit fd values were obtained for 79 % of global 1.9∘ × 2.5∘ grid cells, while the remaining 21 % of global grid cells had predicted WTD deeper than the observationally constrained estimate. Comparison with predictions using the default fd value demonstrated that calibration significantly improved predictions, primarily by allowing much deeper WTDs. Model evaluation using the International Land Model Benchmarking package (ILAMB) showed that improvements in WTD predictions did not degrade model skill for any other metrics. We evaluated the computational performance of the VSFM model and found that the model is about 30 % more expensive than the default ELMv0 with an optimal processor layout. The modular software design of VSFM not only provides flexibility to configure the model for a range of problem setups but also allows for building the model independently of the ELM code, thus enabling straightforward testing of the model's physics against other models.

58 GEOSCIENCES↗

DOE OSTI2016

S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition

Modeling the molecular mechanisms that govern genetic variation can be useful in understanding the dynamics that drive genetic state transition in quasispecies viruses. For example, there is considerable interest in understanding how the relatively benign vaccine strains of poliovirus eventually revert to forms that confer neurovirulence and cause disease (ie, vaccine-derived poliovirus). This report describes a stochastic simulation model, S2M, which can be used to generate hypothetical outcomes based on known mechanisms of genetic diversity. S2M begins with predefined genotypes based on the Sabin-1 and Mahoney wild-type sequences, constructs a set of independent cell-based populations, and performs in-cell replication and cell-to-cell infection cycles while quantifying genetic changes that track the transition from Sabin-1 toward Mahoney. Realism is incorporated into the model by assigning defaults for variables that constrain mechanisms of genetic variability based roughly on metrics reported in the literature, yet these values can be modified at the command line in order to generate hypothetical outcomes driven by these parameters. To demonstrate the utility of S2M, simulations were performed to examine the effects of the rates of replication error and recombination and the presence or absence of defective interfering particles, upon reaching the end states of Mahoney resemblance (semblance of a vaccine-derived state), neurovirulence, genome fitness, and cloud diversity. Simulations provide insight into how modeled biological features may drive hypothetical outcomes, independently or in combination, in ways that are not always intuitively obvious.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes

We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in Eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties determined by the parameter space defined by the GLOPNET global leaf trait database. Further, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked with each other, but we also find support for direct linkages to environmental conditions. We advocate for intensified study of the costs and benefits of plant life history strategies in different environments, and for the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

NPIP: A skew line needle configuration optimization system for HDR brachytherapy

Purpose: In this study, the authors introduce skew line needle configurations for high dose rate (HDR) brachytherapy and needle planning by integer program (NPIP), a computational method for generating these configurations. NPIP generates needle configurations that are specific to the anatomy of the patient, avoid critical structures near the penile bulb and other healthy structures, and avoid needle collisions inside the body. Methods: NPIP consisted of three major components: a method for generating a set of candidate needles, a needle selection component that chose a candidate needle subset to be inserted, and a dose planner for verifying that the final needle configuration could meet dose objectives. NPIP was used to compute needle configurations for prostate cancer data sets from patients previously treated at our clinic. NPIP took two user-parameters: a number of candidate needles, and needle coverage radius, {delta}. The candidate needle set consisted of 5000 needles, and a range of {delta} values was used to compute different needle configurations for each patient. Dose plans were computed for each needle configuration. The number of needles generated and dosimetry were analyzed and compared to the physician implant. Results: NPIP computed at least one needle configuration for every patient that met dose objectives, avoided healthy structures and needle collisions, and used as many or fewer needles than standard practice. These needle configurations corresponded to a narrow range of {delta} values, which could be used as default values if this system is used in practice. The average end-to-end runtime for this implementation of NPIP was 286 s, but there was a wide variation from case to case. Conclusions: The authors have shown that NPIP can automatically generate skew line needle configurations with the aforementioned properties, and that given the correct input parameters, NPIP can generate needle configurations which meet dose objectives and use as many or fewer needles than the current HDR brachytherapy workflow. Combined with robot assisted brachytherapy, this system has the potential to reduce side effects associated with treatment. A physical trial should be done to test the implant feasibility of NPIP needle configurations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

CSP Systems Analysis - Final Project Report

The CSP Systems Analysis project was a three-year effort supporting the Concentrating Solar Power (CSP) Subprogram within the Solar Energy Technologies Office of the U.S. Department of Energy (DOE). The goal of the CSP Systems Analysis project was to provide timely and accurate CSP cost data to the DOE and predict performance and cost for emerging technologies that may impact research directions and industry investment. The project consisted of six, mostly independent tasks. Major accomplishments over the three-year period of performance were as follows. The NREL team maintained current cost estimates for CSP technologies by annual updates to the cost input parameters within NREL's System Advisor Model (SAM). SAM is a performance and cost simulation software that includes technology models for the primary CSP technologies. During the period from 2016 to 2018, the estimated cost within SAM's default CSP molten-salt power tower technology dropped from 13.0 to 11.1 cent/kWh (real dollars without incentives). Power cycle models for the supercritical carbon dioxide (sCO2) Brayton Cycle were developed and made an option within SAM's molten-salt power tower model. These models were subsequently used to evaluate the cost reduction potential of alternative salts. The partial-cooling model was found to provide the greatest cost-reduction potential due to its combination of efficiency and temperature differential across the turbine and thermal energy storage (TES) system. These studies were documented in two journal articles. NREL's optical modeling software SolTrace and SolarPILOT were made more versatile and released as open-source software to the CSP community. These tools help researchers and developers to model and optimize the optical performance of CSP collector systems. The new features were highlighted in a recorded webinar that can be accessed from the NREL SAM website. New models for solar-thermal systems, that is, applications for solar industrial process heat, were developed, validated, and installed within SAM. The analysis includes a 2018 review of linear collectors in the market that are tailored for solar process heat applications. In 2018 the NREL team also developed an annual simulation for a direct steam generation system integrated with a latent-heat TES system. This analysis was submitted for journal publication. The NREL team continued to support the exchange of information and development of CSP guidelines within the international CSP community of SolarPACES. Much of the work under this project was presented at SolarPACES, including the development of a modeling checklist to help CSP stakeholders standardize the models used for estimating CSP performance and cost. The CSP Systems Analysis project also supported DOE requests for tracking and reporting programmatic targets for the CSP Subprogram.

14 SOLAR ENERGY↗

DOE OSTI2022

HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations

Physics-informed neural networks (PINNs) based machine learning is an emerging framework for solving nonlinear differential equations. However, due to the implicit regularity of neural network structure, PINNs can only find the flattest solution in most cases by minimizing the loss functions. In this paper, we combine PINNs with the homotopy continuation method, a classical numerical method to compute isolated roots of polynomial systems, and propose a new deep learning framework, named homotopy physics-informed neural networks (HomPINNs), for solving multiple solutions of nonlinear elliptic differential equations. The implementation of an HomPINN is a homotopy process that is composed of the training of a fully connected neural network, named the starting neural network, and training processes of several PINNs with different tracking parameters. The starting neural network is to approximate a starting function constructed by the trivial solutions, while other PINNs are to minimize the loss functions defined by boundary condition and homotopy functions, varying with different tracking parameters. These training processes are regraded as different steps of a homotopy process, and a PINN is initialized by the well-trained neural network of the previous step, while the first starting neural network is initialized using the default initialization method. Finally, several numerical examples are presented to show the efficiency of our proposed HomPINNs, including reaction-diffusion equations with a heart-shaped domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Comparison of the Performance of Various Correlated Fission Multiplicity Monte Carlo Codes

When measuring special nuclear material for a large array of national security needs, it is often important to accurately predict outcomes of the physical experiment using Monte Carlo radiation transport codes. Historically, radiation transport codes have uncorrelated fission emissions. In reality, both spontaneous and induced fissions release particles that are correlated in time, energy, and multiplicity. This work investigates the performance of various current Monte Carlo codes that take into account the correlated physics of fission neutrons. Only fission neutrons are of interest and the physics of gamma production in fission is ignored. Because of their large impact on correlated neutron results, underlying fission neutron multiplicity distributions utilized by the different codes are also compared. The codes currently being compared include MCNP{sup R}6, MCNP{sup R}6/FREYA, and MCNPX-PoliMi. By default, MCNP{sup R}6 uses a bounded integer treatment to sample the number of neutrons emitted from each fission event. The FMULT card, an optional input in MCNP that allows for user definition of spontaneous and induced fission parameters, can be utilized to call either built-in or user-specified multiplicities to replace the bounded integer treatment. Similarly, MCNPX-PoliMi utilizes one of a few different built-in multiplicity sets. MCNPX-PoliMi also models the multiplicity dependence of emitted neutron energy in spontaneous fission. It is possible to use an anisotropic model for emitted fission neutrons in MCNPXPoliMi, but this option was not used. For MCNP{sup R}6/FREYA, the FREYA fission event generator determines the number of particles emitted for each fission event and gives the results to MCNP{sup R}6 for transport. The fission event generator uses fission fragment mass and kinetic energy distributions, unbounded statistical evaporation models, and conservation of energy and momentum to generate the number, energy, and direction of neutrons released by each fission event. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Bayesian inference of the incompressibility, skewness and kurtosis of nuclear matter from empirical pressures in relativistic heavy-ion collisions

Within the Bayesian statistical framework we infer the incompressibility K 0 , skewness J 0 and kurtosis Z 0 parameters of symmetric nuclear matter (SNM) at its saturation density ρ 0 using the constraining bands on the pressure in cold SNM in the density range of 1.3ρ 0 to 4.5ρ 0 from transport model analyses of kaon production and nuclear collective flow in relativistic heavy-ion collisions. As the default option assuming the K 0 , J 0 and Z 0 have Gaussian prior probability distribution functions (PDFs) with the means and variances of 235±30, -200±200 and -146±1728 MeV, their posterior most probable values are narrowed down to 192$^{+12}_{-16}$ MeV, -180$^{+100}_{-110}$ MeV and ${\mathrm{200}}_{-250}^{+250}$ at 68% confidence level, respectively. The results are largely independent of the prior PDFs of J 0 and Z 0 used. However, if one adopts the strong belief that the incompressibility K 0 has a uniform prior PDF within its absolute boundary of 220–260 MeV as one can find easily in the literature, the posterior most probable values of K 0 , J 0 and Z 0 shift to ${K}_{0}=22{0}_{-0}^{+6}$ MeV, ${J}_{0}=-39{0}_{-70}^{+60}$ MeV and ${Z}_{0}=60{0}_{-200}^{+200}$ MeV, respectively. While the posterior PDFs of the SNM EOS parameters depend somewhat on the prior PDF of K 0 used, the results from using different prior PDFs are qualitatively consistent. The uncertainties of all three parameters are significantly reduced especially for the J 0 and Z 0 parameters compared to their current values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Evaluating the impact of aquifer layer properties on geomechanical response during CO2 geological sequestration

Numerical models play an essential role in understanding the facts of carbon dioxide (CO2) geological sequestration in the life cycle of a storage reservoir. We present a series of test cases that reflect a broad and realistic range of aquifer reservoir properties to systematically evaluate and compare the impacts on the geomechanical response to CO2 injection. In this study, a coupled hydro-mechanical model was introduced to simulate the sequestration process, and a quasi-Monte Carlo sampling method was introduced to efficiently sample the value of aquifer properties and geometry parameters. Aquifer permeability was found to be of significant importance to the geomechanical response to the injection. To study the influence of uncertainty of the permeability distribution in the aquifer, an additional series of tests is presented, based on a default permeability distribution site sample with various distribution deviations generated by the Monte Carlo sampling method. The results of the test series show that different permeability distributions significantly affect the displacement and possible failure zone.

Bao, Jie↗

DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗