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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Efficient Adjoint Computation of Hybrid Systems of Differential Algebraic Equations with Applications in Power Systems

Sensitivity analysis is an important tool to describe power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this work, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating trajectory sensitivities of larger systems and is consistent, within machine precision, with the function whose sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as DC exciters, by deriving and implementing the adjoint jump conditions that arise from state and time-dependent discontinuities. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Thermal Analysis for SRS Saltstone Disposal Unit Facility

Saltstone Disposal Units (SDU) are long term disposal units for low activity grout produced from the solidification of decontaminated non-hazardous salt waste at SRS. The unit is a cylindrical disposal vault of 150 ft. in diameter and 23 ft. in height. The total volume the SDU can hold is 3 million gallons of grout. The hydration process of the grout material produces heat from an exothermic chemical reaction. If the grout reaches temperatures higher than the maximum safety temperature of 95 deg. C, cracks can start forming leading to contamination of the environment. The purpose of this study is to develop the optimal baseline domain boundary conditions and to perform a sensitivity analysis with respect to the baseline modeling conditions. The following assumptions were made for the modeling calculations: - The system geometry is axisymmetric; - The system was at steady state; - Ambient temperature was set to 100 deg. F; - Air was assumed to be an incompressible ideal gas; - Radiation was neglected for a conservative estimation; - Natural convection for the SDU vapor space is assumed to be turbulent because Rayleigh number based on typical operating conditions is higher than 10{sup 8}; - Water tables were not taken into consideration. The following sensitivity analyses were performed to find the optimal domain boundary: - Mesh size sensitivity; - Soil depth sensitivity - Soil depth varied from 30 ft to 250 ft with a soil thickness of 90 ft; - Soil thickness sensitivity - Soil thickness varied from 30 ft to 270 ft with a soil depth of 180 ft; - Soil conductivity sensitivity - Soil thermal conductivity varied from 0.25 W/m-K to 1 W/m-K; - Ambient Temperature Sensitivity - Ambient temperature varied from 70 deg. F to 110 deg. F. For the thermal calculations, the temperature of the bottom of the soil was set to 17.4 deg. C. The heat flux on the roof of the building and surface of the earth is 163.2 W/m{sup 2}. The soil sides are set to be adiabatic. The sensitivity analysis shows that the mesh size has no effect on the numerical results. This is mainly due to a small convection zone and large conduction zones in the modeling domain. The optimal domain boundary was found to be a soil thickness of 90 ft and a soil depth of 150 ft. The different sensitivity analysis performed here shows the optimal boundary domain to be used for thermal calculations is a soil depth of 150 ft beneath the SDU and a soil thickness of 90 ft. Anything larger would produce similar solutions but at a longer computational time to solve. The maximum temperature was more sensitive to soil properties as shown here. While the ambient temperature affected the max temperature linearly. Most of the heat transfer takes place downward vertically between the SDU and the soil beneath it. Using the optimal domain boundary determined by the sensitivity analyses, the maximum thermal loading to keep the maximum grout temperature below 95 deg. C is 1.646 W/m{sup 3}.

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

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Sensitivity and optimization of composite structures using MSC/NASTRAN

Design sensitivity analysis for composites will soon be available in MSC/NASTRAN. The design variables for composites can be lamina thicknesses, orientation angles, material properties or a combination of all three. With the increasing use of composites in aerospace and automotive industries, this general capability can be used in its own right for carrying out sensitivity analysis of complicated real-life structures. As part of a research effort, the sensitivity analysis was coupled with a general purpose optimizer. This preliminary version of the optimizer is capable of dealing with minimum weight structural design with a rather general design variable linking capability at the element level or system level. Only sizing type of design variables (i.e., lamina thicknesses) can be handled by the optimizer. Test cases were run and validated by comparison with independent finite element packages. The linking of design sensitivity capability for composites in MSC/NASTRAN with an optimizer would give designers a powerful automated tool to carry out practical opitmization design of real-life complicated composite structures.

Nagendra, Gopal K.↗
NASA NTRS1991

Some Aspects of uncertainty in computational fluid dynamics results

Uncertainties are inherent in computational fluid dynamics (CFD). These uncertainties need to be systematically addressed and managed. Sources of these uncertainty analysis are discussed. Some recommendations are made for quantification of CFD uncertainties. A practical method of uncertainty analysis is based on sensitivity analysis. When CFD is used to design fluid dynamic systems, sensitivity-uncertainty analysis is essential.

Mehta, U. B.↗
NASA NTRS1984

Structural synthesis: Precursor and catalyst

More than twenty five years have elapsed since it was recognized that a rather general class of structural design optimization tasks could be properly posed as an inequality constrained minimization problem. It is suggested that, independent of primary discipline area, it will be useful to think about: (1) posing design problems in terms of an objective function and inequality constraints; (2) generating design oriented approximate analysis methods (giving special attention to behavior sensitivity analysis); (3) distinguishing between decisions that lead to an analysis model and those that lead to a design model; (4) finding ways to generate a sequence of approximate design optimization problems that capture the essential characteristics of the primary problem, while still having an explicit algebraic form that is matched to one or more of the established optimization algorithms; (5) examining the potential of optimum design sensitivity analysis to facilitate quantitative trade-off studies as well as participation in multilevel design activities. It should be kept in mind that multilevel methods are inherently well suited to a parallel mode of operation in computer terms or to a division of labor between task groups in organizational terms. Based on structural experience with multilevel methods general guidelines are suggested.

Schmit, L. A.↗
NASA NTRS2017

Thermal Design, Analysis, and Sensitivity of a Sample Tube on the Martian Surface

NASA will launch a rover to Mars in 2020 to collect and cache samples of Martian rocks and regolith for potential return to Earth on a future mission. The collected samples will be placed in tubes, hermetically sealed, and deposited on the surface of Mars for a period of up to 10 years. Preventing the deposited samples from overheating is an important consideration in order to ensure their scientific integrity. The samples are required to remain below 60 °C, however there is a goal to keep them below 40 °C. This temperature requirement is challenging, considering that the maximum surface temperature of Mars is 38 °C and the tube must be thermally controlled through only passive means. As a result, a significant effort has been made to design a tube to contain the samples and investigate innovative tube surface treatments that maintain proper sample temperatures without significant adverse effects of sample contamination. The tube uses a specialized aluminum oxide coating to prevent overheating on the Martian surface. The maximum predicted tube temperature is relatively insensitive to local thermal inertia, ground contact, and ground slope, but is very sensitive to landing site latitude and dust deposition. The maximum predicted sample temperature in the +/- 30° landing latitude range with worst case assumptions is 54 °C, but many of the potential landing sites have significantly cooler maximum predicted sample temperatures. This paper describes the thermal design, analysis, and sensitivity studies performed on the sample tubes and also discusses operational considerations.

Novak, Keith↗
NASA NTRS1990

Critical evaluation of Jet-A spray combustion using propane chemical kinetics in gas turbine combustion simulated by KIVA-2

Jet-A spray combustion has been evaluated in gas turbine combustion with the use of propane chemical kinetics as the first approximation for the chemical reactions. Here, the numerical solutions are obtained by using the KIVA-2 computer code. The KIVA-2 code is the most developed of the available multidimensional combustion computer programs for application of the in-cylinder combustion dynamics of internal combustion engines. The released version of KIVA-2 assumes that 12 chemical species are present; the code uses an Arrhenius kinetic-controlled combustion model governed by a four-step global chemical reaction and six equilibrium reactions. Researchers efforts involve the addition of Jet-A thermophysical properties and the implementation of detailed reaction mechanisms for propane oxidation. Three different detailed reaction mechanism models are considered. The first model consists of 131 reactions and 45 species. This is considered as the full mechanism which is developed through the study of chemical kinetics of propane combustion in an enclosed chamber. The full mechanism is evaluated by comparing calculated ignition delay times with available shock tube data. However, these detailed reactions occupy too much computer memory and CPU time for the computation. Therefore, it only serves as a benchmark case by which to evaluate other simplified models. Two possible simplified models were tested in the existing computer code KIVA-2 for the same conditions as used with the full mechanism. One model is obtained through a sensitivity analysis using LSENS, the general kinetics and sensitivity analysis program code of D. A. Bittker and K. Radhakrishnan. This model consists of 45 chemical reactions and 27 species. The other model is based on the work published by C. K. Westbrook and F. L. Dryer.

Nguyen, H. L.↗
DOE OSTI2017

Analysis of Factors that Influence Infiltration Rates using the HELP Model

The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall system model for the E-Area LLWF.

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

Modeling perennial bioenergy crops in the E3SM land model (ELMv2)

Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few earth system models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Model (E3SM) Land Model (ELMv2) to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high-productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality , although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems.

09 BIOMASS FUELS↗
DOE OSTI2023

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗
NASA NTRS1984

Acoustically-induced modulation spectroscopy for ultra-sensitive gas analysis

A new optical technique has been developed for ultra-sensitive attenuation measurements in gaseous media and, in particular, for determination of low levels of smoke emitted from jet engines. It is a variation on direct light transmission where the sample gas density in a cell is modulated acoustically by a speaker. The amplitude variation of the light transmission is proportional to the gas density and is insensitive to window contamination and detector instabilities. Preliminary analysis and experiments indicate that the instrument promises to measure light absorption to less than 1 percent per meter and allow measurment of smoke emissions from 1 to 100 mg/cu m. The technique has been demonstrated through the use of an absorbing gas, viz., 200 ppm of NO2 in N2 which produces 25 percent per meter absorption.

Pitz, R. W.↗
DOE OSTI2016

Experimental and CFD Studies of Coolant Flow Mixing within Scaled Models of the Upper and Lower Plenums of NGNP Gas-Cooled Reactors

A 1/16th scaled VHTR experimental model was constructed and the preliminary test was performed in this study. To produce benchmark data for CFD validation in the future, the facility was first run at partial operation with five pipes being heated. PIV was performed to extract the vector velocity field for three adjacent naturally convective jets at statistically steady state. A small recirculation zone was found between the pipes, and the jets entered the merging zone at 3 cm from the pipe outlet but diverged as the flow approached the top of the test geometry. Turbulence analysis shows the turbulence intensity peaked at 41-45% as the jets mixed. A sensitivity analysis confirmed that 1000 frames were sufficient to measure statistically steady state. The results were then validated by extracting the flow rate from the PIV jet velocity profile, and comparing it with an analytic flow rate and ultrasonic flowmeter; all flow rates lie within the uncertainty of the other two methods for Tests 1 and 2. This test facility can be used for further analysis of naturally convective mixing, and eventually produce benchmark data for CFD validation for the VHTR during a PCC or DCC accident scenario. Next, a PTV study of 3000 images (1500 image pairs) were used to quantify the velocity field in the upper plenum. A sensitivity analysis confirmed that 1500 frames were sufficient to precisely estimate the flow. Subsequently, three (3, 9, and 15 cm) Y-lines from the pipe output were extracted to consider the output differences between 50 to 1500 frames. The average velocity field and standard deviation error that accrued in the three different tests were calculated to assess repeatability. The error was varied, from 1 to 14%, depending on Y-elevation. The error decreased as the flow moved farther from the output pipe. In addition, turbulent intensity was calculated and found to be high near the output. Reynolds stresses and turbulent intensity were used to validate the data by comparing it with benchmark data. The experimental data gave the same pattern as the benchmark data. A turbulent single buoyant jet study was performed for the case of LOFC in the upper plenum of scaled VHTR. Time-averaged profiles show that 3,000 frames of images were sufficient for the study up to second-order statistics. Self-similarity is an important feature of jets since the behavior of jets is independent of Reynolds number and a sole function of geometry. Self-similarity profiles were well observed in the axial velocity and velocity magnitude profile regardless of z/D where the radial velocity did not show any similarity pattern. The normal components of Reynolds stresses have self-similarity within the expected range. The study shows that large vortices were observed close to the dome wall, indicating that the geometry of the VHTR has a significant impact on its safety and performance. Near the dome surface, large vortices were shown to inhibit the flows, resulting in reduced axial jet velocity. The vortices that develop subsequently reduce the Reynolds stresses that develop and the impact on the integrity of the VHTR upper plenum surface. Multiple jets study, including two, three and five jets, were investigated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Analysis of Supply Chains and Advanced Manufacturing of Small Hydropower Systems

Clean energy manufacturing is a sector of increasing importance both in the United States and worldwide. In the United States, it has been presented as an engine for job creation in a sector of the U.S. economy that was hit hard by the 2008-2009 recession. The Clean Energy Manufacturing Analysis Center (CEMAC), sponsored by the U.S. Department of Energy (DOE), conducts credible, objective, industry-relevant, recurring, and consistent analyses of clean energy technologies. In this project, the National Renewable Energy Laboratory (NREL) and the Oak Ridge National Laboratory (ORNL) collaborated in a hydropower manufacturing and supply chain analysis primarily for U.S. small hydropower (SHP) market to provide insights on supply chain constraints, manufacturing cost, location factors, and opportunities for hydropower using advanced manufacturing technologies. The goal of the project is to improve understanding of the manufacturing opportunities in the U.S. hydropower supply chain, specific competitive advantages, and factors for manufacturing location decisions for SHP. These insights could be used to inform investment and research strategies, policy, and other decisions that could promote economic growth and strengthen U.S. manufacturing capabilities. The project scope included a design for manufacturing and assembly (DFMA) costs analysis and a levelized cost of electricity (LCOE) sensitivity analysis of modular, representative SHP turbines. A representative SHP system was chosen to be made up of two 5 megawatt (MW) turbines, as these Kaplan style turbines could be useable for many non-powered dams (NPDs) across the U.S. The sensitivity analysis used the Integrated Design and Assessment (IDEA) tool, which has helped highlight the impact of the turbine equipment cost on the potential capital expenditure (CAPEX) and LCOE of the installed, representative system. In addition, the project team analyzed international tradeflows (e.g., export and import of hydropower turbines and the parts used in hydropower systems). As part of this project, hydropower stakeholder interviews were undertaken to help identify insights related to hydropower manufacturing opportunities for U.S. players in SHP, the country-specific manufacturing advantages in the United States and threats, and key factors behind manufacturing location decisions made by the SHP industry. Key opportunities for the US SHP market and industry include: Foreign export markets such as Chile, China, Columbia, India and Japan, have an estimated 71,795 MW of technical potential that could be serviced with SHP installations. Utilization of existing manufacturing capacity and expertise, along with economies of scale benefits, could result in increased global competitiveness through lower investment requirements and component costs, which could lead to increased U.S. strength in the export of SHP systems and components. Technology advances such as modular turbine designs, standardized units for conduit systems, precast systems, and improved powertrain technologies could help reduce costs, particularly for manufacturing a small number of units. The DOE is also exploring modular hydropower designs, which would integrate standard independently-produced components, validated to meet multiple specifications. Using modules in product design simplifies manufacturing activities such as inspection, testing, assembly, and purchasing, may increase economies of scale to lower total system cost. There are significant opportunities for the use of composites and Additive Manufacturing (AM) for hydropower components, when design changes are made to hydropower systems. By taking advantage of the corrosion resistance, light-weighting and structural benefits, composites and AM could help significantly alter hydropower structures and installations.

13 HYDRO ENERGY↗
DOE OSTI2021

Sequential Learning of Active Subspaces

This study shows that in recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Establishing Cost-Effective Allocation of Proton Therapy for Breast Irradiation

Purpose: Cardiac toxicity due to conventional breast radiation therapy (RT) has been extensively reported, and it affects both the life expectancy and quality of life of affected women. Given the favorable oncologic outcomes in most women irradiated for breast cancer, it is increasingly paramount to minimize treatment side effects and improve survivorship for these patients. Proton RT offers promise in limiting heart dose, but the modality is costly and access is limited. Using cost-effectiveness analysis, we provide a decision-making tool to help determine which breast cancer patients may benefit from proton RT referral. Methods and Materials: A Markov cohort model was constructed to compare the cost-effectiveness of proton versus photon RT for breast cancer management. The model was analyzed for different strata of women based on age (40 years, 50 years, and 60 years) and the presence or lack of cardiac risk factors (CRFs). Model entrants could have 1 of 3 health states: healthy, alive with coronary heart disease (CHD), or dead. Base-case analysis assumed CHD was managed medically. No difference in tumor control was assumed between arms. Probabilistic sensitivity analysis was performed to test model robustness and the influence of including catheterization as a downstream possibility within the health state of CHD. Results: Proton RT was not cost-effective in women without CRFs or a mean heart dose (MHD) <5 Gy. Base-case analysis noted cost-effectiveness for proton RT in women with ≥1 CRF at an approximate minimum MHD of 6 Gy with a willingness-to-pay threshold of $100,000/quality-adjusted life-year. For women with ≥1 CRF, probabilistic sensitivity analysis noted the preference of proton RT for an MHD ≥5 Gy with a similar willingness-to-pay threshold. Conclusions: Despite the cost of treatment, scenarios do exist whereby proton therapy is cost-effective. Referral for proton therapy may be cost-effective for patients with ≥1 CRF in cases for which photon plans are unable to achieve an MHD <5 Gy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources

Abstract. Here, computer models are ubiquitous tools used to represent systems across many scientific and engineering domains. For any given system, many computer models exist, each built on different assumptions and demonstrating variability in the ways in which these systems can be represented. This variability is known as epistemic uncertainty, i.e. uncertainty in our knowledge of how these systems operate. Two primary sources of epistemic uncertainty are (1) uncertain parameter values and (2) uncertain mathematical representations of the processes that comprise the system. Many formal methods exist to analyse parameter-based epistemic uncertainty, while process-representation-based epistemic uncertainty is often analysed post hoc, incompletely, informally, or is ignored. In this model description paper we present the multi-assumption architecture and testbed (MAAT v1.0) designed to formally and completely analyse process-representation-based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation), allowing for the generation and running of large model ensembles that vary in process representation, parameters, parameter values, and environmental conditions during a single execution of the code. MAAT v1.0 approaches epistemic uncertainty through sensitivity analysis, assigning variability in model output to processes (process representation and parameters) or to individual parameters. In this model description paper we describe MAAT and, by using a simple groundwater model example, verify that the sensitivity analysis algorithms have been correctly implemented. The main system model currently coded in MAAT is a unified, leaf-scale enzyme kinetic model of C 3 photosynthesis. In the Appendix we describe the photosynthesis model and the unification of multiple representations of photosynthetic processes. The numerical solution to leaf-scale photosynthesis is verified and examples of process variability in temperature response functions are provided. For rapid application to new systems, the MAAT algorithms for efficient variation of model structure and sensitivity analysis are agnostic of the specific system model employed. Therefore MAAT provides a tool for the development of novel or toy models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.

54 ENVIRONMENTAL SCIENCES↗