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At least 865 records · Page 48

Initial Efforts Organizing WPNCS SG-8: Preservation of Expert Knowledge and Judgement Applied to Criticality Benchmarks

The Working Party on Nuclear Criticality Safety (WPNCS) under the guidance of the Organization for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) has over 20 years of experience addressing concerns related to static and transient configurations encountered within the nuclear fuel cycle: fuel fabrication, transportation, reprocessing, storage, and geological disposal. One of the cornerstone activities of the WPNCS is the International Criticality Safety Benchmark Evaluation Project (ICSBEP), which was established to identify a comprehensive set of criticality benchmark data, evaluate the data, including quantification of overall uncertainties; compile the data into a standardized format, perform sample calculations utilizing modern nuclear data sets and codes utilized in nuclear criticality safety, and formally document the work into a single source of verified benchmark data. Annually, members of the ICSBEP Technical Review Group (TRG) contribute evaluated benchmark data that undergoes comprehensive technical review prior to publication in the ICSBEP Handbook. In the years since the ICSBEP was established, there has been much work to prepare benchmark data to support validation activities in nuclear criticality safety. The 2020 edition of the ICSBEP Handbook contains acceptable benchmark specifications for 5,053 critical, subcritical, or near-critical configurations in 582 benchmark evaluations. Modern benchmark development benefits from decades of experienced international participants, a well-established handbook format, supplementary guides to deal with uncertainty quantification, and a comprehensive review process based upon independent reviews from international experts. The ICSBEP Handbook also contains 838 configurations deemed unacceptable to support criticality safety efforts. They are recorded, with the reasoning for their rejection, to preserve the experimental data, prevent reevaluation of data that are incomplete or contain known errors, and/or to potentially allow future reevaluation of the experiment pending the identification of sufficient data to resolve identified inconsistencies and errors. Users of the ICSBEP Handbook today might notice that the rigor and quality of modern criticality safety benchmarks is much greater than those prepared within the initial decade of the project. Benchmarks with 1s uncertainties in k eff greater than 1% were traditionally rejected unless they were identified as unique experiment types that encompassed materials, fuels, or designs not available from other benchmark experiments. However, benchmarks developed using modern experimental techniques and practices typically have uncertainties on the order of a few tenths of a percent. There have been ongoing efforts to improve the overall quality of previously published benchmark evaluations. Seventy-eight evaluations, containing approximately 600 configurations, have been revised just within the past decade. An additional eleven benchmarks are under revision for updated release in the 2020 edition of the ICSBEP Handbook. If some of the historic benchmarks were resubmitted in their current form to the TRG today, they would be rejected due to lack of data, missing components in the uncertainty analysis, or incomplete benchmark model development. The use of historic criticality safety benchmarks that underestimate the total uncertainty, lack properly quantified biases, or provide inadequate benchmark specifications do not sufficiently support modern criticality safety and nuclear data efforts. Although the ICSBEP Handbook is recognized by regulating bodies to support criticality safety, users are required to justify their reasons to ignore historic benchmark data and include additional safety margins within their designs. Discussions were held at the WPNCS 23rd Annual Meeting in September 2019 regarding the aforementioned issues. The resultant decision was to establish Subgroup 8 (SG-8): Preservation of Expert Knowledge and Judgement Applied to Criticality Benchmarks. The current activities of SG-8 are discussed herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Structural Uncertainty in the Sensitivity of Urban Temperatures to Anthropogenic Heat Flux

One key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof-air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2-m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2-m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

54 ENVIRONMENTAL SCIENCES↗

Multiresolutional models of uncertainty generation and reduction

Kolmogorov's axiomatic principles of the probability theory, are reconsidered in the scope of their applicability to the processes of knowledge acquisition and interpretation. The model of uncertainty generation is modified in order to reflect the reality of engineering problems, particularly in the area of intelligent control. This model implies algorithms of learning which are organized in three groups which reflect the degree of conceptualization of the knowledge the system is dealing with. It is essential that these algorithms are motivated by and consistent with the multiresolutional model of knowledge representation which is reflected in the structure of models and the algorithms of learning.

Meystel, A.↗

Uncertainty quantification for competing failure mechanisms in unidirectionally reinforced carbon–carbon composites

Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.

36 MATERIALS SCIENCE↗

Informing Geologic CO 2 Storage Site Management Decisions under Uncertainty: Demonstration of NRAP's Integrated Assessment Model (NRAP-IAM-CS) Application

Managing risks at geologic CO 2 storage sites requires making decisions based on the quantitative predictions of long-term performance that not only take into account various subsurface hydrologic, geochemical processes and interactions but also the related uncertainties. The US DOE (United States Department of Energy) funded National Risk Assessment Partnership (NRAP) has developed a quantitative approach and a model to predict the long-term behavior of GCS sites. The approach uses an integrated assessment model (NRAP-IAM-CS) that couples reduced order models (ROMs) for various GCS components (such as storage reservoir, wells, seals, and groundwater aquifer) in a stochastic modeling framework that allows for rapid simulations of entire system behavior over thousands of years while probing uncertainties. We demonstrate application of NRAP-IAM-CS using two examples that focus on CO 2 leakage and resulting impacts. The examples are used to demonstrate how different uncertain parameters affect overall performance objectives.

03 NATURAL GAS↗

Laboratory Studies of Chemical and Photochemical Processes Relevant to Stratospheric Ozone

This is the final report for this project. Its purpose is to reduce the uncertainty in rate coefficients for key gas-phase kinetic processes which impact our understanding of stratospheric ozone. The main emphasis of this work is on measuring the rate coefficients for the reactions of HO2 + O3, and HO2 + NO2 in the temperature range (200-240 K) relevant to the lower stratosphere. In order to accomplish this, a high pressure turbulent flow tube reactor was built and its flow characteristics were quantified. The instrument was coupled with tunable diode laser spectroscopy for HO2 detection. Room temperature measurements of the HO2 + NO2 rate coefficients over the pressure range of 50-300 torr agree well with previous measurements. Preliminary measurements of the HO2 + O, rate coefficients at 50 - 300 Torr over the temperature range of 208-294 K agree with the NASA evaluation from 294-225 K but deviate significantly (50 % higher) at approximately 210 K.

Villalta, P. W.↗

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Motility and centrosomal organization during sea urchin and mouse fertilization

It is noted that microfilaments are essential for incorporation of sperm in sea urchins and for pronuclear apposition in mice. The ability of sea urchin sperm to fertilize eggs is lowered by latrunculin, giving evidence that acrosomal microfilaments are of importance to the process of fertilization. Due to the uncertainty regarding the presence of microfilaments in various mammalian sperm, it is interesting that latrunculin does not noticeably affect the ability of mouse sperm to fertilize oocytes. The movements of the sperm and egg nuclei at the time of sea urchin fertilization are dependent on microtubules arranged into a radial monastral array (the sperm aster). In the mouse egg, microtubule activity is also required during pronuclear apposition, but they are arranged by a number of egg cytoplasmic sites. Results of the investigations show that both microtubules and microfilaments are necessary for the successful completion of fertilization in both mice and sea urchins, but at different stages. Also, it is demonstrated that centrosomes are contributed by the sperm in the process of sea urchin fertilization, but in mammals they may be inherited maternally.

Schatten, Heide↗

Need for Uncertainty Quantification in Predictions [Slides]

Natural and engineered systems are often modeled through mathematical equations using computer codes. Finite resources, inability to accurately control experimental conditions, encounter, or explore the entire parameter space of interest are a few reasons that scientists and engineers rely on numerical simulations for predictive capability. Associated with every prediction is an uncertainty, since our ability to simulate a physical process is limited by our knowledge of the physics, and the parameters. In the last two decades, the need for Uncertainty Quantification (UQ) has been recognized in several fields and we often ask questions about the risk or uncertainty associated with events and make decisions accordingly. In this talk, I will illustrate the effects of model and parametric uncertainties and the need to include them in our predictive capabilities.

97 MATHEMATICS AND COMPUTING↗

Survey of DAKOTA's V&V Capabilities in the Simulation of Residual Stresses in a Simple Composite Structure

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses can result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, a process modeling methodology was developed and implemented into Sandia National Laboratories' SIERRA/SolidMechanics code. This method can be used to predict the process-induced stresses in any composite structure, regardless of material composition or geometric complexity. However, to develop confidence in these predictions, they must be rigorously validated. Specifically, sensitivity studies should be completed to define which model parameters are critical to the residual stress predictions. Then, the uncertainty associated with those critical parameters should be quantified and processed through the model to develop stress-state predictions encompassing the most important sources of physical variability. Numerous sensitivity analysis and uncertainty quantification methods exist, each offering specific strengths and weaknesses. Therefore, the objective of this study is to compare the performance of several accepted sensitivity analysis and uncertainty quantification methods during the manufacturing process simulation of a composite structure. The examined methods include simple sampling techniques as well as more sophisticated surrogate approaches. The computational costs are assessed for each of the examined methods, and the results of the study indicate that the surrogate approaches are the most computationally efficient validation methods and are ideal for future residual stress investigations.

36 MATERIALS SCIENCE↗

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Abstract Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

54 ENVIRONMENTAL SCIENCES↗

On the Causes of Mid-Pliocene Warmth and Polar Amplification

The mid-Pliocene (approximately 3 to 3.3 Ma ago), is a period of sustained global warmth in comparison to the late Quaternary (0 to approximately 1 Ma ago), and has potential to inform predictions of long-term future climate change. However, given that several processes potentially contributed, relatively little is understood about the reasons for the observed warmth, or the associated polar amplification. Here, using a modelling approach and a novel factorisation method, we assess the relative contributions to mid-Pliocene warmth from: elevated CO2, lowered orography, and vegetation and ice sheet changes. The results show that on a global scale, the largest contributor to mid-Pliocene warmth is elevated CO2. However, in terms of polar amplification, changes to ice sheets contribute significantly in the Southern Hemisphere, and orographic changes contribute significantly in the Northern Hemisphere. We also carry out an energy balance analysis which indicates that that on a global scale, surface albedo and atmospheric emmissivity changes dominate over cloud changes. We investigate the sensitivity of our results to uncertainties in the prescribed CO2 and orographic changes, to derive uncertainty ranges for the various contributing processes.

Lunt, Daniel J.↗

Navigation for the ACS3 Solar Sail Mission

NASA’s Advanced Composite Solar Sail System (ACS3) mission consists of a spacecraft that plans to be launched in early 2024. The spacecraft carries an 80 m2 solar sail that can produce effective thrust to alter the initial 1000 km sun-synchronous orbit. The main objective of the mission is to demonstrate the capabilities of the solar sail to effectively change various orbital parameters such as the semi-major axis and the inclination. Various composite materials were used to produce the sail, together with lightweight booms that will deploy from a 12U CubeSat bus. The astrodynamics team at NASA Ames Research Center has built a Flight Dynamics System (FDS) to provide mission navigation and to produce regular ephemeris once in orbit. The FDS can compute the trajectories that the spacecraft will perform once the sail is deployed. To achieve that, GPS data is obtained from the spacecraft telemetry and then is used with a Kalman filter and a smoother to obtain an orbit determination solution. The outcome of this process reduces the position and velocity uncertainty in a daily cadence. After that, the state vector output is used to feed a propagation model that includes the updated attitude and orbit of the spacecraft at that given moment. The trajectory model considers the updated attitude plan of the spacecraft as well as the environment conditions such as the solar weather to compute the associated drag and solar radiation pressure. This paper explains in detail the implementation of the FDS, as well as the solar sail solar radiation pressure trajectory model. We also present the results of several potential trajectory models under various assumptions of orbit parameters, attitude, environment, and material properties. In addition, we introduce a trajectory model for potential interplanetary use of an equivalent solar sail in the future. The outcome of this process will be critical to achieving the objective of determining effective altitude change produced by the solar sail.

Andres Dono Perez↗

Navigation for the ACS3 Solar Sail Mission

NASA’s Advanced Composite Solar Sail System (ACS3) mission consists of a spacecraft that plans to be launched no earlier than April 2024. The spacecraft carries an 81 m2 solar sail that can produce effective ΔV to alter the initial 1000 km sun-synchronous orbit. The main objective of the mission is to demonstrate the capabilities of the solar sail to effectively change the semi-major axis of its initial orbit. Various composite materials were used to produce the sail, together with lightweight booms that will deploy from a 12U CubeSat bus. The ACS3 navigation team at NASA Ames Research Center has built a Flight Dynamics System (FDS) to provide mission navigation and to produce regular ephemeris once in orbit. The FDS can compute the orbit transfers that the spacecraft will perform once the sail is deployed. To achieve that, GPS data is obtained from the spacecraft telemetry and then is used with a Kalman filter and a smoother to obtain an orbit determination solution. The outcome of this process reduces the position and velocity uncertainty in a daily cadence. After that, the state vector output is used to feed a propagation model that includes the updated attitude and orbit of the spacecraft at that given moment. The trajectory model considers the updated attitude plan of the spacecraft as well as the environment conditions such as the solar weather to compute the associated drag and solar radiation pressure. This paper explains in detail the implementation of the FDS, as well as the solar sail solar radiation pressure trajectory model. We also present the results of several potential trajectory models under various assumptions of orbit parameters, attitude, environment, and material properties. In addition, we introduce a trajectory model for potential interplanetary use of an equivalent solar sail in the future. The outcome of this process will be critical to achieving the objective of determining effective semi-major axis change produced by the solar sail.

Andres Dono↗

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. In conclusion, improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.

54 ENVIRONMENTAL SCIENCES↗

Robust data-driven uncertainty quantification in water cycle extreme predictions

Climate experts apply global climate models to predict the future climate, informing decisions and adaptations. Uncertainty plays a fundamental role in this process (particularly for the water cycle simulation, the most relevant but least well-simulated). For many aspects of the water cycle there are disagreements for future climate projections, reflecting the significant uncertainty in model formulation. Also, the chaotic nature of climate dynamics, as well as indeterministic future projection emission scenarios, contribute to the uncertainty in climate predictions. New AI methods that are beginning to be adopted by the climate community have the power to improve predictions of water cycle extremes. The crucial question is how to capture all sources of uncertainty in a systematic way in order for the predictions from the AI models to be maximally leveraged by decision makers

54 ENVIRONMENTAL SCIENCES↗

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗

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

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

54 ENVIRONMENTAL SCIENCES↗