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At least 397 records · Page 22

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NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRS2021

Self-Consistent Implementation of a Zero-Equation Transport Model Into a Predictive Model for a Hall Effect Thruster

The performance of an axisymmetric multi-fluid Hall thruster code that incorporates a self-consistent, data-driven closure model for the anomalous electron transport is investigated. Five different operating conditions of the H9 magnetically shielded Hall thruster are simulated with the Jet Propulsion Laboratory’s Hall2De. In order to capture the inherent uncertainty associated with the closure model, O(100) simulations are run for each condition, each of using a coefficient set sampled randomly from a probability distribution. The results of these simulations provide probabilistic predictions of thruster performance quantities including thrust, and discharge current, as well as several component efficiencies and centerline plasma properties. The model is found to yield converged solutions at all conditions, with large 10 kHzrange oscillations and performance trends with voltage and flow rate similar to experiment. The model under-predicts the thrust by 15-25% and over-predicts the discharge current by 20% on average compared to experiments at the same discharge voltage and mass flow rate. This performance discrepancy is due to lower beam utilization, mass utilization, and divergence efficiency than experiment, resulting from high Hall parameters in the acceleration region, which lead to a protracted ion acceleration region. The physical processes underlying this result are discussed in the context of future data-driven modeling efforts.

Jorns, Benjamin A.↗

DOE OSTI2017

Quality Assurance on a Custom SiPMs Array for the Mu2e Experiment

The Mu2e experiment at Fermilab will search for the coherent $\mu \to e$ conversion on aluminum atoms. The detector system consists of a straw tube tracker and a crystal calorimeter. A pre-production of 150 Silicon Photomultiplier arrays for the Mu2e calorimeter has been procured. A detailed quality assur- ance has been carried out on each SiPM for the determination of its own operation voltage, gain, dark current and PDE. The measurement of the mean-time-to-failure for a small random sample of the pro-production group has been also completed as well as the determination of the dark current increase as a function of the ioninizing and non-ioninizing dose.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

SCALE/Sampler Sensitivity Indices

The Sampler sequence of the SCALE code system for random sampling–based uncertainty analyses has recently been extended by including a sensitivity analysis. In uncertainty analyses with respect to uncertainties in the nuclear data, the determination of two multiple correlation coefficients—R2 and SPC2—allows for the identification of nuclide reactions that are significantly contributing to the investigated output uncertainty. Individual nuclide reactions are ranked by importance, allowing suggestions for additional measurements and evaluations to significantly reduce the observed output uncertainty. A brief description of the theory is provided herein, followed by application of this analysis to a sodium-cooled fast reactor pin cell.

Bostelmann, Rike↗

DOE OSTI2019

ASSESSMENT OF NORMALITY FOR CRITICALITY SAFETY BIAS AND BIAS UNCERTAINTY CALCULATION

The single-sided lower tolerance factors frequently used for non-trending assessment of the validation bias and bias uncertainty are sensitive to departures from normality. When used properly, the tolerance limits ensure that an appropriate fraction of the true population of applicable critical experiments lies above the calculated lower tolerance limit with the required statistical confidence level. One condition necessary to ensure that the appropriate proportion of the true population of keff values in the validation suite lies above the lower tolerance limit is that the assumption that the normality of the underlying population of critical experiments is valid or conservative.This paper discusses various methods used to assess whether the assumption that the validation suite may be treated as a random sample drawn from a normal distribution is acceptable. Techniques for assessing the validity of this underlying assumption include common omnibus hypothesis tests for normality, assessment of sample skewness and kurtosis of the validation suite, and graphical techniques. These techniques are used to assess the nature and potential conservatism/nonconservatism imparted by various departures from normality where possible. A review of hypothesis testing is also presented to frame the discussion of omnibus normality tests. Additionally, two cases are analysed with these techniques to provide an example of how they should be implemented.

Clarity, Justin↗

DOE OSTI2019

Modeling Building Use and Population Distribution Opportunity Using Open Geosocial Data in Urban Areas

Knowing where people are and knowing how the land in urban areas is being used is critical for assessing human mobility patterns, monitoring population health, planning for transportation services, and responding to natural disasters and emergency situations. While geophysical phenomena like land cover are more easily observed through remote sensing, social phenomena like how a city block or building is being used by the population is generally unobservable through standard remote sensing practices. Current methods and land use data by their nature are also difficult to scale to national or global levels. While land use data are often available in large cities, there are abundant data gaps globally. Furthermore, land use data can often appear relatively coarse in its spatial and semantic resolution (i.e. 1km grid cells rather than building level, and "commercial" rather than "coffee shop"). In particular, there is a need to harvest scalable data and develop methods to create global land use maps. To this end, in this work we combine building footprints and Points of Interest (POI) to estimate land use at the building level, including its mixed-use intensity. Next, we propose a method for defining semantic temporal zones referring to Morning, Day, Evening, and Night times unique to a region, based on the cumulative hourly change in the POIs' hours of operation. We then use these POIs' hours of operation to estimate non-residential population distribution opportunity and land use change over a 24-hour period. We validate our results through manual checks of randomly sampled individual buildings using multiple sources. Finally, we discuss the impact of our results on identifying population distribution opportunity and the reliability of estimating building use.

Sparks, Kevin↗

DOE OSTI2019

Summary Report on Synthetic Time History Development and Deployment

This report summarizes research work performed at the Colorado School of Mines (CSM) and George Washington University (GWU) in support of the DOE Office of Nuclear Energy Integrated Energy Systems (IES) program. In particular, the work focuses on improving and strengthening two fundamental steps in the mathematical and computational framework that is currently being developed to optimize the economic performance of Integrated Energy Systems. One of the innovative approaches taken by the IES program is that, rather than optimizing the system for a specific year or week or day, the optimization is performed using stochastic synthetic data. Synthetic data is generated by training artificial intelligence (AI) using available but limited data sets. The synthetic data should possess the same patterns as the original set and the same statistical properties, but never repeat the same set of values exactly. The work from CSM focuses on this first step; namely, investigating a methodology to determine the quality of the synthetic data and the right settings for properly training the AI. Once a large set of trustworthy data can be generated the problem of stochastic optimization can be tackled. The optimal IES configuration is highly dependent on the rare high peaks or deep depths of the net demand (grid electricity demand less variable renewable energy [VRE] production). Using an unbiased random sampling of the synthetic data, these extremes are very difficult to generate and therefore the optimization can be unrepresentative and inaccurate. GWU has started an approach to increase the likelihood of generating these rare events with the proper associated probabilistic weight. Both of these projects represent a step forward in achieving the capability to properly size the IESs and to properly capture the value that these systems can provide to the grid by absorbing volatility of net demand without disproportionately increasing the cost of maintaining grid reliability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

State of Stress Uncertainty Quantification and Geomechanical Risk Analysis for Subsurface Engineering

All subsurface stress measurement methods available for deep subsurface applications are indirect and involve a significant amount of uncertainty. Quantifying these uncertainties is critical for applications where the state of stress is a component of a risk analysis with significant safety, environmental, and economic considerations. A publicly available and open source tool called the State of Stress Analysis Tool (SOSAT) has been developed to aid in performing a Bayesian uncertainty quantification and geomechanical risk analysis using many types of commonly available data. A new feature of this tool is using data from the FutureGen 2.0 site. The new feature of the tool was uses information about the absence or presence of borehole breakouts to constrain the state of stress. This new feature allows the user to specify probability distributions for the relevant parameters such as the drilling mud pressure, temperature, and the formation strength properties. The tool computes the posterior joint probability distribution of the horizontal principal stresses. Using random samples from this posterior distribution the tool then computes the probability of activating a critically oriented fault at a range of pore pressures. This latter feature can be used to aid in the assessment of the risk of induced seismicity and leakage in geologic carbon sequestration and enhanced geothermal systems.

Burghardt, Jeffrey A.↗

DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2023

Beyond Expected Values Evolving Metrics for Resource Adequacy Assessment

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗

DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗

DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗

DOE OSTI2016

Statistical and sensitivity analysis of failing rods in EPR LBLOCA

A statistical fuel failure analysis procedure has been developed, and is now applied in full scale to a large break loss-of-coolant accident (LBLOCA) scenario in EPR-type nuclear power plant. The goal is to statistically determine the number of failing fuel rods. It is also important to bring out the underlying causes for rod failures, and therefore a sensitivity analysis procedure for the preceding complex calculation chain is outlined and adopted. In order to produce a statistically reliable estimation on the number of failing rods, a large number of simulations are required. In the analysis, single rods are simulated with the coupled fuel performance - thermal hydraulics code FRAPTRAN-GENFLO. The statistically varied factors are divided into global and local by their range of influence. Transient thermal hydraulic and power history boundary conditions and model parameters are global and affect all the rods. Fuel rod manufacturing parameters and other rod position related factors are local and are related to a certain rod. The number of global scenarios is 59 as dictated by the Wilks' formula, and 1000 randomly sampled rods are simulated in each scenario. The thermal hydraulic and transient power boundary conditions for the fuel performance code are calculated with a statistical version of the system code APROS. The steady-state irradiation histories are simulated with FRAPCON, and the steady-state power histories used as boundary conditions for FRAPCON are obtained from SIMULATE calculations. Thus, a multistage calculation chain is required to consolidate the procedure. As an outcome, in the worst global scenario, 1.4% of the simulated rods failed. It can be concluded that the Finnish safety regulations, i.e. max. 10% of the rods allowed to fail, are met. In order to find out which input parameters have significance to the outcome of the analysis, a sensitivity analysis is done. At the moment, only the worst global scenario is considered but later on it is possible to include also the sensitivities of the global factors. Due to complexity of the existing data, first the relevant input parameters for the sensitivity analysis have to be specified. Data visualization with a cobweb graph is used for the screening. Then, selected sensitivity measures are calculated between the chosen input and output parameters. The sensitivity indices calculated are the Borgonovo's delta measure, the first order Sobol sensitivity index, and squared Pearson correlation coefficients. The first mentioned is a novelty in this context. As an outcome, the most relevant parameters with respect to the cladding integrity were determined to be the decay heat power during the transient, the thermal hydraulic conditions in the rod's location in the reactor, and the steady-state irradiation history of the rod as represented in this analysis by the rod burnup. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Uncertainty evaluation for the realistic safety analysis methodology of RIA by utilizing experimental results

An audit calculation methodology for the safety analysis of reactivity-initiated accident (RIA) in a PWR based on the realistic approach is under developing in Korean Institute of Nuclear Safety. In this paper important uncertainty parameters to the rod performance were described, and the validity of those parameters was assessed by utilizing the CABRI and NSRR experimental results. Sensitivity study has been carried out to identify the parameters by changing fuel burnup, injected energy and full-width half maximum (FWHM) of given RIA power pulses. FRAPCON-3.5 and FRAPTRAN-1.5 fuel performance code were utilized. Non-parametric order statistics was used for the uncertainty quantification, and 124 inputs on each experimental case were produced by simple random sampling technique. Analysis results showed that the assessed fuel performance uncertainties such as the fuel enthalpy, fission gas release and clad plastic hoop strain could not bound some experimental results. This means that the currently considered uncertainty range, particularly for the peak power, is not enough to cover the fuel performance uncertainty within a sufficient probability and confidence level. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Transportation of Low-Level Radioactive Waste from KAERI to the Final Repository - 18179

The Korea Atomic Energy Research Institute (KAERI) collects the radioactive wastes generated during the operation of nuclear facilities and R and D processes. Radioactive wastes are treated and repackaged according to the national radioactive waste management policy, and then transferred to the final disposal site at the Korea Radioactive Waste Agency (KORAD). After 2015, the wastes delivered to the final disposal site have consisted of spent filters and combustible wastes such as clothes and gloves generated during R and D or facility operation processes at KAERI. The wastes generated by each facility are collected at the radioactive waste treatment facility, then treated and repackaged in 200 L drums according to their generation characteristics, i.e. each drum contains waste of the same type from the same facility, and with the same date of generation. Then, a radionuclide inventory is obtained for the repackaged drum by using a direct measurement method such as the drum assay system, radiochemical analysis through random sampling or an indirect measurement method such as applying a scaling factor. When performing sampling of the radioactive waste drums, the waste samples with a high contamination are selected in a conservative approach as representative. The radioactive waste drums are inspected and checked for acceptability by the Quality Management Division of KAERI, and a preliminary inspection is carried out on-site by KORAD. At this time, the acceptability of the radioactive waste drums is assessed through visual inspection, the appropriateness of their nuclide analysis value is confirmed, and a survey or smear test of sample drums is performed. Drums that meet the acceptance criteria are transferred to KORAD after a radiation environmental impact assessment of the transportation. Since 2015, 1,084 low-level operational radioactive wastes have been transferred from KAERI to the final disposal site, and further preparation work is now being made for the transportation of additional drums. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

A Solid of Revolution Time Study using COG11.1 and MCNP6.1

The performance of two continuous energy Monte Carlo transport codes, COG11.1 and MCNP6.1, has been compared for the {sup 239}Pu Jezebel Benchmark using cross sections based on the ENDF/B-VII.1 evaluated nuclear data library. The purpose of this study is to determine the length of time it takes each code to complete a series of criticality calculations, using two different methods to model a solid of revolution. A solid of revolution is essential in many fields of engineering and mathematics, and has been a standard feature in computer-aided design and manufacturing since the 1970's. In the field of criticality safety, a solid of revolution can be used to reproduce and simplify complex engineering drawings and systems that would otherwise require excessive use of geometric primitives and computational resources to achieve a comparable level of fidelity. There are two basic methods to model a solid of revolution: Method 1 (COG) specifies a set of x-y pairs or polar coordinates and rotate the curve about the x-axis. Method 2 (COG and MCNP) creates a set of end-to-end conical frustums and/or cylinders. Method 1 is only available in COG and can be implemented using one surface and sector specification. Method 2 can be implemented in both COG and MCNP. This time study was initially conceived as a result of criticality safety analysis performed for a complex system with a high degree of rotational symmetry. Each COG input deck typically took 1-2 days to run in serial mode. COG input decks were then converted to MCNP format and run on the same computer cluster. Instead of expected 1-2 day runtimes, it took MCNP6.1 more than one week to complete each run. Since MCNP typically runs faster than many codes, these lengthy runtimes were considered unusual. This study attempts to investigate the runtime discrepancy between the two codes. The methods and benchmark in this time study were selected to provide a consistent, generalized approach to modeling an arbitrary solid of revolution. While there are many different ways to model the {sup 239}Pu Jezebel Benchmark, the focus of this study is on investigating the practical difference between the two basic methods to model a surface of revolution. Throughout this study, the only code that maintains reasonably low runtimes is COG11.1 (Method 1). Based on the observed performance of each code and method, it is likely that the divergent runtimes are the result of tracking particles across planes that lie between conical frustums, as opposed to defining the object boundary as a single surface. Slight fluctuations in graphed results were also observed, due to random sampling and CPU loading. This was confirmed by re-running input decks and comparing average runtimes to a preliminary curve fit. From the perspective of a criticality safety analyst, the results of this study are expected to contribute towards understanding one of the practical trade-offs between codes and methods as they apply to real-world analysis. Ultimately, it's about choosing the right tool for the job.

97 MATHEMATICS AND COMPUTING↗