ENSA Cask Multimodal Transportation Test and Related Structural Modeling and Analysis.
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The U.S. Department of Energy Office of Nuclear Energy (DOE-NE), Office of Fuel Cycle Technology, has established the Used Fuel Disposition Campaign (UFDC) to conduct research and development activities related to storage, transportation, and disposal of used nuclear fuel (UNF) and high-level radioactive waste (HLW). The mission of the UFDC is to identify alternatives and conduct scientific research and technology development to enable storage, transportation and disposal of used nuclear fuel and HLW generated by existing and future nuclear fuel cycles. The Storage and Transportation staff within the UFDC is responsible for addressing issues regarding the long-term or extended storage (ES) of UNF and its subsequent transportation.
Structured model using loss functions to assess risk of using given decontamination procedure on all Martian unmanned landing craft
Fluvial floods drive severe risk to riverine communities. There is strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters, and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on the Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.
Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e . using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent ( e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox ( CCTBX ) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.
Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors’ impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20%25 in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.
This report summarizes efforts performed during Fiscal Year 2022 to develop capabilities for modeling structural component degradation in support of the U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation Program. These efforts were centered around development of capabilities for the Grizzly code. Efforts focused both on foundational engineering-scale analysis capabilities for damage and shell formulations and on material-scale tools for accounting for irradiation effects in Grade 91 steel. A major outcome of this effort was the development of a general-purpose tool for nonlocal averaging of material properties, which enabled nonlocal damage models. In addition, the applicability of the shell elements in MOOSE has been expanded to allow modeling a wider range of component geometries and using a wide variety of material models. For irradiation effects on materials, a previously developed cluster dynamics model for light water reactor pressure vessel steels has been adapted for application to Grade 91 alloy. This work builds on prior efforts to build a flexible, capable code for addressing a variety of aging and component performance issues in nuclear power plant structural components.
Structural health monitoring (SHM) has the capacity to reduce failure by detecting damage during service life, by periodic, automated monitoring. Guided Wave (GW) Ultrasound is a common SHM approach for aerospace structures. Modelling the physics of GW SHM systems provides a route for understanding system dependencies, capabilities and limitations as damage evolves during service life. Such a toolset can strengthen the understanding of the connection between GW SHM results and the true material state. The most useful modelling tools are those that provide versatile solutions with respect to the simulated component geometry and computational grid connectivity. This work details a versatile application programming interface (API) for the elastodynamic finite integration technique for modelling GW SHM of metals. The custom code implementation, EFIT-CompCell, allows for the modelling of diverse geometries by automatically balancing the message passing interface parallelization layout. The user provides the basic parameters of the simulation and the software automatically performs an initial balancing based on anticipated computational loads, and establishes the CPU communication patterns for any geometry. This work describes the programming philosophy and code structure used to create EFIT-CompCell and compares its performance and capacity to simulation tools that are more specialized for specific architectures. Results are presented for a simulation of GW SHM of an aluminum fuselage section being tested by the FAA. The simulation consists of 733M voxels which took approximately 70 hours to complete 25000 time steps using 40 Intel Xeon E5-4650v2 Ivy Bridge processor cores.
In the track structure model, the inactivation cross section is found by summing an inactivation probability over all impact parameters from the ion to the sensitive sites within the cell nucleus. The inactivation probability is evaluated by using the dose response of the system to gamma rays and the radial dose of the ions and may be equal to unity at small impact parameters. We apply the track structure model to recent data with heavy ion beams irradiating biological samples of E. Coli, B. Subtilis spores, and Chinese hamster (V79) cells. Heavy ions have observed cross sections for inactivation that approach and sometimes exceed the geometric size of the cell nucleus. We show how the effects of inactivation may be taken into account in the evaluation of the mutation cross sections in the track structure model through correlation of sites for gene mutation and cell inactivation. The model is fit to available data for HPRT (hypoxanthine guanine phosphoribosyl transferase) mutations in V79 cells, and good agreement is found. Calculations show the high probability for mutation by relativistic ions due to the radial extension of ions track from delta rays. The effects of inactivation on mutation rates make it very unlikely that a single parameter such as LET (linear energy transfer) can be used to specify radiation quality for heavy ion bombardment.
The γZ-box is an important contribution to the proton's weak charge. The γZ-box is calculated dispersively and depends on γZ-structure functions, F{sub 1,2,3}{sup γZ}(x,Q{sup 2}). At present there is no data for these structure functions and they must be modeled by modifying existing fits to electromagnetic data. Each group that has studied the γZ-box used different modifications. The results of the PVDIS experiment at Jefferson Lab may provide a first test of the validity of each group's models. I present details of the different models and their predictions for the PVDIS result.
NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a “sample and select” method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls ( 1 H, 15 N, and 13 C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.
Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land-atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e. model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.
A finite element analysis emulator providing a basis for efficiently establishing an optimum computer implementation strategy when many calculations are involved is described. The SCOPE emulator determines computer resources required as a function of the structural model, structural load-deflection equation characteristics, the storage allocation plan, and computer hardware capabilities. Thereby, it provides data for trading analysis implementation options to arrive at a best strategy. The models contained in SCOPE lead to micro-operation computer counts of each finite element operation as well as overall computer resource cost estimates. Application of SCOPE to the Memphis-Arkansas bridge analysis provides measures of the accuracy of resource assessments. Data indicate that predictions are within 17.3 percent for calculation times and within 3.2 percent for peripheral storage resources for the ELAS code.
An MSC.Dytran model of an all-composite Lear Fan aircraft fuselage was developed to simulate an impact test conducted at the NASA Langley Research Center Impact Dynamics Research Facility (IDRF). The test was the second of two Lear Fan impact tests. The purpose of the second test was to evaluate the performance of retrofitted composite energy-absorbing floor beams. A computerized photogrammetric survey was performed to provide airframe geometric coordinates, and over 5000 points were processed and imported into MSC.Patran via an IGES file. MSC.Patran was then used to develop the curves and surfaces and to mesh the finite element model. A model of the energy-absorbing floor beams was developed separately and then integrated into the Lear Fan model. Structural responses of components such as the wings were compared with experimental data or previously published analytical data wherever possible. Comparisons with experimental results were used to guide structural model modifications to improve the simulation performance. This process was based largely on qualitative (video and still camera images and post-test inspections) rather than quantitative results due to the relatively few accelerometers attached to the structure.
The man machine interface between the human driver and his automobile during car following tasks was examined. A direct connection between models of the human operator and the theory of optimal control was established for car following situations involving both linear and nonlinear car dynamics. Results obtained from several types of mathematical models are discussed. Conventional parameter identification techniques were used to obtain the parameters of these models by comparing their performance to data obtained by aerial photogrammetry. The results indicate that there are two general classes of model structures: models assumed to have preview information and models without preview.
A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.
A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.