Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sensitivity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between skin sensitization and skin permeability has been found. • Structural rules for optimizing sensitization and penetration were established.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"

This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the American River Watershed. The Portage_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the Portage River Watershed. The src folder contains the source code of sensitivity analysis and plotting functions. The notebooks folder contains the Jupyter notebooks for performing sensitivity analysis and other post analyses.Global Sensitivity Analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process-based watershed models. We propose to use Mutual Information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI-based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Water Assessment Tool to no less than 20 parameters for each watershed. Our MI-based sensitivity analyses achieved convergence with about 300~500 realizations, a small fraction of the ensemble size (i.e., several thousand) required by the Sobol method. Nevertheless, MI yields similar sensitivity ranking compared to the Sobol method, especially for sensitive parameters. Our study thus sheds new light on the use of MI as an affordable GSA method for computationally intensive models such as the hyper-resolution, watershed hydrobiogeochemical models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Comparison of the Gamma-Ray Burst Sensitivity of Different Detectors

Gamma-ray burst detectors are sensitive at different energies, complicating the comparison of the burst populations that they detect. The instrument teams often report their detector sensitivities in their instruments' energy band. I propose that sensitivities be reported its the threshold peak photon flux FT over the 1-1000 keV energy band for a specific spectral shape. The primary spectral parameter is E(sub p), the energy of the maximum E(sup 2)N(sub E) proportional to upsilon f(sub upsilon). Thus F(sub T) vs. E(sub p). E(sub p) is a useful description of a detector's sensitivity. I find that Swift will be marginally more sensitive than BATSE for E(sub p) greater than 100 keV, but significantly more sensitive for E(sub p) less than 100 keV. Because of its small field-of-view and low energy sensitivity, the FREGATE on HETE-2 is surprisingly sensitive. Both the WFC on BeppoSAX and the WXM on HETE-2 are/were sensitive for low E(sub p). As expected, the GBM on GLAST will be less sensitive than BATSE, while EXIST will be significantly more sensitive than Swift. The BeppoSAX GRBM was less sensitive that the WFC, particularly at low E(sub p).

Band, David L.↗
DOE OSTI2021

Sensitivity Calculations for Systems with Polyethylene Reflector Materials Using CLUTCH

The SCALE 6.2.4 code package contains four sequences for calculating $k_{eff}$ sensitivity coefficients. Two of these sequences use deterministic transport solvers: a one-dimensional (1D) capability based on XSDRN, and a two-dimensional (2D) capability based on NEWT. These sequences are restricted to the multigroup (MG) treatment of neutron energy. The three-dimensional (3D) sequences use the KENO V.a or KENO-VI Monte Carlo transport codes and can be used to calculate sensitivity coefficients with either MG or continuous-energy (CE) transport. The 3D sensitivities are ultimately reported in an MG structure, regardless of the method used in the transport calculations. If desired, the sensitivity coefficients can be reported with very fine energy resolution from a CE calculation, but they are calculated only in the MG library structure in the MG mode. CE TSUNAMI methods are available in SCALE starting in SCALE version 6.2. Sensitivity coefficients were generated using the 3D sequences as part of the generation of the SCALE 6.2.2 Validation Report; difficulties encountered when using the CLUTCH method for thick, fissionable-material reflectors were discussed and investigated as documented in a previous paper. This paper discusses the difficulties encountered in generating accurate sensitivity coefficients using the CLUTCH technique for polyethylene reflectors for two fast spectrum benchmarks. Direct perturbation (DP) calculations were performed to confirm the accuracy of the total sensitivity coefficient for important isotopes with large sensitivities in the system. Discrepancies were detected for CLUTCH-calculated sensitivity coefficients in the reflector of a critical experiment with a radial polyethylene reflector. A simple polyethylene-reflected plutonium sphere was then used to further investigate the discrepancy. Calculations performed using the iterated fission probability (IFP) method generated accurate sensitivity coefficients in both cases. The results of this study emphasize the need to confirm CLUTCH sensitivity results with DP calculations. IFP calculations are generally less efficient but more reliable than CLUTCH calculations. Improvements to the CLUTCH methodology that retain the greater efficiency but address identified difficulties are therefore potentially useful to analysts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

High-fidelity Multidisciplinary Sensitivity Analysis and Design Optimization for Rotorcraft Applications

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. A sensitivity-enabled fluid dynamics solver and a nonlinear flexible multibody dynamics solver are coupled to predict aerodynamic loads and structural responses of helicopter rotor blades. A discretely consistent adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system is validated by conducting simulations for a benchmark rotorcraft model and comparing solutions with established analyses and experimental data. Sensitivities of lift computed by the multidisciplinary sensitivity analysis are verified by comparison with the sensitivities obtained by complex-variable simulations. Finally the multidisciplinary sensitivity analysis is applied to a constrained gradient-based design optimization for a HART-II rotorcraft configuration.

Wang, Li↗
NASA NTRS2022

Computing Sensitivities in Evolutionary Systems: A Real-time Reduced Order Modeling Strategy

We present a new methodology for computing sensitivities in evolutionary systems using a model-driven low-rank approximation. To this end, we formulate a variational principle that seeks to minimize the distance between the time derivative of the reduced approximation and sensitivity dynamics. The first order optimality condition of the variational principle leads to a system of closed form evolution equations for an orthonormal basis and corresponding sensitivity coefficients. This approach allows for the computation of sensitivities with respect to a large number of parameters in an accurate and tractable manner by extracting correlations between different sensitivities on the fly. The presented method requires solving forward evolution equations, sidestepping the restrictions imposed by the forward/backward workflow of adjoint sensitivities. For example, the presented method, unlike the adjoint equation, does not impose any input/output load and can be used in applications in which real-time sensitivities are of interest. We demonstrate the utility of the method for three test cases: (1) computing sensitivity with respect to model parameters in the Rössler system, (2) computing sensitivity with respect to an infinite-dimensional forcing parameter in the chaotic Kuramoto--Sivashinsky equation, and (3) computing sensitivity with respect to reaction parameters for species transport in a turbulent reacting flow.

Reduced order model↗
DOE OSTI2017

Monte Carlo Estimates of Eigenvalue Sensitivity to System Dimensions using Kernel Density Estimators

In the last decade there has been an increasing interest in calculating sensitivity coefficients in order to quantify the impact of uncertainty in material properties and system parameters on quantities of interest. Extensive work has been done developing Monte Carlo methods to estimate eigenvalue sensitivity to material properties. While deterministic methods exist for determining the eigenvalue sensitivity to system dimensions, the use of Monte Carlo methods to efficiently calculate eigenvalue sensitivity coefficients to system dimensions is still an open topic. Current Monte Carlo methods for calculating eigenvalue sensitivity coefficients to system dimensions are inefficient. Direct Monte Carlo perturbations require two additional simulations of high statistical precision in order to determine the eigenvalue sensitivity to the location of a single boundary. If the sensitivity to the location of multiple surfaces are desired, two additional simulations must be run for each surface. Kiedrowski and Favorite extended continuous energy Monte Carlo methods for sensitivities of nuclear cross sections to calculate sensitivities to interface boundaries or system dimensions to create the only current alternative to direct Monte Carlo calculations. However this method relies on the simulation of additional random walks launched at each surface crossing event. The present method extends upon Kiedrowski's work and eliminates the inefficient portions of the calculation through the use of Kernel Density Estimators (KDEs). The new method efficiently estimates eigenvalue sensitivities to the uniform expansion or contraction of material interface boundaries or system dimensions, negligibly impacting the runtime performance of the Monte Carlo code. The new estimator is tested on bare and reflected critical configurations in spherical geometry and is compared to estimates obtained from direct Monte Carlo perturbations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Sensitivity Coefficients for Diffusion Coefficients and Other Reactor Physics Parameters using CE TSUNAMI-3D

The TSUNAMI (Tools for Sensitivity and Uncertainty Analysis Methodology Implementation) capabilities within the SCALE code system leverage sensitivity coefficients for many criticality safety applications, including quantifying the data-induced uncertainty in the eigenvalue of critical systems, quantifying computational biases, and selecting applicable benchmark experiments. TSUNAMI sensitivity and uncertainty analysis tools have traditionally been limited to criticality safety application spaces, but the introduction of Generalized Perturbation Theory (GPT) sensitivity methods by Jessee in 2009 and Perfetti in 2013 has extended the scope of SCALE adjoint-weighted sensitivity and uncertainty analysis to include reactor physics applications. The continuous-energy (CE) TSUNAMI-3D GPT sensitivity analysis methods were originally developed to calculate sensitivity coefficients for a limited number of reaction-rate responses using CE Monte Carlo methods. These tools were recently applied in an Oak Ridge National Laboratory (ORNL)-directed research and development project to improve the efficiency of {sup 252}Cf production in the High Flux Isotope Reactor (HFIR) by understanding the sensitivity of key heavy actinide capture-to- fission ratios to potential system design changes. This work further extends the applicability of the CE TSUNAMI- 3D GPT methods to reactor physics applications by enabling the calculation of sensitivity coefficients for an expanded set of reaction rate responses-including a first-ever capability for conducting sensitivity analysis of neutron diffusion coefficients.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Transitory sensitivity in automatic chemical kinetic mechanism analysis

Abstract Detailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams.

Johnson, Matthew S.↗
DOE OSTI2020

Perspectives on Dye Sensitization of Nanocrystalline Mesoporous Thin Films

Recent advances in our mechanistic understanding of dye-sensitized electron transfer reactions occurring at metal oxide interfaces are described. These advances were enabled by the advent of mesoporous thin films, comprised of anatase TiO 2 nanocrystallites, that are amenable to spectroscopic and electrochemical characterization in unprecedented molecular-level detail. The metal-to-ligand charge transfer (MLCT) excited states of Ru polypyridyl compounds serve as the dye sensitizers. Excited-state injection often occurs on ultrafast time scales with yields that can be tuned from unity to near zero through modification of the sensitizer or the electrolyte composition. Transport of the injected electron and the oxidized sensitizer (hole hopping) are both operative in the composite mechanism for charge recombination between the injected electron and the oxidized sensitizer. Sensitizers that contain a pendant electron donor, as well as core/shell SnO 2 /TiO 2 nanostructures, often prolong the lifetime of the injected electron and provide fundamental insights into adiabatic and nonadiabatic electron transfer mechanisms. Regeneration of the oxidized sensitizer by iodide is enhanced through halogen bonding, orbital pathways, and ion pairing. A substantial ~10 MV cm –1 electric field is created by electron injection into TiO 2 nanocrystallites that induces ion migration, reports on the sensitizer dipole orientation, and (in some cases) reorients or flips the sensitizer. Dye-sensitized conductive oxides also promote long-lived charge separation with bias dependent kinetics that provide insights into the reorganization energies associated with electron and proton-coupled electron transfer in the electric double layer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Correlating Mechanical Sensitivity with Spin Transition in the Explosive Spin Crossover Complex [Fe(Htrz) 3 ] n [ClO 4 ] 2 n

Spin crossover complexes are known to undergo bond length, volume, and enthalpy changes during spin transition. In an explosive spin crossover complex, these changes could affect the mechanical and initiation sensitivity of the explosive and lead to the development of a new class of sensitivity switchable materials. To explore this relationship, the well-known spin crossover compound [Fe(Htrz) 3 ] n [ClO 4 ] 2n (1) was re-evaluated for its explosive properties, and its mechanical impact sensitivity was correlated to spin transition. A variable temperature impact test was developed and used to evaluate the impact sensitivity of 1 in the low spin (LS, S = 0), thermally accessed high spin (HS, S = 2), and mixed LS and HS states. For comparison, the structurally similar Ni compound, [Ni(Htrz) 3 ] n [ClO 4 ] 2n (2), which does not undergo a spin transition at accessible temperatures, was synthesized and characterized, and its explosive properties and variable temperature impact sensitivity measured. These results reveal a correlation between impact sensitivity and spin transition, where 1 exhibits lower impact sensitivity in the LS state and increases in sensitivity upon transition to the HS state. Here, density functional theory was used to predict structural changes that occur upon spin transition that correlate to the change in sensitivity. This demonstrates, for the first time, an explosive spin crossover compound (ExSCO) that exhibits switchable impact sensitivity with a fully reversible internal switching mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Sensitivity of Beam Parameters to a Station C Solenoid Scan on Axis II

Magnet scans are a standard technique for determining beam parameters in accelerators. Beam parameters are inferred from spot size measurements using a model of the beam optics. The sensitivity of the measured beam spot size to the beam parameters is investigated for typical DARHT Axis II beam energies and currents. In a typical S4 solenoid scan, the downstream transport is tuned to achieve a round beam at Station C with an envelope radius of about 1.5 cm with a very small divergence with S4 off. The typical beam energy and current are 16.0 MeV and 1.625 kA. Figures 1-3 show the sensitivity of the bean size at Station C to the emittance, initial radius and initial angle respectively. To better understand the relative sensitivity of the beam size to the emittance, initial radius and initial angle, linear regressions were performed for each parameter as a function of the S4 setting. The results are shown in Figure 4. The measured slope was scaled to have a maximum value of 1 in order to present the relative sensitivities in a single plot. Figure 4 clearly shows the beam size at the minimum of the S4 scan is most sensitive to emittance and relatively insensitive to initial radius and angle as expected. The beam emittance is also very sensitive to the beam size of the converging beam and becomes insensitive to the beam size of the diverging beam. Measurements of the beam size of the diverging beam provide the greatest sensitivity to the initial beam radius and to a lesser extent the initial beam angle. The converging beam size is initially very sensitive to the emittance and initial angle at low S4 currents. As the S4 current is increased the sensitivity to the emittance remains strong while the sensitivity to the initial angle diminishes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

DAGSENS: Directed Acyclic Graph Based Direct and Adjoint Transient Sensitivity Analysis for Event-Driven Objective Functions

We present DAGSENS, a new theory for parametric transient sensitivity analysis of Differential Algebraic Equation systems (DAEs), such as SPICE-level circuits. The key ideas behind DAGSENS are, (1) to represent the entire sequence of computations, starting from DAE parameters, all the way up to the objective function whose sensitivity is needed, as a Directed Acyclic Graph (DAG) called the "sensitivity DAG", and (2) to compute the required sensitivities efficiently (with time complexity linear in the size of the sensitivity DAG) by leveraging dynamic programming techniques to traverse the DAG. DAGSENS is simple, elegant, and easy-to-understand compared to existing sensitivity analysis approaches; for example, in DAGSENS, one can switch between direct and adjoint transient sensitivities just by changing the direction of DAG traversal (i.e., topological order vs. reverse topological order). Also, DAGSENS is significantly more powerful than existing sensitivity analysis approaches because it allows one to compute the sensitivities of a much more general class of objective functions, including those defined based on "events" that occur during a transient simulation (e.g., a node voltage crossing a particular threshold, a phase-locked loop (PLL) achieving lock, a signal reaching its maximum/minimum value during a transient run, etc.). In this paper, we apply DAGSENS to compute the sensitivities of important event-driven performance metrics in several real-world electronic and biological applications, including high-speed communication (featuring sub-systems such as I/0 links and PLLs), statistical cell library characterization, and gene expression in Drosophila embryos.

42 ENGINEERING↗
DOE OSTI2026

On the Statistical Uncertainty of Monte Carlo-Calculated Scattering Sensitivities

Sensitivity coefficients calculated with Monte Carlo codes are widely used for nuclear data uncertainty quantification in the modeling and simulation of complex 3D reactor systems. This study systematically compares sensitivity coefficients and associated statistical uncertainties for the multiplication factor and fuel temperature reactivity across multiple Monte Carlo codes (SCALE/KENO, SCALE/Shift, MCNP, and Serpent) using simple models representing light-water reactors and advanced reactor concepts. For multiplication factor sensitivities, statistical uncertainties are generally acceptable, although scattering sensitivities show significantly larger statistical uncertainties than, for example, fission and capture reactions. Fuel temperature reactivity sensitivities show significantly larger statistical uncertainties across all reactions. Elastic scattering sensitivities are the most problematic: all Monte Carlo codes fail to resolve energy-dependent coefficients, and they produce dramatically different energy-collapsed values. Critically, the use of these sensitivity coefficients in nuclear data uncertainty propagation leads to reduced statistical uncertainties in individual uncertainty contributions. This can lead to the masking of unusable sensitivity coefficients and producing misleading uncertainty results. The findings of this study show that new or enhanced methods are needed to improve Monte Carlo elastic scattering sensitivity calculations. Additionally, this study shows the relevance of verifying sensitivity coefficients through direct perturbation calculations for individual nuclide reactions, instead of only for total cross sections as commonly done.

Bostelmann, Rike [ORNL] (ORCID:0000000165968088)↗
NASA NTRS1994

Aeroacoustic sensitivity analysis and optimal aeroacoustic design of turbomachinery blades

During the first year of the project, we have developed a theoretical analysis - and wrote a computer code based on this analysis - to compute the sensitivity of unsteady aerodynamic loads acting on airfoils in cascades due to small changes in airfoil geometry. The steady and unsteady flow though a cascade of airfoils is computed using the full potential equation. Once the nominal solutions have been computed, one computes the sensitivity. The analysis takes advantage of the fact that LU decomposition is used to compute the nominal steady and unsteady flow fields. If the LU factors are saved, then the computer time required to compute the sensitivity of both the steady and unsteady flows to changes in airfoil geometry is quite small. The results to date are quite encouraging, and may be summarized as follows: (1) The sensitivity procedure has been validated by comparing the results obtained by 'finite difference' techniques, that is, computing the flow using the nominal flow solver for two slightly different airfoils and differencing the results. The 'analytic' solution computed using the method developed under this grant and the finite difference results are found to be in almost perfect agreement. (2) The present sensitivity analysis is computationally much more efficient than finite difference techniques. We found that using a 129 by 33 node computational grid, the present sensitivity analysis can compute the steady flow sensitivity about ten times more efficiently that the finite difference approach. For the unsteady flow problem, the present sensitivity analysis is about two and one-half times as fast as the finite difference approach. We expect that the relative efficiencies will be even larger for the finer grids which will be used to compute high frequency aeroacoustic solutions. Computational results show that the sensitivity analysis is valid for small to moderate sized design perturbations. (3) We found that the sensitivity analysis provided important insight into how airfoils should be modified to improve their aeroelastic stability. Using this insight, we redesigned an aeroelastically unstable cascade to produce a stable cascade.

Hall, Kenneth C.↗
DOE OSTI2014

Radiation sensitive devices and systems for detection of radioactive materials and related methods

Radiation sensitive devices include a substrate comprising a radiation sensitive material and a plurality of resonance elements coupled to the substrate. Each resonance element is configured to resonate responsive to non-ionizing incident radiation. Systems for detecting radiation from a special nuclear material include a radiation sensitive device and a sensor located remotely from the radiation sensitive device and configured to measure an output signal from the radiation sensitive device. In such systems, the radiation sensitive device includes a radiation sensitive material and a plurality of resonance elements positioned on the radiation sensitive material. Methods for detecting a presence of a special nuclear material include positioning a radiation sensitive device in a location where special nuclear materials are to be detected and remotely interrogating the radiation sensitive device with a sensor.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Sensitivity Studies on UNF Cladding Structural Integrity under NCT Loading

This work describes the sensitivity analyses performed on the transportation package used in the NCT initial modeling and simulation demonstration [1]. The initial demonstration predicted the structural integrity of the fuel cladding by integrating models from the fuel rod/grid slot level, the single assembly-level, and the full cask-level. A number of sensitivity analyses were performed on the models at the different levels to determine how sensitive the results were to the parameters that were varied. The results are used to coarsely group the parameters investigated into four categories of sensitivity: No Sensitivity, Low Sensitivity; Moderate Sensitivity, and High Sensitivity. The High Sensitivity analyses are described in detail below. The Moderate, Low and No Sensitivity results are briefly described.

used nuclear fuel, UNF, normal conditions of trans↗