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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Demonstration of a rapidly-swept external cavity quantum cascade laser for rapid and sensitive quantification of chemical mixtures

A rapidly-swept external cavity quantum cascade laser (ECQCL) system for fast open-path quantification of multiple chemicals and mixtures is presented. The ECQCL system is swept over its entire tuning range (>100 cm-1) at frequencies up to 200 Hz. At 200 Hz the wavelength tuning rate and spectral resolution are 2x104 cm-1/sec and < 0.2 cm-1, respectively. The capability of the current system to quantify changes in chemical concentrations on millesecond timescales is demonstrated at atmospheric pressure using an open-path multi-pass cell. The detection limits for chemicals ranged from ppb to ppm levels depending on the absorption cross-section.

Brumfield, Brian E.↗
DOE OSTI2017

Notch-Jagged complex structure implicates a catch bond in tuning ligand sensitivity

Notch receptor activation initiates cell fate decisions and is distinctive in its reliance on mechanical force and protein glycosylation. The 2.5-angstrom-resolution crystal structure of the extracellular interacting region of Notch1 complexed with an engineered, high-affinity variant of Jagged1 (Jag1) reveals a binding interface that extends ~120 angstroms along five consecutive domains of each protein. O-Linked fucose modifications on Notch1 epidermal growth factor–like (EGF) domains 8 and 12 engage the EGF3 and C2 domains of Jag1, respectively, and different Notch1 domains are favored in binding to Jag1 than those that bind to the Delta-like 4 ligand. Jag1 undergoes conformational changes upon Notch binding, exhibiting catch bond behavior that prolongs interactions in the range of forces required for Notch activation. In conclusion, this mechanism enables cellular forces to regulate binding, discriminate among Notch ligands, and potentiate Notch signaling.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Use of Taguchi Design of Experiments to Determine ALPLS Ascent Delta-5 Sensitivities and Total Mass Sensitivities to Release Conditions and Vehicle Parameters

The objective of this study is to evaluate the use of Taguchi's Design of Experiment Methods to improve the effectiveness of this and future parametric studies. Taguchi Methods will be applied in addition to the typical approach to provide a mechanism for comparing the results and the cost or effort necessary to complete the studies. It is anticipated that results of this study should include an improved systematic analysis process, an increase in information obtained at a lower cost, and a more robust, cost effective vehicle design.

Carrasco, Hector Ramon↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Sensitivity Analysis of Multidisciplinary Rotorcraft Simulations

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. An unstructured sensitivity-enabled Navier-Stokes solver, FUN3D, and a nonlinear flexible multibody dynamics solver, DYMORE, are coupled to predict the aerodynamic loads and structural responses of helicopter rotor blades. A discretely-consistent adjoint-based sensitivity analysis available in FUN3D provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute DYMORE 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. Numerical results verify accuracy of the FUN3D/DYMORE system by conducting simulations for a benchmark rotorcraft test model and comparing solutions with established analyses and experimental data. Complex-variable implementation of sensitivity analysis of DYMORE and the coupled FUN3D/DYMORE system is verified by comparing with real-valued analysis and sensitivities. Correctness of adjoint formulations for FUN3D/DYMORE interfaces is verified by comparing adjoint-based and complex-variable sensitivities. Finally, sensitivities of the lift and drag functions obtained by complex-variable FUN3D/DYMORE simulations are compared with sensitivities computed by the multidisciplinary sensitivity analysis, which couples adjoint-based flow and grid sensitivities of FUN3D and FUN3D/DYMORE interfaces with complex-variable sensitivities of DYMORE structural responses.

Wang, Li↗
DOE OSTI2015

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Through-thickness microstructural evolution during grain boundary engineering type thermomechanical processing and its implication on sensitization behavior in austenitic stainless steel

Highlights: • GBE-treated 304L steel exhibits thickness-dependent microstructural evolution. • Degree of sensitization increases from surface to interior in GBE specimen. • Low fractal dimension and large TRD size lead to lesser sensitization at surface. • As-received specimen shows high albeit thickness-independent sensitization behavior. • Higher residual strain leads to higher sensitization at surface of deformed sample. - Abstract: Through-thickness microstructural variations and their synergistic influence on degree of sensitization in grain boundary engineering (GBE) treated 304L stainless steel was compared with solution-annealed and marginally cold-worked (~ 3% thickness reduction) specimens. The solution-annealed specimen has shown higher degree of sensitization due to the presence of lower fraction of Σ3 (as well as other low Σ CSL) boundaries and dominant random high-angle boundaries (RHAGBs) connectivity. However, it has shown thickness-independent sensitization behavior as grain boundary character distribution, residual strain and grain boundary network topology towards thickness direction are similar. Owing to higher residual strain, as revealed by local misorientation analysis, the cold-worked specimen showed maximum degree of sensitization (higher than solution-annealed condition) at surface. The GBE specimen has shown much lower degree of sensitization at surface due to higher-fraction of Σ3 boundaries and discontinuous RHAGBs connectivity. However, increasing fraction of RHAGBs and their connectivity, as confirmed by twinning-related domain and fractal analysis, cause more sensitization at interior of this specimen. In spite of this, the degree of sensitization at the interior of GBE specimen is lower than the solution-annealed or cold-worked specimen. Even though the residual strain in GBE specimen is higher than the solution-annealed condition, the lower degree of sensitization indicates that the grain boundary character distribution and grain boundary network topology have higher implication than residual strain in controlling sensitization.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-E-T-315: The Change of Optically Stimulated Luminescent Dosimeters (OSLDs) Sensitivity by Accumulated Dose and High Dose

Purpose: The objective of this study is to evaluate radiation sensitivity of optical stimulated luminance dosimeters (OSLDs) by accumulated dose and high dose. Methods: This study was carried out in Co-60 unit (Theratron 780, AECL, and Canada) and used InLight MicroStar reader (Landauer, Inc., Glenwood, IL) for reading. We annealed for 30 min using optical annealing system which contained fluorescent lamps (Osram lumilux, 24 W, 280 ∼780 nm). To evaluate change of OSLDs sensitivity by repeated irradiation, the dosimeters were repeatedly irradiated with 1 Gy. And whenever a repeated irradiation, we evaluated OSLDs sensitivity. To evaluate OSLDs sensitivity after accumulated dose with 5 Gy, We irradiated dose accumulatively (from 1 Gy to 5 Gy) without annealing. And OSLDs was also irradiated with 15, 20, 30 Gy to certify change of OSLDs sensitivity after high dose irradiation. After annealing them, they were irradiated with 1Gy, repeatedly. Results: The OSLDs sensitivity increased up to 3% during irradiating seven times and decreased continuously above 8 times. That dropped by about 0.35 Gy per an irradiation. Finally, after 30 times irradiation, OSLDs sensitivity decreased by about 7%. For accumulated dose from 1 Gy to 5 Gy, OSLDs sensitivity about 1 Gy increased until 4.4% after second times accumulated dose compared with before that. OSLDs sensitivity about 1 Gy decreased by 1.6% in five times irradiation. When OSLDs were irradiated ten times with 1Gy after irradiating high dose (10, 15, 20 Gy), OSLDs sensitivity decreased until 6%, 9%, 12% compared with it before high dose irradiation, respectively. Conclusion: This study certified OSLDs sensitivity by accumulated dose and high dose. When irradiated with 1Gy, repeatedly, OSLDs sensitivity decreased linearly and the reduction rate of OSLDs sensitivity after high dose irradiation had dependence on irradiated dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here in this paper we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

97 MATHEMATICS AND COMPUTING↗