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At least 1,153 records · Page 64

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NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Dynamic stability testing techniques currently utilized at NASA Langley Research Center (LaRC) are conducted in multiple facilities and consists of free flight, forced oscillation, and free-to-oscillate tests. The MIT/NASA/ODU Magnetic Suspension and Balance System (MSBS) has been recommissioned to explore its utility as an additional facility to expand the dynamic stability test capabilities currently available at NASA LaRC. Simulations were created to replicate each current test facility and method as closely as possible. Data collected from the simulated environments was corrupted with replicated noise sources of the different testing environments and then compared to real data collected during tests when such data was available. The corrupted data was then passed through data reduction and System Identification (SID) to estimate the accuracy of the results with the known aerodynamic model that was utilized within the simulation to generate the original data. Magnitudes of noise were varied utilizing Monte Carlo analysis to perform sensitivity analysis of each noise source on the extracted dynamic stability coefficients. Some preliminary results will be presented.

Otoniel Ramirez↗

NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Otoniel Ramirez↗

DOE OSTI2017

Neutron cross section sensitivity and uncertainty analysis of candidate accident tolerant fuel concepts

Not Available

Burns, Joseph R.↗

DOE OSTI2014

Combined Municipal Solid Waste and biomass system optimization for district energy applications

Highlights: • Combined energy conversion of MSW and agricultural residue biomass is examined. • The model optimizes the financial yield of the investment. • Several system specifications are optimally defined by the optimization model. • The application to a case study in Greece shows positive financial yield. • The investment is mostly sensitive on the interest rate, the investment cost and the heating oil price. - Abstract: Municipal Solid Waste (MSW) disposal has been a controversial issue in many countries over the past years, due to disagreement among the various stakeholders on the waste management policies and technologies to be adopted. One of the ways of treating/disposing MSW is energy recovery, as waste is considered to contain a considerable amount of bio-waste and therefore can lead to renewable energy production. The overall efficiency can be very high in the cases of co-generation or tri-generation. In this paper a model is presented, aiming to support decision makers in issues relating to Municipal Solid Waste energy recovery. The idea of using more fuel sources, including MSW and agricultural residue biomass that may exist in a rural area, is explored. The model aims at optimizing the system specifications, such as the capacity of the base-load Waste-to-Energy facility, the capacity of the peak-load biomass boiler and the location of the facility. Furthermore, it defines the quantity of each potential fuel source that should be used annually, in order to maximize the financial yield of the investment. The results of an energy tri-generation case study application at a rural area of Greece, using mixed MSW and biomass, indicate positive financial yield of investment. In addition, a sensitivity analysis is performed on the effect of the most important parameters of the model on the optimum solution, pinpointing the parameters of interest rate, investment cost and heating oil price, as those requiring the attention of the decision makers. Finally, the sensitivity analysis is enhanced by a stochastic analysis to determine the effect of the volatility of parameters on the robustness of the model and the solution obtained.

09 BIOMASS FUELS↗

DOE OSTI2014

Phase field method to optimize dielectric devices for electromagnetic wave propagation

We discuss a phase field method for shape optimization in the context of electromagnetic wave propagation. The proposed method has the same functional capabilities as the level set method for shape optimization. The first advantage of the method is the simplicity of computation, since extra operations such as re-initialization of functions are not required. The second is compatibility with the topology optimization method due to the similar domain representation and the sensitivity analysis. Structural shapes are represented by the phase field function defined in the design domain, and this function is optimized by solving a time-dependent reaction diffusion equation. The artificial double-well potential function used in the equation is derived from sensitivity analysis. We study four types of 2D or 2.5D (axisymmetric) optimization problems. Two are the classical problems of photonic crystal design based on the Bloch theory and photonic crystal wave guide design, and two are the recent topics of designing dielectric left-handed metamaterials and dielectric ring resonators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Hanford Site Composite Analysis: Dose Sensitivity Analyses

This environmental calculation file (ECF) documents the methodologies, assumptions, and results of five analyses that evaluate parameter changes to the revised Hanford Site Composite Analysis (CA) (DOE-RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau). Specifically, this ECF describes the impact on total dose and analyte-specific doses for five sensitivity cases where changes to the following exposure-related parameters were implemented – (1) inventory, (2) recharge rate, (3) external dose conversion factors (DCFs) based on Federal Guidance Report (FGR) No. 15 in (EPA-402-R-19-002, Federal Guidance Report No. 15, External Exposure to Radionuclides in Air, Water, and Soil) instead of FGR No. 12 (EPA 402-R-93-081, Federal Guidance Report No. 12, External Exposure to Radionuclides in Air, Water, and Soil) for groundwater pathway scenario; (4) use of mean instead of 95th percentile intake assumptions for groundwater pathway scenario; and (5) consideration of limited sources based on U.S. Department of Energy (DOE) owned waste sites regulated under DOE O 435.1, Radioactive Waste Management. It should be noted that dose assessments for both base and sensitivity cases are based on the all-pathways representative person groundwater exposure scenario presented in DOE/RL-2022-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

TPSAS-NF1676L-31914-DND

Analysis of aircraft systems involves many disciplines: aerodynamics, structural dynamics, aeroacoustics, etc. - rotorcraft aeromechanics is a typical example. Gradient-based, multidisciplinary optimization (MDO) requires sensitivity analysis for all disciplines. Adjoint-based sensitivity analysis capabilities for: - Coupled aero/structure FUN3D/DYMORE system; - Coupled aero/acoustics FUN3D/ANOPP2 system.

Li Wang↗

DOE OSTI2025

GEAR-MC and Differential-Operator Methods Applied to Electron-Photon Transport in the Integrated TIGER Series

The sensitivity analysis algorithms that have been developed by the radiation transport community in multiple neutron transport codes, such as MCNP and SCALE, are extensively used by fields such as the nuclear criticality community. However, these techniques have seldom been considered for electron transport applications. In the past, the differential-operator method with the single scatter capability has been implemented in Sandia National Laboratories’ Integrated TIGER Series (ITS) coupled electron-photon transport code. This work is meant to extend the available sensitivity estimation techniques in ITS by implementing an adjoint-based sensitivity method, GEAR-MC, to strengthen its sensitivity analysis capabilities. To ensure the accuracy of this method being extended to coupled electron-photon transport, it is compared against the central-difference and differential-operator methodologies to estimate sensitivity coefficients for an experiment performed by McLaughlin and Hussman. Energy deposition sensitivities were calculated using all three methods, and the comparison between them has provided confidence in the accuracy of the newly implemented method. Unlike the current implementation of the differential-operator method in ITS, the GEAR-MC method was implemented with the option to calculate the energy-dependent energy deposition sensitivities, which are the sensitivity coefficients for energy deposition tallies to energy-dependent cross sections. The energy-dependent cross sections could be the cross sections for the material, elements in the material, or reactions of interest for the element. Further, these sensitivities were compared to the energy-integrated sensitivity coefficients and exhibited a maximum percentage difference of 2.15%.

42 ENGINEERING↗

DOE OSTI2015

Critical processes and parameters in the development of accident tolerant fuels drop-in capsule irradiation tests

The goal of the Accident Tolerant Fuel (ATF) program is to develop the next generation of Light Water Reactor (LWR) fuels with improved performance, reliability, and safety characteristics during normal operations and accident conditions and with reduced waste generation. An irradiation test series has been defined to assess the performance of proposed ATF concepts under normal LWR operating conditions. The Phase I ATF irradiation test series is planned to be performed as a series of drop-in capsule tests to be irradiated in the Advanced Test Reactor (ATR) operated by the Idaho National Laboratory (INL). Design, analysis, and fabrication processes for ATR drop-in capsule experiment preparation are presented in this paper to demonstrate the importance of special design considerations, parameter sensitivity analysis, and precise fabrication and inspection techniques for figure innovative materials used in ATF experiment assemblies. A Taylor Series Method sensitivity analysis approach was used to identify the most critical variables in cladding and rodlet stress, temperature, and pressure calculations for design analyses. The results showed that internal rodlet pressure calculations are most sensitive to the fission gas release rate uncertainty while temperature calculations are most sensitive to cladding I.D. and O.D. dimensional uncertainty. The analysis showed that stress calculations are most sensitive to rodlet internal pressure uncertainties, however the results also indicated that the inside radius, outside radius, and internal pressure were all magnified as they propagate through the stress equation. This study demonstrates the importance for ATF concept development teams to provide the fabricators as much information as possible about the material properties and behavior observed in prototype testing, mock-up fabrication and assembly, and chemical and mechanical testing of the materials that may have been performed in the concept development phase. Special handling, machining, welding, and inspection of materials, if known, should also be communicated to the experiment fabrication and inspection team.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Economic and life cycle assessments of biomass utilization for bioenergy products

A modeling process was developed to examine the economic and environmental benefits of utilizing energy crops for biofuels and bioproducts. Three energy crops (hybrid willow, switchgrass and miscanthus) that can potentially grow on marginal agricultural land or abandoned mine land in the northeastern United States were considered in the analytical process for the production of biofuels, biopower and pellet fuel. The supply chain components for both the economic analysis and life cycle modeling processes included feedstock establishment, harvest, transportation, storage, preprocessing, conversion, distribution and final usage. Sensitivity analysis was also conducted to assess the effects of energy crop yield, transportation distance, conversion rate, facility capacity and internal rate of return (IRR) on the production of bioenergy products. The required selling price (RSP) ranged from $ 7.7/GJ to $ 47.9/GJ for different bioproducts. The production of biopower had the highest RSP and pellet fuel had the lowest. The results also indicated that bioenergy production using hybrid willow demonstrated lower RSP than the two perennial grass feedstocks. Pellet production presented the lowest greenhouse gas (GHG) emissions (less than 10 kg CO 2 eq per 1,000 MJ) and fossil energy consumption (less than 150 MJ per 1,000 MJ). The production of biofuel resulted in the highest GHG emissions. Sensitivity analysis indicated that IRR was the most sensitive factor to RSP and followed by conversion rate for biofuel and biopower production. As a result, conversion rate and transportation distance of feedstock presented a significant effect on environmental impacts during the production of the bioproducts.

09 BIOMASS FUELS↗

DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗

DOE OSTI2019

Evaluation of diffusion kurtosis and diffusivity from baseline staging MRI as predictive biomarkers for response to neoadjuvant chemoradiation in locally advanced rectal cancer

Purpose: To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC). Materials and methods: This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s{sup 2}) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2–72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0–3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout. Results: In the univariable proportional odds regression analysis, higher diffusivity summary (D{sub sum}) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher D{sub sum} values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most D{sub sum} values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG. Conclusion: Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC. Clinical relevance statement: Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90–98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68–73%.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS1990

Improved calculation of optimum design sensitivity

Optimum design parameter sensitivity analysis has become an important topic in recent years. The principal reasons for obtaining optimum design sensitivity information with respect to various problem parameters are (l) to predict revised optimum designs, associated with specified perturbations of the problem parameters, without re-optimizing the problem and, (2) to provide sensitivity information in multilevel optimization strategies. Methods for calculating these derivatives were proposed by several authors. The most important drawback in estimating parameter sensitivities by the current methods is that they do not allow for changes in the active constraint set. Changes in the active constraint set produce discontinuities in the optimum design sensitivities, and therefore, a correct formulation has to be cast in terms of directional derivatives. Optimum design parameter sensitivity analysis is addressed in terms of directional derivatives so as to include possible discontinuities and it also presents a method for estimating optimum design sensitivities using finite differences.

Sepulveda, Abdon E.↗

NASA NTRS1996

Expendable Launch Vehicle Studies

Analytical support studies of expendable launch vehicles concentrates on the stability of the dynamics during launch especially during or near the region of maximum dynamic pressure. The in-plane linearized dynamic equations of a generic launch vehicle with multiple flexible bending and fuel sloshing modes are developed. The design of a robust LQR controller based on the reduced order system is accomplished using the parameter perturbation technique. The ELV modeling and analysis team has spent the past three years working on the theoretical development and application of sensitivity analysis to solve eigenvalue problems associated with structural dynamics. Specific application areas include stochastic vibrations, viscously damped vibrations and nonlinear dynamics. In stochastic linear vibrations, sensitivity analysis methods have been developed which determined the expectation and variance of the response for given probability density functions of various stochastic parameters. Examples worked include beam vibrations with up to six stochastic parameters; and the eigensensitivity results differ little from those obtained, with much effect, from Monte Carlo techniques. For viscously damped vibrations, eigensensitivity analysis has given excellent results for homogeneous beams, modeled by either quadratic or quartic eigenvalue equations. Specific applications include Kevin and Maxwell-type viscoelastic beams.

Bainum, P. M.↗

DOE OSTI2014

Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010). The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by the false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%. To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗

DOE OSTI2015

Analyzing and Comparing Biomass Feedstock Supply Systems in China: Corn Stover and Sweet Sorghum Case Studies

This paper analyzes the rural Chinese biomass supply system and models supply chain operations according to U.S. concepts of logistical unit operations: harvest and collection, storage, transportation, preprocessing, and handling and queuing. In this paper, we quantify the logistics cost of corn stover and sweet sorghum in China under different scenarios. We analyze three scenarios of corn stover logistics from northeast China and three scenarios of sweet sorghum stalks logistics from Inner Mongolia in China. The case study estimates that the logistics cost of corn stover and sweet sorghum stalk to be $52.95/dry metric ton and $52.64/dry metric ton, respectively, for the current labor-based biomass logistics system. However, if the feedstock logistics operation is mechanized, the cost of corn stover and sweet sorghum stalk decreases to $36.01/dry metric ton and $35.76/dry metric ton, respectively. The study also includes a sensitivity analysis to identify the cost factors that cause logistics cost variation. Results of the sensitivity analysis show that labor price has the most influence on the logistics cost of corn stover and sweet sorghum stalk, with a variation of $6 to $12/dry metric ton.

09 BIOMASS FUELS↗

DOE OSTI2015

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

58 GEOSCIENCES↗