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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.

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At least 109 records · Page 6

A Quantitative Study of Oxygen as a Metabolic Regulator

An acute reduction in oxygen (O2) delivery to a tissue is generally associated with a decrease in phosphocreatine, increases in ADP, NADH/NAD, and inorganic phosphate, increased rates of glycolysis and lactate production, and reduced rates of pyruvate and fatty acid oxidation. However, given the complexity of the human bioenergetic system and its components, it is difficult to determine quantitatively how cellular metabolic processes interact to maintain ATP homeostasis during stress (e.g., hypoxia, ischemia, and exercise). Of special interest is the determination of mechanisms relating tissue oxygenation to observed metabolic responses at the tissue, organ, and whole body levels and the quantification of how changes in tissue O2 availability affect the pathways of ATP synthesis and the metabolites that control these pathways. In this study, we extend a previously developed mathematical model of human bioenergetics to provide a physicochemical framework that permits quantitative understanding of O2 as a metabolic regulator. Specifically, the enhancement permits studying the effects of variations in tissue oxygenation and in parameters controlling the rate of cellular respiration on glycolysis, lactate production, and pyruvate oxidation. The whole body is described as a bioenergetic system consisting of metabolically distinct tissue/organ subsystems that exchange materials with the blood. In order to study the dynamic response of each subsystem to stimuli, we solve the ordinary differential equations describing the temporal evolution of metabolite levels, given the initial concentrations. The solver used in the present study is the packaged code LSODE, as implemented in the NASA Lewis kinetics and sensitivity analysis code, LSENS. A major advantage of LSENS is the efficient procedures supporting systematic sensitivity analysis, which provides the basic methods for studying parameter sensitivities (i.e., changes in model behavior due to parameter variation). Sensitivity analysis establishes relationships between model predictions and problem parameters (i.e., initial concentrations, rate coefficients, etc). It helps determine the effects of uncertainties or changes in these input parameters on the predictions, which ultimately are compared with experimental observations in order to validate the model. Sensitivity analysis can identify parameters that must be determined accurately because of their large effect on the model predictions and parameters that need not be known with great precision because they have little or no effect on the solution. This capability may prove to be important in optimizing the design of experiments, thereby reducing the use of animals. This approach can be applied to study the metabolic effects of reduced oxygen delivery to cardiac muscle due to local myocardial ischemia and the effects of acute hypoxia on brain metabolism. Other important applications of sensitivity analysis include identification of quantitatively relevant pathways and biochemical species within an overall mechanism, when examining the effects of a genetic anomaly or pathological state on energetic system components and whole system behavior.

Radhakrishnan, Krishnan↗

Reliability estimation procedures and CARE: The Computer-Aided Reliability Estimation Program

Ultrareliable fault-tolerant onboard digital systems for spacecraft intended for long mission life exploration of the outer planets are under development. The design of systems involving self-repair and fault-tolerance leads to the companion problem of quantifying and evaluating the survival probability of the system for the mission under consideration and the constraints imposed upon the system. Methods have been developed to (1) model self-repair and fault-tolerant organizations; (2) compute survival probability, mean life, and many other reliability predictive functions with respect to various systems and mission parameters; (3) perform sensitivity analysis of the system with respect to mission parameters; and (4) quantitatively compare competitive fault-tolerant systems. Various measures of comparison are offered. To automate the procedures of reliability mathematical modeling and evaluation, the CARE (computer-aided reliability estimation) program was developed. CARE is an interactive program residing on the UNIVAC 1108 system, which makes the above calculations and facilitates report preparation by providing output in tabular form, graphical 2-dimensional plots, and 3-dimensional projections. The reliability estimation of fault-tolerant organization by means of the CARE program is described.

Mathur, F. P.↗

Design Parameters Influencing Reliability of CCGA Assembly: A Sensitivity Analysis

Area Array microelectronic packages with small pitch and large I/O counts are now widely used in microelectronics packaging. The impact of various package design and materials/process parameters on reliability has been studied through extensive literature review. Reliability of Ceramic Column Grid Array (CCGA) package assemblies has been evaluated using JPL thermal cycle test results (-50(deg)/75(deg)C, -55(deg)/100(deg)C, and -55(deg)/125(deg)C), as well as those reported by other investigators. A sensitivity analysis has been performed using the literature da to study the impact of design parameters and global/local stress conditions on assembly reliability. The applicability of various life-prediction models for CCGA design has been investigated by comparing model's predictions with the experimental thermal cycling data. Finite Element Method (FEM) analysis has been conducted to assess the state of the stress/strain in CCGA assembly under different thermal cycling, and to explain the different failure modes and locations observed in JPL test assemblies.

reliability↗

Sensitivity Analysis of Salt Storage Cavern Mechanical Integrity Test Parameters

This report examines the sensitivity of salt cavern Mechanical Integrity Tests (MIT) to uncertainties in key test parameters. MIT's are used by cavern operators to detect and quantify leak rates in access wells in underground salt storage caverns and involve a suite of measured and assumed parameters that have a direct impact on the sensitivity of the testing to detect actual leaks from the cavern storage system. Determining the sensitivity of the testing to these different parameters provides a basis for understanding the results from, and informing the design criteria for, this type of testing. Without fully understanding the sensitivity of the test to the testing parameters, it is possible that the test results may not accurately reflect the integrity of the cavern system; an actual leak may be missed, or an intact system may be interpreted as leaking. This report reviews the main parameters included in MITs and examines how selected changes in their values can impact test results. The deviations used in the sensitivity analyses were designed to be within the ranges believed to be similar to those which may be encountered during testing. The results show that small, plausible fluctuations in some of the parameters measured values can have a significant impact on the testing results. Of the parameters studied here, the sensitivity analyses showed the order of importance to be (from highest to lowest): nitrogen-oil interface depth measurement, well bore temperature, well head pressure, and finally the internal geometry of the testing interval.

42 ENGINEERING↗

A design methodology for nonlinear systems containing parameter uncertainty

In the present design methodology for nonlinear systems containing parameter uncertainty, a generalized sensitivity analysis is incorporated which employs parameter space sampling and statistical inference. For the case of a system with j adjustable and k nonadjustable parameters, this methodology (which includes an adaptive random search strategy) is used to determine the combination of j adjustable parameter values which maximize the probability of those performance indices which simultaneously satisfy design criteria in spite of the uncertainty due to k nonadjustable parameters.

Young, G. E.↗

Sensitivity Analysis for Takeoff and Landing Distance Parameters for Regional Air Mobility (RAM) Aircraft

Takeoff and landing performance models require low-speed aerodynamic and thrust characteristics that are difficult to predict in the conceptual design stage. Although an empirical approach can be utilized to predict takeoff and landing performance, this approach is inadequate for aircraft incorporating novel technologies and design such as those proposed for regional air mobility aircraft. The focus of this paper is to understand the sensitivity of takeoff and landing distance to aerodynamic, propulsive, and other parameters of interest for regional air mobility class vehicles. The aerodynamic and propulsive characteristics are generated and modified using simplified approximations based on fundamental physics and integrated into the equations of motion to calculate takeoff and landing distances. The most impactful parameters for takeoff and landing were those related to propulsion and the lift curve. Additional impactful parameters for landing included braking coefficient, approach angle, and wing incidence. Lesser impact was observed for the parameters describing the low-speed drag polar.

Gabino Martinez Rodriguez↗

Uncertainty Analysis of the Single-Vector Force Balance Calibration System

This paper presents an uncertainty analysis of the Single-Vector Force Balance Calibration System (SVS). This study is focused on the uncertainty involved in setting the independent variables during the calibration experiment. By knowing the uncertainty in the calibration system, the fundamental limits of the calibration accuracy of a particular balance can be determined. A brief description of the SVS mechanical system is provided. A mathematical model is developed to describe the mechanical system elements. A sensitivity analysis of these parameters is carried out through numerical simulations to assess the sensitivity of the total uncertainty to the elemental error sources. These sensitivity coefficients provide valuable information regarding the relative significance of the elemental sources of error. An example calculation of the total uncertainty for a specific balance is provided. Results from this uncertainty analysis are specific to the Single-Vector System, but the approach is broad in nature and therefore applicable to other measurement and calibration systems.

Parker, Peter A.↗

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗

Assessment of BISON capabilities for component-level prediction of tritium transport in fusion and fission applications

Here we assessed the hydrogen migration and redistribution model implemented in the BISON fuel performance code for component-level tritium transport applications in fusion and fission example scenarios. We developed BISON models of an ITER heat exchanger, light water reactor fuel cladding, and a fluoride salt cooled high temperature reactor heat exchanger. Reasonable agreement between reported values in reference studies and BISON predictions demonstrated the ability of the BISON models to predict tritium transport behavior through steel materials. Next, sensitivity and uncertainty analysis was used to understand key parameter sensitivities within the BISON model for evaluation of tritium migration and redistribution. . The sensitivity analysis showed that the diffusivity activation energy was the most important parameter and that the heat of transport was the least important parameter for tritium migration in steels. Overall, the capabilities of the BISON code for component level modeling of tritium transport are promising.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗

Key Reliability Drivers of Liquid Propulsion Engines and A Reliability Model for Sensitivity Analysis

This paper is to address the in-flight reliability of a liquid propulsion engine system for a launch vehicle. We first establish a comprehensive list of system and sub-system reliability drivers for any liquid propulsion engine system. We then build a reliability model to parametrically analyze the impact of some reliability parameters. We present sensitivity analysis results for a selected subset of the key reliability drivers using the model. Reliability drivers identified include: number of engines for the liquid propulsion stage, single engine total reliability, engine operation duration, engine thrust size, reusability, engine de-rating or up-rating, engine-out design (including engine-out switching reliability, catastrophic fraction, preventable failure fraction, unnecessary shutdown fraction), propellant specific hazards, engine start and cutoff transient hazards, engine combustion cycles, vehicle and engine interface and interaction hazards, engine health management system, engine modification, engine ground start hold down with launch commit criteria, engine altitude start (1 in. start), Multiple altitude restart (less than 1 restart), component, subsystem and system design, manufacturing/ground operation support/pre and post flight check outs and inspection, extensiveness of the development program. We present some sensitivity analysis results for the following subset of the drivers: number of engines for the propulsion stage, single engine total reliability, engine operation duration, engine de-rating or up-rating requirements, engine-out design, catastrophic fraction, preventable failure fraction, unnecessary shutdown fraction, and engine health management system implementation (basic redlines and more advanced health management systems).

Huang, Zhao-Feng↗

Uncertainty Quantification and Sensitivity Analysis of Nuclear Construction Cost Reduction Pathways

High construction costs have plagued recent nuclear projects and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a plant economic framework that quantifies the impact that various plant design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model outputs and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through a sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of plants. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.

Levelized Cost of Electricity↗

Sensitivity and uncertainty analysis of PWR spent fuel observables to operational and model parameters

Sensitivity and uncertainty analyses of spent nuclear fuel (SNF) observables - decay heat, neutron and γ-ray emission rate - to operational and model parameters have been performed. A 2D model representing a typical PWR 17x17 UO{sub 2} fuel assembly has been taken as reference. The Serpent code and ENDF/B-VII.1 evaluated nuclear data library have been used for the analyses. Relative uncertainty of decay heat, neutron and γ-ray emission rates due to Monte Carlo counting statistics, as well as the number of fuel pin radial regions required for an accurate characterization of SNF neutron emission, have been estimated. A parametric study has been carried out to investigate the impact of the homogenization to an average value of the fuel and moderator temperature, boron concentration power and burnup on the prediction of the selected observables. In addition, linear sensitivity coefficients have been calculated. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Dynamic Rupture Modeling of the 1999 Chi-Chi, Taiwan Earthquake Using 3DFinite Element Method. Sensitivity Analysis of Slip Rate Function to Model Parameters of Crustal Weak-Zone

The main objective of this study was the dynamic rupture modeling of the M7.3 1999 Chi-Chi, Taiwan earthquake. The purpose of the numerical modeling was two folded. First, using models of rupture dynamics, derive kinematic rupture characteristics, including spatial and temporal variations of the slip rate time history and rupture speed, in the long-period ground motion generation area (LMGA), strong motion generation areas (SMGAs) and background fault area for a thrust shallow rupture. Second, investigate the sensitivity of source time function characteristics in the LMGA and SMGA areas to model parameterization of low-velocity weak zone, such as thickness and shear wave velocity.

58 GEOSCIENCES↗

Microgravity-Induced Physiological Fluid Redistribution: Computational Analysis to Assess Influence of Physiological Parameters

Space flight impacts human physiology in many ways, the most immediate being the marked cephalad (headward) shift of fluid upon introduction into the microgravity environment. This physiological response to microgravity points to the redistribution of blood and interstitial fluid as a major factor in the loss of venous tone and reduction in heart muscle efficiency which impact astronaut performance. In addition, researchers have hypothesized that a reduction in astronaut visual acuity, part of the Visual Impairment and Intracranial Pressure (VIIP) syndrome, is associated with this redistribution of fluid. VIIP arises within several months of beginning space flight and includes a variety of ophthalmic changes including posterior globe flattening, distension of the optic nerve sheath, and kinking of the optic nerve. We utilize a suite of lumped parameter models to simulate microgravity-induced fluid redistribution in the cardiovascular, central nervous and ocular systems to provide initial and boundary data to a 3D finite element simulation of ocular biomechanics in VIIP. Specifically, the lumped parameter cardiovascular model acts as the primary means of establishing how microgravity, and the associated lack of hydrostatic gradient, impacts fluid redistribution. The cardiovascular model consists of 16 compartments, including three cerebrospinal fluid (CSF) compartments, three cranial blood compartments, and 10 thoracic and lower limb blood compartments. To assess the models capability to address variations in physiological parameters, we completed a formal uncertainty and sensitivity analysis that evaluated the relative importance of 42 input parameters required in the model on relative compartment flows and compartment pressures. Utilizing the model in a pulsatile flow configuration, the sensitivity analysis identified the ten parameters that most influenced each compartment pressure. Generally, each compartment responded appropriately to parameter variations associated with itself and adjacent compartments. However, several unexpected interactions between components, such as between the choroid plexus and the lower capillaries, were found, and are due to simplifications in the formulation of the model. The analysis illustrates that highly influential parameters and those that have unique influences within the model formulation must be tightly controlled for successful model application.

gravitational physiology↗

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗