Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sensitive”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The effect of chemical functional groups on the octane sensitivity of fuel blends for spark-ignited and multimode engines

We report the octane sensitivity of fuel blends containing blendstocks of diverse chemical functionalities blended in concentrations up to 20% by volume into BOBs (Blendstocks for Oxygenate Blending). This study focuses on the blendstocks containing functional groups which lack reliable octane number data. The effects on octane sensitivity of blends of cyclopropanes, alkyl carbonates, oxiranes, alcohols, ketones, esters, nitrogen-containing compounds, cyclic and olefinic hydrocarbons, multifunctional materials containing combinations of these functionalities, and the co-blends of the blendstocks with ethanol in a BOB have been evaluated. Octane sensitivity of the blends containing functional groups that haven’t been evaluated before are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimal PMU design based on sampling model and sensitivity analysis

The precise measurements of the synchrophasor and frequency from phasor measurement units (PMUs) are widely used in power grid applications. With the improvement of the technique, applications always require stable dynamic performance and higher accuracy for the synchrophasor and frequency measurements, which is challenging for PMU development. To evaluate the contribution of PMU hardware to measurement accuracy, this paper proposes a general-purpose sampling model to analyze the measurement error. In the proposed sampling model, a strict mathematical derivation is derived, where its error is purely determined by the parameters of the PMU hardware. The sensitivity analysis is carried out by three methods, including mathematical analysis, computer simulation, and variance-based sensitivity analysis. Through the sensitivity analysis, this paper establishes the systematic formulation and the inclusion of synchrophasor, frequency, and ROCOF. Experimental results based on the real-world testbench involving distribution-level PMUs match the mathematical analysis conclusion, which verifies the correctness of the general-purpose sampling model. Furthermore, a strategy for the optimal PMU design is proposed, which could guide PMU design in the future.

42 ENGINEERING↗
DOE OSTI2023

Determination of aquatic hazard concentrations for the oil spill response product class of surface washing agents using species sensitivity distributions

Surface washing agents (SWAs) are a diverse class of oil spill response products intended to facilitate removal of stranded oil from shorelines. This class of agents has high application rates relative to other categories of spill response products, but global toxicity data is generally limited to two standard test species: inland silverside and mysid shrimp. Here, in this study, we provide a framework to maximize the utility of limited toxicity data across a product class. To characterize species sensitivity to SWAs, the toxicity of three agents spanning a range of chemical and physical properties were tested in eight species. The relative sensitivity of mysids shrimp and inland silversides as surrogate test organisms was determined. Toxicity normalized species sensitivity distributions (SSDn) were used to estimate fifth centile hazard concentration (HC5) values for SWAs with limited toxicity data. Chemical toxicity distributions (CTD) of SWA HC5 values were used to compute a fifth centile chemical hazard distribution (HD5) to provide a more comprehensive assessment of hazard across a spill response product class with limited toxicity data than traditional single species or single agent approaches can give.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Verification of the sensitivity and uncertainty-based criticality safety validation techniques: ORNL’s SCALE case study

The use of sensitivity and uncertainty-based techniques has been well-developed in the neutronic community for criticality safety applications. Sensitivity techniques calculate the first-order relative variations in keff due to relative cross-section variations by isotope, reaction type, and incident neutron energy. The result is a vector of sensitivity coefficients which is folded with prior cross-section uncertainties to calculate the so-called similarity indices. Similarity indices are integral quantities which serve as useful mathematical measures for assessing the relevance of existing or new experiments to given application conditions, representing design systems for which the models are to be validated. Despite their importance for a wide range of validation activities, the peer-reviewed literature is non-existent on the development of rigorous verification analysis for their calculated values. Thus, this manuscript proposes a simple non-intrusive method to calculate similarity indices, serving as an independent verification tool for their evaluation. The manuscript derives the theoretical basis for the proposed verification analysis. Next, the verification analysis is applied to the ORNL’s SCALE code suite which employs adjoint-based methods to calculate similarity indices. Numerical experiments using a number of well-known benchmark experiments are employed to verify the calculation of the similarity indices.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Effects of grain size and porosity on cladding failure in high-burnup UO 2 : A sensitivity and uncertainty study

Isotopic taggants are being studied to aid in the provenance assessment of nuclear materials. However, these taggants must be selected such that they do not adversely affect fuel performance during normal operation or accident scenarios. Taggants are known to affect the fuel’s grain size and porosity. In the work described in this paper, the BISON fuel performance code was used to assess the potential effects of taggants (i.e., grain size and porosity) on fuel rod behavior and cladding failure during a high-burnup, large-break loss-of-coolant accident. Here, 281 individual fuel rods from the same reactor core were modeled for a sensitivity study, a parametric study, and uncertainty quantification. The cladding failure predictions often exhibited stochastic behavior. After additional study, it was found that the cladding failure model is highly sensitive to residual error inherent to numerical approximation solvers. Some strategies to mitigate this sensitivity are discussed. The study found no relationship between known taggant effects and cladding failure status. However, taggants were found to affect the time and location of failure in certain rods. In conclusion, future work to continue investigating and validating these findings is briefly discussed.

Doped UO 2↗
DOE OSTI2023

Kernel-based global sensitivity analysis obtained from a single data set

Results from global sensitivity analysis (GSA) often guide the understanding of complicated input–output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In this paper, we develop a new set of kernel GSA tools when only a single set of input–output data is available. Three key advances are made: (1) A new numerical estimator is proposed that demonstrates an empirical improvement over previous procedures. (2) A computational method for generating inner statistical functions from a single data set is presented. (3) A theoretical extension is made to define conditional sensitivity indices, which reveal the degree that the inputs carry shared information about the output when inherent input–input correlations are present. Utilizing these conditional sensitivity indices, a decomposition is derived for the output uncertainty based on what is called the optimal learning sequence of the input variables, which remains consistent when correlations exist between the input variables. Further, while these advances cover a range of GSA subjects, a common single data set numerical solution is provided by a technique known as the conditional mean embedding of distributions. The new methodology is implemented on benchmark systems to demonstrate the provided insights.

42 ENGINEERING↗
DOE OSTI2021

An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis

Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study—including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes—are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.

14 SOLAR ENERGY↗
DOE OSTI2021

Controlling the Morphology of Dynamic Thia-Michael Networks to Target Pressure-Sensitive and Hot Melt Adhesives

A series of multistage (pressure-sensitive/hot melt) adhesives utilizing dynamic thia-Michael bonding motifs are reported. The benzalcyanoacetate Michael acceptors used in this work undergo bond exchange under ambient conditions without external catalysis, facilitating pressure-sensitive adhesion. A key feature of this system is the dynamic reaction-induced phase separation that lends reinforcement to the otherwise weakly bonded materials, enabling weak, repeatable pressure-sensitive adhesion under ambient conditions and strong adhesion when processed as a hot melt adhesive. Furthermore, by using different pairs of benzalcyanoacetate cross-linking units, the phase separation characteristics of the adhesives can be directly manipulated, allowing for a tailored adhesive response.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Novel method to control explosive shock sensitivity: A mesoscale study to understand the effect of thermally expandable microsphere (TEM) inclusions in high explosives (HE) microstructure

When the void content and/or void structure of a high explosive (HE) is altered by some means (i.e., bulk heating or mechanical damage), the shock initiation behavior of the material changes. The ability to precisely predict the change in shock sensitivity after an HE has undergone microstructural changes is a crucial capability in multi-scale reactive flow models. Here, we utilize thermally expandable microspheres (TEMs) as a dopant in a polymer bonded explosive (PBX) matrix to alter the shock initiation properties in a controlled fashion. Here, using a mesoscale modeling approach, we evaluated how a single TEM (before and after thermal expansion) behaves under shock compression, as well as how the matrix PBX in the direct vicinity of the TEM is affected. We first examined the effect of an unexpanded TEM in the explosive matrix and found that its presence does not significantly perturb the bulk flow and by extension will not affect bulk sensitivity. Next, we examined the effect of an expanded TEM and found that its presence significantly perturbs the flow via hydrodynamic jetting, which causes a secondary shock wave with a strength that exceeds that of the incident wave. Finally, we showed that this secondary shock interacts with the downstream porosity to ignite a larger fraction of the overall pore volume, commensurate with the secondary shock strength and the affected volume, increasing the global (bulk) shock sensitivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Soil incubation methods lead to large differences in inferred methane production temperature sensitivity

Abstract Quantifying the temperature sensitivity of methane (CH 4 ) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q 10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q 10 values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model ( ecosys ) to interpret incubation observations and investigate controls on inferred CH 4 production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH 4 production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH 4 production. CH 4 production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q 10 values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q 10 values of CH 4 production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q 10 values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q 10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

On the dual advantage of placing observations through forward sensitivity analysis

The four-dimensional variational data assimilation methodology for assimilating noisy observations into a deterministic model has been the workhorse in forecasting centres for over three decades. While this method provides a computationally efficient framework for dynamic data assimilation, it is largely silent on the important question concerning the minimum number and placement of observations. To answer this question, we demonstrate the dual advantage of placing the observations where the square of the sensitivity of the model solution with respect to the unknown control variables, called forward sensitivities, attains its maximum. By following this approach, we can force the observability Gramian to be of full rank, which in turn guarantees efficient recovery of the optimal values of the control variables, which is the first of the two advantages of this strategy. We further show that the proposed strategy of placing observations has another inherent optimality: the square of the sensitivity of the optimal estimates of the control with respect to the observations (used to obtain these estimates) attains its minimum value, a second advantage that is a direct consequence of the above strategy for placing observations. Our analytical framework and numerical experiments on linear and nonlinear systems confirm the effectiveness of our proposed strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Sensitivity Analysis of a Polyphase Wireless Power Transfer System under Off-Nominal Conditions

Misaligned and/or variable coil airgaps cause coupling coefficient variation in wireless power transfer (WPT) systems, resulting in a decrease in the system’s transmitted power and efficiency. This paper presents sensitivity analyses of a three-phase, Y-Y connected, series-tuned WPT system in the frequency domain in terms of several different electric vehicle wireless charging off-nominal conditions (misalignments in Δ x and Δ y directions, change of airgap Δ z , and the roll Δψ, pitch Δθ, and yaw ΔΦ angles) as specified in Society of Automotive Engineers (SAE) J2954 Standard. Coil inductance matrices were obtained by measuring the self- and mutual inductances of the primary and secondary coil phase windings at variable airgap classes (from 5 cm to 30 cm) for five different charging positions as identified in SAE J2954. These 6 × 6 inductance matrices were used in sensitivity and Plexim/PLECS simulation analyses. The sensitivity of the WPT system was analyzed analytically using the input impedance and phase angle, voltage gain, current gain, quality factor, and coupling coefficient parameters of the series-tuned WPT system. The results were confirmed experimentally on a 50 kW WPT system.

42 ENGINEERING↗
DOE OSTI2022

Substrate availability and not thermal acclimation controls microbial temperature sensitivity response to long-term warming

Microbes are responsible for cycling carbon (C) through soils, and predicted changes in soil C stocks under climate change are highly sensitive to shifts in the mechanisms assumed to control the microbial physiological response to warming. Two mechanisms have been suggested to explain the long-term warming impact on microbial physiology: microbial thermal acclimation and changes in the quantity and quality of substrates available for microbial metabolism. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year-old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationships between temperature sensitivity of physiology (growth, respiration, carbon use efficiency, and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation of microbial respiration, but only in summer, when warming had exacerbated the seasonally-induced, already small dissolved organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon increased the extracellular enzymatic pool and its temperature sensitivity. We propose that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes to decadal warming. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming effects on these soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microbial Interactions Related to N 2 O Emissions and Temperature Sensitivity from Rice Paddy Fields

The soil microbiome is a driver of nitrous oxide (N 2 O) emissions in terrestrial ecosystems. Identifying the core microbiome of N 2 O emissions and its temperature sensitivity from trillions of soil microorganisms is a great challenge and is essential to improving the predictability of soil-climate feedback related to increasing temperature. Here, the integrated soil microbiome covering archaeal, bacterial, fungal, algal, and microfaunal communities was studied to disengage the potential linkage with its N 2 O emissions and its temperature sensitivity in paddy fields by hunting for core species pairs. The results showed that between-group interactions of core bacterial and archaeal members and the within-group interactions of core bacterial members jointly contributed to the N 2 O emissions and its temperature sensitivity. The contribution of between-group interactions (32 to 33%) was greater than that of within groups (10 to 18%). These results suggested that N 2 O emissions and their fluctuations related to climate warming are affected by the within- and between-group interactions of the soil microbiome. Our results help advance the knowledge on the importance of microbial keystone species and network associations in controlling N 2 O production and their responses to increasing temperature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enabling Hyper-Differential Sensitivity Analysis for Ill-Posed Inverse Problems

Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model that must be estimated. However, high dimensionality of the parameters and computational complexity of the PDE solves make such problems challenging. A common approach is to reduce the dimension by fixing some parameters (which we will call auxiliary parameters) to a best estimate and use techniques from PDE-constrained optimization to estimate the other parameters. In this article, hyper-differential sensitivity analysis (HDSA) is used to assess the sensitivity of the solution of the PDE-constrained optimization problem to changes in the auxiliary parameters. Foundational assumptions for HDSA require satisfaction of the optimality conditions which are not always practically feasible as a result of ill-posedness in the inverse problem. Here we introduce novel theoretical and computational approaches to justify and enable HDSA for ill-posed inverse problems by projecting the sensitivities on likelihood informed subspaces and defining a posteriori updates. Our proposed framework is demonstrated on a nonlinear multiphysics inverse problem motivated by estimation of spatially heterogeneous material properties in the presence of spatially distributed parametric modeling uncertainties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensitivity analysis of small modular reactor mPower with ATHLET

Small modular reactor (SMR) possesses the characteristics of less radioactive source term, simpler structure, modular design and features like passive safety as well as low risk of proliferation. Developed and designed by Babcock and Wilcox, the mPower reactor is one of pronouncing small modular reactors that adopts the integrated design of the pressurized water reactor, steam generator and a built-in internal control rod drive mechanism. In this paper, the code ATHLET (Analysis of fossil-hydraulics of Leaks and Transients) is used to model the mPower reactor. At the same time, the sensitivity analysis of the core structure and geometric parameters of the pipeline loop, as well as the steady-state simulation and post-exothermic simulation are carried out depending on the given scenarios. In addition, the sensitivity analysis of pressure, temperature and mass flow in the secondary loop is performed. The results of this study will mitigate some of the frequent misunderstanding in the field and will help to better understand the design of SMR and provide basic research data for further research. However, there are limitations in the current research. Considering that the mPower is still in the conceptual design, the established model is only based on the limited public data while detailed parameters are still in myth, which leads to certain level of uncertainties in the calculation. Besides, the research is mainly on the influence of sensitivity analysis under steady state conditions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗