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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Improving the Sensitivity of Mass Spectrometry by Using a New Sheath Flow Electrospray Emitter Array at Subambient Pressures

Arrays of chemically etched emitters with individualized sheath gas capillaries have been developed to enhance electrospray ionization (ESI) at subambient pressures. By including an emitter array in a subambient pressure ionization with nanoelectrospray (SPIN) source, ionization and transmission efficiency can be maximized allowing for increased sensitivity in mass spectrometric analyses. The SPIN source eliminates the major ion losses at conventional ESI-mass spectrometry (MS) interface by placing the emitter in the first vacuum region of the instrument. To facilitate stable electrospray currents in such conditions we have developed an improved emitter array with individualized sheath gas around each emitter. The utility of the new emitter arrays for generating stable multi-electrosprays at subambient pressures was probed by coupling the emitter array/SPIN source with a time of flight (TOF) mass spectrometer. The instrument sensitivity was compared between single emitter/SPIN-MS and multi-emitter/SPIN-MS configurations using an equimolar solution of 9 peptides. An increase in sensitivity correlative to the number of emitters in the array was observed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Chapter 5: Modulation Excitation Spectroscopy with Phase-Sensitive Detection for Surface Analysis

Advancements in in situ spectroscopic techniques have led to significant progress being made in elucidating heterogeneous reaction mechanisms. The potential of these progressive methods is often limited only by the complexity of the system and noise in the data. Short-lived intermediates can be challenging, if not impossible, to identify with conventional spectra analysis means. Often equally difficult is separating signals that arise from active and inactive species. Modulation excitation spectroscopy combined with phase-sensitive detection analysis is a powerful tool for removing noise from the data while simultaneously revealing the underlying kinetics of the reaction. A stimulus is applied at a constant frequency to the reaction system, for example, a reactant cycled with an inert phase. Through mathematical manipulation of the data, any signal contributing to the overall spectra but not oscillating with the same frequency as the stimulus will be dampened or removed. With phase-sensitive detection, signals oscillating with the stimulus frequency but with various lag times are amplified providing valuable kinetic information. In this chapter, some examples are provided from the literature that have successfully used modulation excitation spectroscopy with phase-sensitive detection to uncover previously unobserved reaction intermediates and kinetics. Examples from a broad range of spectroscopic methods are included to provide perspective to the reader.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Environment-sensitivity functions for gross primary productivity in light use efficiency models

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Extension of SCALE/Sampler’s sensitivity analysis

Nuclear data are a major source of uncertainties in reactor physics calculations. The propagation of nuclear data uncertainties to important system responses is instrumental when determining appropriate safety margins in reactor safety analyses. It is also important to understand the major contributors to the observed uncertainties to make recommendations for further measurements and evaluations and aid in the understanding of the studied system. The SCALE code system allows for nuclear data uncertainty analysis based on the random sampling approach as implemented in SCALE’s Sampler sequence. Sampler was recently extended by a sensitivity analysis in terms of the calculation of two correlation-based sensitivity indices. This analysis allows for the identification of the top contributing nuclear reactions to any analyzed output uncertainty. This paper presents the sensitivity indices, along with their interpretation and limitations. It demonstrates the application in an eigenvalue and decay heat analysis for a boiling water reactor fuel assembly.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Sensitization of cancer cells to radiation by selenadiazole derivatives by regulation of ROS-mediated DNA damage and ERK and AKT pathways

Highlights: • Selenadiazole derivatives could be used as an effective and low toxic sensitizer for radiotherapy. • Selenadiazole derivatives enhances radiation-induced growth inhibition on A375 cells through induction of G2/M arrest. • ROS-mediated signaling pathways play important roles in radiosensitization of selenadiazole derivatives. - Abstract: X-ray-based radiotherapy represents one of the most effective ways in treating human cancers. However, radioresistance and side effect remain as the most challenging issue. This study describes the design and application of novel selenadiazole derivatives as radiotherapy sensitizers to enhance X-ray-induced inhibitory effects on A375 human melanoma and Hela human cervical carcinoma cells. The results showed that, pretreatment of the cells with selenadiazole derivatives dramatically enhance X-ray-induced growth inhibition and colony formation. Flow cytometry analysis indicates that the sensitization by selenadiazole derivatives was mainly caused by induction of G2/M cell cycle arrest. Results of Western blotting demonstrated that the combined treatment-induced A375 cells growth inhibition was achieved by triggering reactive oxygen species-mediated DNA damage involving inactivation of AKT and MAPKs. Further investigation revealed that selenadiazole derivative in combination with X-ray could synergistically inhibit the activity of thioredoxin reductase-1 in A375 cells. Taken together, these results suggest that selenadiazole derivatives can act as novel radiosensitizer with potential application in combating human cancers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

The reason for a high Ca2+-sensitivity associated with Arg91Gly substitution in TPM2 gene is the abnormal behavior and high flexibility of tropomyosin during the ATPase cycle

Highlights: • Mutation R91G initiates abnormally high flexibility of β-tropomyosin (TM). • Troponin moves R91G-TM to the blocked position at low Ca{sup 2+}. • This mutation does not alter the ability of troponin to switch off actin monomers. • R91G-TM is located near the open position during the ATPase cycle at low Ca{sup 2+}. • Mutation R91G increases the fraction of rigor myosin heads at low Ca{sup 2+}. Substitution of Arg for Gly residue in 91th position in β-tropomyosin caused by a point mutation in TPM2 gene is associated with distal arthrogryposis, characterized by a high Ca{sup 2+}-sensitivity of myofilament and contracture syndrome. To understand the mechanisms of this defect, we studied multistep changes in mobility and spatial arrangement of tropomyosin, actin and myosin heads during the ATPase cycle in reconstituted ghost fibres, using the polarized fluorescence microscopy. The mutation was shown to markedly decrease the bending stiffness of β-tropomyosin in the thin filaments. In the absence of the myosin heads the mutation did not alter the ability of troponin to shift tropomyosin to the blocked position and to switch actin monomers off at low Ca{sup 2+}. During the ATPase cycle the movement of the mutant tropomyosin is restrained, it is located near the open position, which allows strong binding of the myosin heads to actin even at low Ca{sup 2+}. This may be the reason for both high Ca{sup 2+}-sensitivity and contractures associated with the Arg91Gly mutation. The use of reagents that decrease the Ca{sup 2+}sensitivity of the troponin complex may not be appropriate to restore muscle function in patients with this mutation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Two-stage ignition behavior and octane sensitivity of toluene reference fuels as gasoline surrogate

Current approaches to improve the efficiency of Spark-Ignition (SI) gasoline engines have been focusing on turbocharging, increasing the compression ratio, and pursuing advanced low-temperature combustion concepts. In order to maximize these strategies, it is important to optimize the knock resistance of the fuel, and therefore knowledge of the sensitivity of the ignition process under a wide range of engine operating conditions is required. Octane sensitivity (OS), which is defined as the difference between Research Octane Number (RON) and Motored Octane Number (MON), has been introduced to represent how fuel's ignition reactivity changes relative to the primary reference fuels (n-heptane/iso-octane) within RON/MON conditions. Previous works have indicated that OS is intimately related to low temperature reactivity of the fuel, which can be revealed as two-stage heat release characteristics during an ignition event. Prompted by these findings, in this paper, we investigate the relationship between two-stage ignition behavior and OS, using chemical kinetic simulations of 24 Toluene Reference Fuels (TRFs)/ethanol blends. TRFs are ternary mixtures of n-heptane/iso-octane/toluene, which is capable of capturing aromatic content and positive values of OS of real gasoline fuels. Simulation results show that fuels with weak or no two-stage ignition behavior tend to have high OS, due to their lack of Negative Temperature Coefficient (NTC) effect and high sensitivity in ignition delay time. Leveraging such observations, we develop a correlation between two-stage behavior and OS as an OS prediction method. Two metrics that represent the strength of the two-stage ignition behavior are proposed and used as OS predictors, which are Low Temperature Heat Release percentage (LTHR%) and Heat Release Rate at the end of first stage (HRR inf ) calculated from a simple kinetic simulation. Regression analysis shows a clear trend between decreases in the proposed two-stage behavior metrics and increases in the value of OS of the fuel. We also test the new metric (LTHR%) using simulation results of 0-D reactors with imposed pressure time histories obtained from engine experiments, as well as using different TRF kinetic mechanisms. Here, the results demonstrate the effectiveness of the metric as a representation of the two-stage ignition behavior in practical combustion systems, highlighting the importance of the proposed relationship, and its potential as a simple and effective OS predictor.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Social sensitivity analysis in conflictive environmental governance: A case of forest planning

In environmental governance, there is a need to include tools that analyze the robustness of the assessment processes, as well as the validity of policies and measures. This requires a methodological framework that integrates formal techniques such as sensitivity analysis with what the authors have called social sensitivity analysis. The latter consists of participatory processes in which stakeholders analyze the robustness of the assessment process used as well as the validity of the results of such assessments. This methodology was applied to a procedure for assessing planning alternatives for forest tracks. Sensitivity analysis studies the technical robustness of the results of the assessment, as well as exploring the social validity of these results, thus facilitating processes of dialogue and consensus needed in decision-making in conflictive situations. These results are considered interesting, not only from the perspective of implanting polices, but also as a reference for other places with similar situations.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2016

Investigation of uncertainty in CO 2 reservoir models: A sensitivity analysis of relative permeability parameter values

Numerical reservoir models of CO 2 injection in saline formations rely on parameterization of laboratory-measured pore-scale processes. Here, we have performed a parameter sensitivity study and Monte Carlo simulations to determine the normalized change in total CO 2 injected using the finite element heat and mass-transfer code (FEHM) numerical reservoir simulator. Experimentally measured relative permeability parameter values were used to generate distribution functions for parameter sampling. The parameter sensitivity study analyzed five different levels for each of the relative permeability model parameters. All but one of the parameters changed the CO 2 injectivity by <10%, less than the geostatistical uncertainty that applies to all large subsurface systems due to natural geophysical variability and inherently small sample sizes. The exception was the end-point CO 2 relative permeability, k$0\atop{r}$ CO2 , the maximum attainable effective CO 2 permeability during CO 2 invasion, which changed CO2 injectivity by as much as 80%. Similarly, Monte Carlo simulation using 1000 realizations of relative permeability parameters showed no relationship between CO 2 injectivity and any of the parameters but k$0\atop{r}$ CO2 , which had a very strong (R 2 = 0.9685) power law relationship with total CO 2 injected. Model sensitivity to k$0\atop{r}$ CO2 points to the importance of accurate core flood and wettability measurements.

58 GEOSCIENCES↗
DOE OSTI2014

Grid and basis adaptive polynomial chaos techniques for sensitivity and uncertainty analysis

The demand for accurate and computationally affordable sensitivity and uncertainty techniques is constantly on the rise and has become especially pressing in the nuclear field with the shift to Best Estimate Plus Uncertainty methodologies in the licensing of nuclear installations. Besides traditional, already well developed methods – such as first order perturbation theory or Monte Carlo sampling – Polynomial Chaos Expansion (PCE) has been given a growing emphasis in recent years due to its simple application and good performance. This paper presents new developments of the research done at TU Delft on such Polynomial Chaos (PC) techniques. Our work is focused on the Non-Intrusive Spectral Projection (NISP) approach and adaptive methods for building the PCE of responses of interest. Recent efforts resulted in a new adaptive sparse grid algorithm designed for estimating the PC coefficients. The algorithm is based on Gerstner's procedure for calculating multi-dimensional integrals but proves to be computationally significantly cheaper, while at the same it retains a similar accuracy as the original method. More importantly the issue of basis adaptivity has been investigated and two techniques have been implemented for constructing the sparse PCE of quantities of interest. Not using the traditional full PC basis set leads to further reduction in computational time since the high order grids necessary for accurately estimating the near zero expansion coefficients of polynomial basis vectors not needed in the PCE can be excluded from the calculation. Moreover the sparse PC representation of the response is easier to handle when used for sensitivity analysis or uncertainty propagation due to the smaller number of basis vectors. The developed grid and basis adaptive methods have been implemented in Matlab as the Fully Adaptive Non-Intrusive Spectral Projection (FANISP) algorithm and were tested on four analytical problems. These show consistent good performance both in terms of the accuracy of the resulting PC representation of quantities and the computational costs associated with constructing the sparse PCE. Basis adaptivity also seems to make the employment of PC techniques possible for problems with a higher number of input parameters (15–20), alleviating a well known limitation of the traditional approach. The prospect of larger scale applicability and the simplicity of implementation makes such adaptive PC algorithms particularly appealing for the sensitivity and uncertainty analysis of complex systems and legacy codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

42 ENGINEERING↗
DOE OSTI2017

A nanoscale Zr-based fluorescent metal-organic framework for selective and sensitive detection of hydrogen sulfide

Hydrogen sulfide (H{sub 2}S) has been commonly viewed as a gas signaling molecule in various physiological and pathological processes. However, the highly efficient H{sub 2}S detection still remains challenging. Herein, we designed a new robust nano metal-organic framework (MOF) UiO-66-CH=CH{sub 2} as a fluorescent probe for rapid, sensitive and selective detection of biological H{sub 2}S. UiO-66-CH=CH{sub 2} was prepared by heating ZrCl{sub 4} and 2-vinylterephthalic acid via a simple method. UiO-66-CH=CH{sub 2} displayed fluorescence quenching to H{sub 2}S and kept excellent selectivity in the presence of biological relevant analytes especially the cysteine and glutathione. This MOF-based probe also exhibited fast response (10 s) and high sensitivity with a detection limit of 6.46 μM which was within the concentration range of biological H{sub 2}S in living system. Moreover, this constructed MOF featured water-stability, nanoscale (20–30 nm) and low toxicity, which made it a promising candidate for biological H{sub 2}S sensing. - Graphical abstract: A nanoscale Zr-based fluorescent metal-organic framework was synthesized for selective and sensitive biological detection of hydrogen sulfide. Display Omitted - Highlights: • We designed a new metal-organic framework UiO-66-CH=CH{sub 2} for biological H{sub 2}S sensing. • The responsive time was 10 s and the detection limit was 6.46 μM. • This probe has anti-interference especially from cysteine and glutathione. • This biological H{sub 2}S probe features low cytotoxicity, water-stability and nanoscale.

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