Contribution To Uncertainty Research Articles

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

The effect of intra- and inter-fractional motion on target coverage and margins in proton therapy for uveal melanoma

Introduction.This study aims to assess the effective lateral margin requirements for target coverage in ocular proton therapy (OPT), considering the unique challenges posed by eye motion and hypofractionation. It specifically addresses the previously unaccounted-for uncertainty contribution of intra-fractional motion, in conjunction with setup uncertainties, on dosimetric determination of lateral margin requirements.Method.The methodology integrates dose calculations from the in-house developed treatment planning system OCULARIS with measured intra-fractional motion, patient models from EyePlan and Monte Carlo (MC) sampling of setup uncertainties. The study is conducted on 16 uveal melanoma patients previously treated in the OPTIS2 treatment room at the Paul Scherrer Institute (PSI).Result.The retrospective simulation analysis highlights a significant impact of non-systematic factors on lateral margin requirements in OPT. Simulations indicate that reducing the 2.5 mm clinical lateral margin, represented by a 2.1 mm margin in this work, would have resulted in inadequate target coverage for two patients, revealing a greater impact of non-systematic factors on lateral margin requirements.Conclusions.This work characterizes intra-fractional motion in 16 OPT patients and identifies limitations of clinical margin selection protocols for OPT applications. A novel framework was introduced to assess margin sufficiency for target coverage. The findings suggest that prior research underestimated non-systematic factors and overestimated systematic contributions to lateral margin components. This re-evaluation highlights the critical need to prioritize the management of non-systematic uncertainty contributions in OPT.

Possible measure of soil factors in the Italian seismic code

AbstractThe Italian seismic code provides a simplified approach to account for the effect of local seismostratigraphical configuration on the expected ground motion. This approach, common with other seismic codes, provides specific ‘soil factors’ as a function of a set of reference subsoil conditions (soil classes): these factors are considered in 1D subsoil configurations to modify the uniform probability hazard spectrum deduced from probabilistic seismic hazard at reference soil conditions. It is inferred that, to provide a coherent management of uncertainty affecting the response spectrum to be used for the design, the contribution of uncertainty affecting soil factors must be carefully considered to avoid biases in the hazard evaluation. In the present study, variability of soil factors representative of each soil class has been explored by numerical simulation relative to many seismostratigraphical configurations inferred from seismic microzonation studies available in Italy relative to 1689 municipalities. This analysis shows that variability of soil factors is of the same order of magnitude of variability affecting reference response spectra, which implies that the former cannot be neglected as presently happens in the common practice. It is also shown that neglecting this contribution can lead to underestimate the impact of subsoil configuration on the regularized response spectrum provided by the norm, in particular, in the short period range.

Climate change impacts on seasonal runoff in the source region of the Yellow River: Insights from CORDEX experiments with uncertainty analysis

The source region of the Yellow River (SRYR) plays a crucial role in water resources to downstream areas. This study investigates the potential impacts of climate change on seasonal runoff in the SRYR using an ensemble of Regional Climate Models (RCMs) from two CORDEX experiments under RCP2.6 and RCP8.5 scenarios coupled with the WaSiM hydrological model. Two versions of the WaSiM hydrological model (WaSiM-PM and WaSiM-Hamon) are employed to validate the historical runoff, and WaSiM-PM’s superior performance make it the preferred choice for projecting climate change impacts on runoff. After bias-correction, RCM outputs show significant improvements, with correlation coefficients above 0.99 for temperature and 0.95 for precipitation. Under RCP2.6, temperature is projected to increase gradually (0.01–0.12 °C/decade), while under RCP8.5, warming is more pronounced (0.49–0.74 °C/decade). Precipitation generally shows an increasing trend, with variations between scenarios and experiments. Runoff projections indicate consistent peak flows in July across all scenarios. By the 2090 s under RCP8.5, substantial decreases in runoff are projected, especially in autumn and winter (approaching 40 %), with annual mean runoff decreasing by 30.3 %-31.5 %. Uncertainty analysis reveals that GCMs are the primary source of uncertainty (37–47 %) in the 2040 s, while emission scenarios dominate (43–58 %) in the 2090 s. Hydrological models show seasonal variability in uncertainty contribution, and downscaling experiments also play a significant role. This study highlights the potential for significant decreases in water resources in the SRYR, particularly under high emission scenarios, emphasizing the need for robust adaptation strategies in water resource management.

Uncertainty of evaluation of spectral mismatch correction factor

Abstract Addressing the effect of partially correlated components of spectral irradiance data on spectral integrals poses a substantial challenge. This study employs Monte Carlo methods to conduct an uncertainty analysis of spectral integrals, with a focus on the uncertainty of the spectral mismatch correction factor. The investigation encompasses the primary types of uncertainty contributors of spectral irradiance, based on carefully calibrated FEL lamp measurements. A novel approach is introduced to estimate the impacts of partial correlations among spectral irradiance values at different wavelengths. The findings reveal that uncertainty components arising from partial correlations significantly outweigh those associated with full spectral correlation or complete lack of correlation at different wavelengths. This insight advances our understanding of uncertainty analysis of spectral integrals and underscores the importance of accounting for partial correlations in accurate measurements.

Value assignment and uncertainty evaluation for certified reference gas mixtures

AbstractThe procedures used to assign values to certified reference gas mixtures and to evaluate their associated uncertainties, which are described in ISO 6143, and that were variously improved by Guenther and Possolo (Anal Bioanal Chem 399:489–500, 2011. 10.1007/s00216-010-4379-z), are further enhanced by the following developments: (i) evaluating and propagating uncertainty contributions derived from comparisons with historical reference gas mixtures of similar nominal composition; (ii) recognizing and quantifying mutual inconsistency (dark uncertainty) between primary standard gas mixtures used for calibration; (iii) employing Bayesian procedures for calibration, value assignment, and uncertainty evaluations; and (iv) employing state-of-the-art methods of meta-analysis to combine cylinder-specific measurement results. These developments are illustrated in examples of certification of two gas mixture Standard Reference Materials developed by the National Institute of Standards and Technology (NIST, USA). These examples serve only to demonstrate the methods described in this contribution and do not replace any official measurement results delivered in the certificates of any reference materials developed by NIST.

Calibration of rotary axis angular positioning deviations in a six-axis robotic manipulator by using the R-Test

Prediction accuracy of the kinematic model plays a pivotal role in offline numerical compensation for three-dimensional (3D) positioning error of a six-axis robotic manipulator. In addition to the Denavit-Hartenberg (D-H) error that was typically discussed in the analogous research, this paper proposes a novel scheme to identify the bidirectional angular positioning deviations of all the rotary axes without using the commercial laser tracker. This proposed scheme employs the R-Test, which is significantly more cost-efficient than a laser tracker employed in many past works. Compared to the conventional D-H model, significantly improved prediction performance of the proposed model was experimentally investigated on three rectangular paths covering the entire workspace. Experimental results indicate that the proposed scheme reduces the residual error from 505.2 to 215.2 mm, compared to 118.8 mm with a laser tracker, while maintaining a significantly lower cost that is 1/100 of a commercial laser tracker. Uncertainty analysis also clarify that when only the D-H error parameters are identified, as in many conventional works, the angular positioning deviations can be a major uncertainty contributor to the identification of the D-H errors. The proposed scheme does not require the R-Test locations to be calibrated, and its influence on the uncertainty in the identified errors is assessed.

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A method is described to measure the thermal expansion coefficient of fused quartz glass. The measurement principle is to monitor the change in resonance frequency of a Fabry–Perot cavity as its temperature changes; the Fabry–Perot cavity is made from fused quartz glass. The standard uncertainty in the measurement was less than 0.6 (nm·m-1)·K-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\ extrm{nm}{\\cdot } \ extrm{m}^{-1}){\\cdot }\ extrm{K}^{-1}$$\\end{document}, or 0.15 %. The limit on performance is arguably uncertainty in the reflection phase-shift temperature dependence, because neither thermooptic nor thermal expansion coefficients of thin-film coatings are reliably known. However, several other uncertainty contributors are at the same level of magnitude, and so any improvement in performance would entail significant effort. Furthermore, measurements of three different samples revealed that material inhomogeneity leads to differences in the effective thermal expansion coefficient of fused quartz; inhomogeneity in thermal expansion among samples is 24 times larger than the measurement uncertainty in a single sample.

An efficient protocol for commissioning radiophotoluminescence dosimeters for radiotherapy dosimetry audits

PurposeThe aim of this study was to develop an efficient protocol for the commissioning of 1000 radiophotoluminescence dosimeters (RPLDs) for use in postal dosimetry audits in radiotherapy. This involved the determination of correction factors necessary to reduce measurement uncertainty and ensure accurate dose measurements. MethodsThe commissioning process started with the RPLDs subjected to a series of controlled irradiations to determine their individual nominal response. Experiments were also conducted to assess the influence of irradiation position, reading position, and ambient temperature on the dosimeter readings all which were accounted for calculating individual sensitivity correction factors (SCFs) for each dosimeter. Statistical analysis was performed to evaluate the variability of SCFs depending on normalization group. An additional investigation simulating different dosimetry audit batches was conducted to study the effect of SCF variability on dosimetry audit measurements. ResultsThe adopted commissioning protocol required irradiation position correction factors (0.990–0.996), readout tray position correction factors (0.992–1.01) and room temperature corrections (∼0.4 % per ° C). This enabled the calculation of SCFs for a batch of 1000 RPLDs and the analysis found the majority of SCFs falling within the range of 0.985–1.015. The standard deviations of the SCF distributions were approximately 1% for all normalization groups. It was observed that SCFs normalized to the entire batch of 1000 dosimeters could be effectively used for smaller audit batches, with an additional uncertainty contribution of up to 0.2%. This minimal increase in uncertainty is acceptable within the context of dosimetry audits. ConclusionsThe developed protocol for commissioning RPLDs provides a reliable method for ensuring accurate dose measurements in postal radiotherapy dosimetry audits. The correction factors applied during the commissioning process were thoroughly described to effectively minimize measurement uncertainty. The findings support the use of SCFs normalized to large dosimeter batches for smaller audit groups, thereby streamlining the dosimetry audit process. Future research should focus on the long-term stability of SCFs to further enhance the reliability of RPLD-based dosimetry audits.

Intolerance of uncertainty on distress and impairment: The mediating role of repetitive negative thinking.

Repetitive negative thinking and intolerance of uncertainty are risk and maintenance factors for emotional disorders. Although emerging evidence suggests that intolerance of uncertainty predicts increases in distress through repetitive negative thinking, these relationships have yet to be investigated among veterans. The present study examines if repetitive negative thinking mediates the relationships of intolerance of uncertainty with stress, disordered symptoms and impairment among a mixed clinical sample of veterans. Two hundred and forty-four treatment-seeking veterans with diagnoses of major depressive disorder, panic disorder, or posttraumatic stress disorder completed measures of intolerance of uncertainty, repetitive negative thinking, stress, impairment, depression, panic, and posttraumatic stress prior to receiving treatment. Mediation models revealed indirect effects of intolerance of uncertainty through repetitive negative thinking on stress and impairment in the full sample, and on disordered symptoms in subsamples with major depressive disorder and posttraumatic stress disorder. Conversely, intolerance of uncertainty did not have direct or indirect effects on disordered symptoms in a panic disorder subsample. Findings suggest that repetitive negative thinking and intolerance of uncertainty uniquely contribute to stress, impairment, and disordered symptoms, but repetitive negative thinking, may, in part, drive intolerance of uncertainty's contribution to emotional disorders. Interventions for repetitive negative thinking might improve the efficacy of existing transdiagnostic treatment protocols. Cross-sectional data is a limitation of the present study. Prospective designs in civilian samples can better establish the temporality of these relationships and if they are generalizable to the larger population.

High voltage determination and stabilization for collinear laser spectroscopy applications.

Fast beam collinear laser spectroscopy is the established method to investigate nuclear ground state properties such as the spin, the electromagnetic moments, and the charge radius of exotic nuclei. These are extracted with high precision from atomic observables, i.e., the hyperfine splitting and the isotope shift, which become possible due to a large reduction of the Doppler broadening by compressing the velocity width of the ion beam through electrostatic acceleration. With the advancement of experimental methods and applied devices, e.g., to measure and stabilize the laser frequency, the acceleration potential became the dominant systematic uncertainty contribution. To overcome this, we present a custom-built high-voltage divider, which was developed and tested at the German metrology institute, and a feedback loop that enabled collinear laser spectroscopy to be performed at a 100-kHz level. Furthermore, we describe the impact of field penetration into the laser-ion interaction region. This affects the determined isotope shifts and hyperfine splittings if Doppler tuning is applied, i.e., the ion beam energy is altered instead of scanning the laser frequency. Using different laser frequencies that were referenced to a frequency comb, the field penetration was extracted laser spectroscopically. This allowed us to define an effective scanning potential to still apply the faster and easier Doppler tuning without introducing systematic deviations.

The Absolute Age of NGC 3201 Derived from Detached Eclipsing Binaries and the Hess Diagram

We estimate the absolute age of the globular cluster NGC 3201 using 10,000 sets of theoretical isochrones constructed through Monte Carlo simulation using the Dartmouth Stellar Evolution Program. These isochrones take into consideration the uncertainty introduced by the choice of stellar evolution parameters. We fit isochrones with three detached eclipsing binaries and obtained an age independent of distance. We also fit isochrones with differential reddening corrected Hubble Space Telescope photometry data utilizing two different Hess diagram-based fitting methods. Results from three different methods analyzing two different types of data agree to within 1σ, and we find the absolute age of NGC 3201 = 11.85 ± 0.74 Gyr. We also perform a variable importance analysis to study the uncertainty contribution from individual parameters, and we find the distance is the dominant source of uncertainty in photometry-based analysis, while total metallicity, helium abundance, α-element abundance, mixing length, and treatment of helium diffusion are an important source of uncertainties for all three methods.

Probabilistic Finite Element Analysis of Human Rib Biomechanics: A Framework for Improved Generalizability.

In dynamic impact events, thoracic injuries often involve rib fractures, which are closely related to injury severity. Previous studies have investigated the behavior of isolated ribs under impact loading conditions, but often neglected the variability in anatomical shape and tissue material properties. In this study, we used probabilistic finite element analysis and statistical shape modeling to investigate the effect of population-wide variability in rib cortical bone tissue mechanical properties and rib shape on the biomechanical response of the rib to impact loading. Using the probabilistic finite element analysis results, a response surface model was generated to rapidly investigate the biomechanical response of an isolated rib under dynamic anterior-posterior load given the variability in rib morphometry and tissue material properties. The response surface was used to generate pre-fracture force-displacement computational corridors for the overall population and a population sub-group of older mid-sized males. When compared to the experimental data, the computational mean response had a RMSE of 4.28N (peak force 94N) and 6.11N (peak force 116N) for the overall population and sub-group respectively, whereas the normalized area metric when comparing the experimental and computational corridors ranged from 3.32% to 22.65% for the population and 10.90% to 32.81% for the sub-group. Furthermore, probabilistic sensitivities were computed in which the contribution of uncertainty and variability of the parameters of interest was quantified. The study found that rib cortical bone elastic modulus, rib morphometry and cortical thickness are the random variables that produce the largest variability in the predicted force-displacement response. The proposed framework offers a novel approach for accounting biological variability in a representative population and has the potential to improve the generalizability of findings in biomechanical studies.

Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment

AbstractA significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

Performance and uncertainty assessment of green roofs for urban flood reduction in a high-density catchment in Ahmedabad, India

Urban flooding poses a significant challenge to the rapidly growing Indian cities. Low-impact development strategies such as green roofs have shown the potential to reduce urban flooding. However, their performance assessment significantly varies across different studies. Therefore, the study's primary objective is to evaluate green roofs in the Indian context. For this evaluation, the green roofs are assessed based on building-level implementation scenarios for a high-density urban area in India for 25%,50%, and 75% application rates and different rainfall intensities (2,3 and 4-h duration and 2,5,10 and 25-year frequencies). Secondly, to probe the variations in the green roof performance across studies, uncertainty contributions to the runoff reduction from different parameters are quantified. The results show that green roofs can reduce up to 62% of flood volume and 24% of runoff. However, they are reasonably effective only beyond 25% application rates. Further, rainfall intensity contributes the most to the uncertainty of runoff reduction from green roofs. This uncertainty assessment implies that localized evaluation of green roofs depending on local rainfall conditions is required for city-wide policy planning. The study has a significant contribution to building confidence in the ability of green roofs to reduce urban floods in the context of developing countries like India.

Partitioning Uncertainty in Model Predictions from Compartmental Modeling of Global Carbon Cycle

Our comprehension of the real world remains perpetually incomplete, compelling us to rely on models to decipher intricate real-world phenomena. However, these models, at their pinnacle, serve merely as close approximations of the systems they seek to emulate, inherently laden with uncertainty. Therefore, investigating the disparities between observed system behaviors and model-derived predictions is of paramount importance. Although achieving absolute quantification of uncertainty in the model-building process remains challenging, there are avenues for both mitigating and highlighting areas of uncertainty. Central to this study are three key sources of uncertainty, each exerting significant influence: (i) structural uncertainty arising from inadequacies in mathematical formulations within the conceptual models; (ii) scenario uncertainty stemming from our limited foresight or inability to forecast future conditions; and (iii) input factor uncertainty resulting from vaguely defined or estimated input factors. Through uncertainty analysis, this research endeavors to understand these uncertainty domains within compartmental models, which are instrumental in depicting the complexities of the global carbon cycle. The results indicate that parameter uncertainty has the most significant impact on model outputs, followed by structural and scenario uncertainties. Evident deviations between the observed atmospheric CO2 content and simulated data underscore the substantial contribution of certain uncertainties to the overall estimated uncertainty. The conclusions emphasize the need for comprehensive uncertainty quantification to enhance model reliability and the importance of addressing these uncertainties to improve predictions related to global carbon dynamics and inform policy decisions. This paper employs partitioning techniques to discern the contributions of the aforementioned primary sources of uncertainty to the overarching prediction uncertainty.

Parameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach

The paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters.

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Uncertainty components in profile likelihood fits

When a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful for understanding the contributions to the total uncertainty, but is also required to propagate these contributions in subsequent analyses, such as combinations or interpretation fits including results from other measurements or experiments. In profile likelihood fits, widely applied in high-energy physics analyses, contributions of systematic uncertainties are routinely quantified using “impacts,” which are not adequate for such applications. We discuss the difference between impacts and actual uncertainty components, and establish methods to determine the latter in a wide range of statistical models.