The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.

Generalized nth-Order Perturbation Method Based on Loop Subdivision Surface Boundary Element Method for Three-Dimensional Broadband Structural Acoustic Uncertainty Analysis

In this paper,a generalized nth-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems.The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field,and the nth-order discretization formulation of the boundary integral equation is derived.In addition,the computation of loop subdivision surfaces and the subdivision rules are introduced.In order to confirm the effectiveness of the algorithm,the computed results are contrasted and analyzed with the results under Monte Carlo simulations(MCs)through several numerical examples.

Research Progress and Prospect of Tourism Carbon Emissions and Its Uncertainty Analysis Under the Goal of Low-Carbon Scenic Spots

Under the dual carbon goal,China Certified Emissions Reductions(CCER)and the national carbon market have become important means of emission reduction and control.The tourism industry is a strategic pillar industry of China’s national economy,and scenic spots are the main sites of tourism activities.Research on carbon emissions in scenic spots is of great significance for the construction of low-carbon scenic spots and the realization of the dual carbon goal.In this paper,the research on carbon emissions in tourism is reviewed,the current research progress is discussed,and further prospects are made.The research on tourism carbon emissions in China has a good foundation and achieved certain results.However,there are few studies on micro-scales such as scenic spots.The statistical data caliber and measurement methods of carbon emissions are not uniform,and there is a general lack of uncertainty analysis.Future research should focus on building a multi-spatial dimension research system,unifying the statistical caliber and measurement methods of carbon emission data,increasing uncertainty analysis,and ensuring the robustness of research results.

Mixed uncertainty analysis of polycyclic aromatic hydrocarbon inhalation and risk assessment in ambient air of Beijing

This article presents the application of an integrated method that estimates the dispersion of polycyclic aromatic hydrocarbons （PAHs） in air, and assesses the human health risk associated with PAHs inhalation. An uncertainty analysis method consisting of three components were applied in this study, where the three components include a bootstrapping method for analyzing the whole process associated uncertainty, an inhalation rate （IR） representation for evaluating the total PAH inhalation risk for human health, and a normally distributed absorption fraction （AF） ranging from 0% to 100% to represent the absorption capability of PAHs in human body. Using this method, an integrated process was employed to assess the health risk of the residents in Beijing, China, from inhaling PAHs in the air. The results indicate that the ambient air PAHs in Beijing is an important contributor to human health impairment, although over 68% of residents seem to be safe from daily PAH carcinogenic inhalation. In general, the accumulated daily inhalation amount is relatively higher for male and children at 10 years old of age than for female and children at 6 years old. In 1997, about 1.73% cancer sufferers in Beijing were more or less related to ambient air PAHs inhalation. At 95% confidence interval, approximately 272-309 individual cancer incidences can be attributed to PAHs pollution in the air. The probability of greater than 500 cancer occurrence is 15.3%. While the inhalation of ambient air PAHs was shown to be an important factor responsible for higher cancer occurrence in Beijing, while the contribution might not be the most significant one.

Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

Uncertainty Analysis for Ship-Bank Interaction Tests in A Circulating Water Channel

This paper presents a systematic model test program to assess the uncertainty of the ship-bank interaction forces,using the planar motion mechanism(PMM)system in a circulating water channel(CWC).Therefore,the uncertainties due to ship-bank distance and water depth are considered,and they are calculated via the partial differentials of the regression formulae based on the test data.The general part of the uncertainty analysis(UA)is performed according to the ITTC recommended procedure 7.5-02-06.04,while the uncertainty of speed is identified as the bias limit due to the flow velocity maldistribution in the CWC.In each example test for the UA of ship-bank interaction forces,12 repeated measurements were conducted.Results from the UA show that the contribution of water depth error and flow velocity maldistribution to the total uncertainty is noticeable,and the paper explains how they increase with the change of the test conditions.The present study will be useful in understanding the uncertainty regarding the ship-bank interaction force measurement in a CWC.

Application of SWAT Model to the Olifants Basin: Calibration, Validation and Uncertainty Analysis

The application of the Soil and Water Assessment Tool (SWAT) to the Olifants Basin in South Africa was the focus of our study with emphasis on calibration, validation and uncertainty analysis. The Basin was discretized into 23 sub-basins and 226 Hydrologic Response Units (HRUs) using 3 arc second (90 m × 90 m) pixel resolution SRTM DEM with stream gauge B7H015 as the Basin outlet. Observed stream flow data at B7H015 were used for model calibration (1988-2001) and validation (2002-2013) using the split sample approach. Relative global sensitivity analysis using SUFI-2 algorithm was used to determine sensitive parameters to stream flow for calibration of the model. Performance efficiency of the Olifants SWAT model was assessed using Nash-Sutcliffe (NSE), coefficient of determination (R2), Percent Bias (PBIAS) and Root Mean Square Error-Observation Standard deviation Ratio (RSR). Sensitivity analysis revealed in decreasing order of significance, runoff curve number (CN2), alpha bank factor (ALPHA_BNK), soil evaporation compensation factor (ESCO), soil available water capacity (SOIL_AWC, mm H2O/mm soil), groundwater delay (GW_ DELAY, days) and groundwater “revap” coefficient (GW_REVAP) to be the most sensitive parameters to stream flow. Analysis of the model during the calibration period gave the following statistics;NSE = 0.88;R2 = 0.89;PBIAS = -11.49%;RSR = 0.34. On the other hand, statistics during the validation period were NSE = 0.67;R2 = 0.79;PBIAS = -20.69%;RSR = 0.57. The observed statistics indicate the applicability of the SWAT model in simulating the hydrology of the Olifants Basin and therefore can be used as a Decision Support Tool (DST) by water managers and other relevant decisions making bodies to influence policy directions on the management of watershed processes especially water resources.

Uncertainty Analysis and Optimization of Quasi-Zero Stifness Air Suspension Based on Polynomial Chaos Method

To improve the vibration isolation performance of suspensions,various new structural forms of suspensions have been proposed.However,there is uncertainty in these new structure suspensions,so the deterministic research cannot refect the performance of the suspension under actual operating conditions.In this paper,a quasi-zero stifness isolator is used in automotive suspensions to form a new suspension−quasi-zero stifness air suspension(QZSAS).Due to the strong nonlinearity and structural complexity of quasi-zero stifness suspensions,changes in structural parameters may cause dramatic changes in suspension performance,so it is of practical importance to study the efect of structural parameter uncertainty on the suspension performance.In order to solve this problem,three suspension structural parameters d_(0),L_(0) and Pc_(0) are selected as random variables,and the polynomial chaos expansion(PCE)theory is used to solve the suspension performance parameters.The sensitivity of the performance parameters to diferent structural parameters was discussed and analyzed in the frequency domain.Furthermore,a multi-objective optimization of the structural parameters d_(0),L_(0) and Pc_(0) of QZSAS was performed with the mean and variance of the root-mean-square(RMS)acceleration values as the optimization objectives.The optimization results show that there is an improvement of about 8%−1_(0)%in the mean value and about 4_(0)%−55%in the standard deviation of acceleration(RMS)values.This paper verifes the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems,which provide a reference for the future structural design and optimization of such suspension systems.

Uncertainty analysis of seawater intrusion forecasting

In order to describe the importance of uncertainty analysis in seawater intrusion forecasting and identify the main factors that might cause great differences in prediction results, we analyzed the influence of sea level rise, tidal effect, the seasonal variance of influx, and the annual variance of the pumping rate, as well as combinations of different parameters. The results show that the most important factors that might cause great differences in seawater intrusion distance are the variance of pumping rate and combinations of different parameters. The influence of sea level rise can be neglected in a short-time simulation （ten years, for instance）. Retardation of seawater intrusion caused by tidal effects is obviously important in aquifers near the coastline, but the influence decreases with distance away from the coastline and depth away from the seabed. The intrusion distance can reach a dynamic equilibrium with the application of the sine function for seasonal effects of influx. As a conclusion, we suggest that uncertainty analysis should be considered in seawater intrusion forecasting, if possible.

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool （SWAT） was implemented in a small forested watershed of the Soan River Basin innorthern Pakistan through application of the sequential uncertainty fitting （SUFI-2） method to investigate the associateduncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period （1991–2000） with aninitial 4-year warm-up period （1987–1990）, and was validated for the subsequent 10-year period （2001–2010）. Themodel evaluation indices R2 （the coefficient of determination）, NS （the Nash-Sutcliffe efficiency）, and PBIAS （percentbias） for stream flows simulation indicated that there was a good agreement between the measured and simulated flows.To assess the uncertainty in the model outputs, p-factor （a 95% prediction uncertainty, 95PPU） and r-factors （averagewideness width of the 95PPU band divided by the standard deviation of the observed values） were taken into account.The 95PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factorwas 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients（R2 and NS） for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78and 0.74, respectively. Meanwhile, the 95PPU covered more than 60% of the observed sediment data during calibrationand validation. Moreover, improved model prediction and parameter estimation were observed with the increasednumber of iterations. However, the model performance became worse after the fourth iterations due to an unreasonableparameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertaintyduring the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessmentof water balance components, climate change studies, and land use management practices.

Third Order Adjoint Sensitivity and Uncertainty Analysis of an OECD/NEA Reactor Physics Benchmark: III. Response Moments

The (180)3 third-order mixed sensitivities of the leakage response of a polyethylene-reflected plutonium (PERP) experimental benchmark with respect to the benchmark’s 180 microscopic total cross sections have been computed in accompanying works [1] [2]. This work quantifies the contributions of these (180)3 third-order mixed sensitivities to the PERP benchmark’s leakage response distribution moments (expected value, variance and skewness) and compares these contributions to those stemming from the corresponding first- and second-order sensitivities of the PERP benchmark’s leakage response with respect to the total cross sections. The numerical results obtained in this work reveal that the importance of the 3rd-order sensitivities can surpass the importance of the 1st- and 2nd-order sensitivities when the parameters’ uncertainties increase. In particular, for a uniform standard deviation of 10% of the microscopic total cross sections, the 3rd-order sensitivities contribute 80% to the response variance, whereas the contribution stemming from the 1st- and 2nd-order sensitivities amount only to 2% and 18%, respectively. Consequently, neglecting the 3rd-order sensitivities could cause a very large non-conservative error by under-reporting the response variance by a factor of 506%. The results obtained in this work also indicate that the effects of the 3rd-order sensitivities are to reduce the response’s skewness in parameter space, rendering the distribution of the leakage response more symmetric about its expected value. The results obtained in this work are the first such results ever published in reactor physics. Since correlations among the group-averaged microscopic total cross sections are not available, only the effects of typical standard deviations for these cross sections could be considered. Due to this lack of correlations among the cross sections, the effects of the mixed 3rd-order sensitivities could not be quantified exactly at this time. These effects could be quantified only when correlations among the group-averaged microscopic total cross sections would be obtained experimentally by the nuclear physics community.

Calibration chain design based on integrating sphere transfer radiometer for SI-traceable on-orbit spectral radiometric calibration and its uncertainty analysis

In order to satisfy the requirement of SI-traceable on-orbit absolute radiation calibration transfer with high accuracy for satellite remote sensors,a transfer chain consisting of a fiber coupling monochromator（FBM） and an integrating sphere transfer radiometer（ISTR） was designed in this paper.Depending on the Sun,this chain based on detectors provides precise spectral radiometric calibration and measurement to spectrometers in the reflective solar band（RSB） covering 300–2500 nm with a spectral bandwidth of 0.5–6 nm.It shortens the traditional chain based on lamp source and reduces the calibration uncertainty from 5% to 0.5% by using the cryogenic radiometer in space as a radiometric benchmark and trap detectors as secondary standard.This paper also gives a detailed uncertainty budget with reasonable distribution of each impact factor,including the weak spectral signal measurement with uncertainty of 0.28%.According to the peculiar design and comprehensive uncertainty analysis,it illustrates that the spectral radiance measurement uncertainty of the ISTR system can reach to 0.48%.The result satisfies the requirements of SI-traceable on-orbit calibration and has wider significance for expanding the application of the remote sensing data with high-quality.

Uncertainty Analysis on Electric Power Consumption

The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields.However,unpredictable phenomena,such as extreme weather events or the novel coronavirus 2019(COVID-19)outbreak,can greatly limit the ability of time-series analyses to establish reliable patterns.The present work addresses this issue by applying uncertainty analysis using a probability distribution function,and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul,South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic process automation.We first apply Facebook Prophet for conducting time-series analysis.The results demonstrate that the COVID19 outbreak seriously compromised the reliability of the time-series analysis.Then,machine learning models are developed in the TensorFlow framework for conducting uncertainty analysis based on modeled relationships between electric power consumption and outdoor temperature.The benefits of the proposed uncertainty analysis for predicting the electricity consumption of the hotel building are demonstrated by comparing the results obtained when considering no uncertainty,aleatory uncertainty,epistemic uncertainty,and mixed aleatory and epistemic uncertainty.The minimum and maximum ranges of predicted electricity consumption are obtained when using mixed uncertainty.Accordingly,the application of uncertainty analysis using a probability distribution function greatly improved the predictive power of the analysis compared to time-series analysis.

Evolution-Based Uncertainty Analysis for Incline Hoist

The effect of uncertainty and its evolution with time on the incline hoist reliability are investigated in this paper.The performance of incline hoist is changed over time and gradually degraded.The degradation will influence the safe usage and reliability of incline hoist.Degradation process can be described by stochastic process.The degradation process of incline hoist is modeled in geometric Brownian motions(GBM),and the drift rate and diffusion rate of this process can reflect the failure extent and fluctuation of the system.Evolution-based uncertainty analysis(EBUA)method is proposed to describe the dynamic reliability of the incline hoist,and the system of incline hoist can be designed with the specified reliability value at the given time.

An Uncertainty Analysis Method for Artillery Dynamics with Hybrid Stochastic and Interval Parameters

This paper proposes a non-intrusive uncertainty analysis method for artillery dynamics involving hybrid uncertainty using polynomial chaos expansion(PCE).The uncertainty parameters with sufficient information are regarded as stochastic variables,whereas the interval variables are used to treat the uncertainty parameters with limited stochastic knowledge.In this method,the PCE model is constructed through the Galerkin projection method,in which the sparse grid strategy is used to generate the integral points and the corresponding integral weights.Through the sampling in PCE,the original dynamic systems with hybrid stochastic and interval parameters can be transformed into deterministic dynamic systems,without changing their expressions.The yielded PCE model is utilized as a computationally efficient,surrogate model,and the supremum and infimum of the dynamic responses over all time iteration steps can be easily approximated through Monte Carlo simulation and percentile difference.A numerical example and an artillery exterior ballistic dynamics model are used to illustrate the feasibility and efficiency of this approach.The numerical results indicate that the dynamic response bounds obtained by the PCE approach almost match the results of the direct Monte Carlo simulation,but the computational efficiency of the PCE approach is much higher than direct Monte Carlo simulation.Moreover,the proposed method also exhibits fine precision even in high-dimensional uncertainty analysis problems.

Uncertainty and sensitivity analysis of in-vessel phenomena under severe accident mitigation strategy based on ISAA-SAUP program

The phenomenology involved in severe accidents in nuclear reactors is highly complex.Currently,integrated analysis programs used for severe accident analysis heavily rely on custom empirical parameters,which introduce considerable uncertainty.Therefore,in recent years,the field of severe accidents has shifted its focus toward applying uncertainty analysis methods to quantify uncertainty in safety assessment programs,known as“best estimate plus uncertainty(BEPU).”This approach aids in enhancing our comprehension of these programs and their further development and improvement.This study concentrates on a third-generation pressurized water reactor equipped with advanced active and passive mitigation strategies.Through an Integrated Severe Accident Analysis Program(ISAA),numerical modeling and uncertainty analysis were conducted on severe accidents resulting from large break loss of coolant accidents.Seventeen uncertainty parameters of the ISAA program were meticulously screened.Using Wilks'formula,the developed uncertainty program code,SAUP,was employed to carry out Latin hypercube sampling,while ISAA was employed to execute batch calculations.Statistical analysis was then conducted on two figures of merit,namely hydrogen generation and the release of fission products within the pressure vessel.Uncertainty calculations revealed that hydrogen production and the fraction of fission product released exhibited a normal distribution,ranging from 182.784 to 330.664 kg and from 15.6 to 84.3%,respectively.The ratio of hydrogen production to reactor thermal power fell within the range of 0.0578–0.105.A sensitivity analysis was performed for uncertain input parameters,revealing significant correlations between the failure temperature of the cladding oxide layer,maximum melt flow rate,size of the particulate debris,and porosity of the debris with both hydrogen generation and the release of fission products.

Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance

Inevitable geometric variations significantly affect the performance of turbines or even that of entire engines;thus,it is necessary to determine their actual characteristics and accurately estimate their impact on performance.In this study,based on 1781 measured profiles of a typical turbine blade,the statistical characteristics of the geometric variations and the uncertainty impact are analyzed,and some commonly used uncertainty modelling methods based on Principal-Component Analysis(PCA)are verified.The geometric variations are found to be evident,asymmetric,and non-uniform,and the non-normality of the random distributions is non-negligible.The performance is notably affected,which is manifested as an overall offset,a notable scattering,and significant deterioration in several extreme cases.Additionally,it is demonstrated that the PCA reconstruction model is effective in characterizing major uncertainty characteristics of the geometric variations and their impact on the performance with almost the first 10 PCA modes.Based on a reasonable profile error and mean geometric deviation,the Gaussian assumption and stochasticprocess-based model are also found to be effective in predicting the mean values and standard deviations of the performance variations.However,they fail to predict the probability of some extreme cases with high loss.Finally,a Chi-square-based correction model is proposed to compensate for this deficiency.The present work can provide a useful reference for uncertainty analysis of the impact of geometric variations,and the corresponding uncertainty design of turbine blades.

Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer

Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties efficiently and reliably,this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer.The comprehensive implementation process of the proposed model was described with an illustrative CFRD example.First,the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy.Afterwards,the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters.This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers.Overall,the developed model effectively identified the random parameters of rockfill materials.This study provided scientific references for uncertainty analysis of CFRDs.In addition,the proposed method can be applied to other similar engineering structures.

Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D

Urban floods are becoming increasingly more frequent,which has led to tremendous economic losses.The application of inundation modeling to predict and simulate urban flooding is an effective approach for disaster prevention and risk reduction,while also addressing the uncertainty problem in the model is always a challenging task.In this study,a cellular automaton(CA)-based model combining a storm water management model(SWMM)and a weighted cellular automata 2D inundation model was applied and a physical-based model(LISFLOOD-FP)was also coupled with SWMM for comparison.The simulation performance and the uncertainty factors of the coupled model were systematically discussed.The results show that the CA-based model can achieve sufficient accuracy and higher computational efficiency than can a physical-based model.The resolution of terrain and rainstorm data had a strong influence on the performance of the CA-based model,and the simulations would be less creditable when using the input data with a terrain resolution lower than 15 m and a recorded interval of rainfall greater than 30 min.The roughness value and model type showed limited impacts on the change of inundation depth and occurrence of the peak inundation area.Generally,the CA-based coupled model demonstrated laudable applicability and can be recommended for fast simulation of urban flood episodes.This study also can provide references and implications for reducing uncertainty when constructing a CA-based coupled model.

Uncertainty analysis and design optimization of hybrid rocket motor powered vehicle for suborbital flight

Abstract In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor （HRM） powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity anal- ysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization （DDO） and uncertainty-based design optimization （UDO） are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation （KMCS） and Kriging-based Taylor series approximation （KTSA）, are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods： DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.