Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines,the formation of hydrates is highly probable,leading to uncontrolled movement and aggregation of hydrates.The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines,resulting in production reduction,shutdowns,and pressure build-ups.Consequently,a cascade of risks is prone to occur.To address this issue,this study focuses on the operational process of natural gas gathering and transmission pipelines,where a comprehensive framework is established.This framework includes theoretical models for pipeline temperature distribution,pipeline pressure distribution,multiphase flow within the pipeline,hydrate blockage,and numerical solution methods.By analyzing the influence of inlet temperature,inlet pressure,and terminal pressure on hydrate formation within the pipeline,the sensitivity patterns of hydrate blockage risks are derived.The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate,potentially mitigating pipeline blockage during natural gas transportation.Furthermore,an increase in inlet temperature and terminal pressure,and a decrease in inlet pressure,results in a displacement of the most probable location for hydrate blockage towards the terminal station.However,it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system,contradicting the operational goal of energy efficiency and reduction of energy consumption.Consequently,for high-pressure gathering pipelines,measures such as raising the inlet temperature or employing inhibitors,electrical heat tracing,and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation.Moreover,considering abnormal conditions such as gas well production and pipeline network shutdowns,which could potentially trigger hydrate formation,the installation of methanol injection connectors remains necessary to ensure production safety.

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.

On an isotropic porous solid cylinder:the analytical solution and sensitivity analysis of the pressure

Within this work,we perform a sensitivity analysis to determine the influence of the material input parameters on the pressure in an isotropic porous solid cylinder.We provide a step-by-step guide to obtain the analytical solution for a porous isotropic elastic cylinder in terms of the pressure,stresses,and elastic displacement.We obtain the solution by performing a Laplace transform on the governing equations,which are those of Biot's poroelasticity in cylindrical polar coordinates.We enforce radial boundary conditions and obtain the solution in the Laplace transformed domain before reverting back to the time domain.The sensitivity analysis is then carried out,considering only the derived pressure solution.This analysis finds that the time t,Biot's modulus M,and Poisson's ratio ν have the highest influence on the pressure whereas the initial value of pressure P_(0) plays a very little role.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

Calculations and Sensitivity Analysis of Chlorine-,NO_(x)-,and Bromine-Depleting Cycles of Stratospheric Ozone

This paper presents an engineering system approach using a 2D model of conservation of mass to study the dynamics of ozone and concerned chemical species in the stratosphere.By considering all fourteen photolysis,ozone-generating,and-depleting chemical reactions,the model calculated the transient,spatial changes of ozone under different physical-chemical-radiative conditions.Validation against the measured data demonstrated good accuracy,close match of our model with the observed ozone concentrations at both 20°S and 90°N locations.The deviation in the average concentration was less than 1% and in ozone profiles less than 17%.The impacts of various chlorine-(Cl),nitrogen oxides-(NO_(x)),and bromine-(Br)depleting cycles on ozone concentrations and distribution were investigated.The chlorine catalytic depleting cycle was found to exhibit the most significant impact on ozone dynamics,confirming the key role of chlorine in the problem of ozone depletion.Sensitivity analysis was conducted with levels of 25%,50%,100%,200%,and 400% of the baseline value.The combined cycles(Cl+NO_(x)+Br)showed the most significant influence on ozone behavior.The total ozone abundance above the South Pole could decrease by a small 3%,from 281 DU(Dubson Units)to 273 DU for the 25% level,or by a huge thinning of 60%to 114 DU for the 400% concentration level.When the level of chlorine gases increased beyond 200%,it would cause ozone depletion to a level of ozone hole(below 220 DU).The 2D Ozone Model presented in this paper demonstrates robustness,convenience,efficiency,and executability for analyzing complex ozone phenomena in the stratosphere.

Sensitivity and resistance risk analysis of Didymella bryoniae populations to fluopyram

Fluopyram is an succinate dehydrogenase inhibitors(SDHI)fungicide that has been registered in China to control gummy stem blight(GSB)in watermelons for many years.However,whether the field pathogens of GSB are still sensitive to fluopyram or not is unknown.Therefore,we collected 69 Didymella bryoniae isolates from the fields that usually use fluopyram to control GSB to determine the sensitivity change.The EC_(50)(50%inhibition effect)values of fluopyram against D.bryoniae ranged from 0.0691 to 0.3503μg mL^(–1) and the variation factor was 5.07.The mean EC_(50) value was(0.1579±0.0669)μg mL^(–1) and the curve of sensitivity was unimodal.No resistant strains were found in the isolates,which means that the pathogens were still sensitive to fluopyram.The minimal inhibition concentration(MIC)of fluopyram against D.bryoniae was 3μg mL^(–1).Four low-resistant mutants and two medium-resistant mutants were obtained using fungicide taming and the resistance of mutants could be inherited stably.The growth rate of mutants decreased significantly compared with that of wild-type strains while the biomass of most mutants was similar to that of wild-type strains.The sensitivity of most resistant mutants to various stresses was increased compared with that of wild-type strains.The virulence of mutants receded except for low-resistant mutant XN51FR-1,which had the same lesion area as XN51 on the watermelon leaves.The results indicated that the fitness of resistant mutants was decreased compared with that of wild-type strains.The cross-resistance assay indicated that fluopyram-resistant mutants were positive cross-resistant to all six SDHI fungicides in this test but were still sensitive to fluazinam and tebuconazole.So the resistance risk of D.bryoniae to fluopyram was moderate.In addition,we found that the SdhB gene of low-resistant mutant XN30FR-1 had three new point mutations at positions K258N,A259P,and H277N.Medium-resistant mutant XN52FR-1 showed a mutation at position H277N and other mutants did not have any point mutation.

An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): II. Illustrative Example

This work highlights the unparalleled efficiency of the “nth-Order Function/ Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-FASAM-N) by considering the well-known Nordheim-Fuchs reactor dynamics/safety model. This model describes a short-time self-limiting power excursion in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This nonlinear paradigm model is sufficiently complex to model realistically self-limiting power excursions for short times yet admits closed-form exact expressions for the time-dependent neutron flux, temperature distribution and energy released during the transient power burst. The nth-FASAM-N methodology is compared to the extant “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) showing that: (i) the 1st-FASAM-N and the 1st-CASAM-N methodologies are equally efficient for computing the first-order sensitivities;each methodology requires a single large-scale computation for solving the “First-Level Adjoint Sensitivity System” (1st-LASS);(ii) the 2nd-FASAM-N methodology is considerably more efficient than the 2nd-CASAM-N methodology for computing the second-order sensitivities since the number of feature-functions is much smaller than the number of primary parameters;specifically for the Nordheim-Fuchs model, the 2nd-FASAM-N methodology requires 2 large-scale computations to obtain all of the exact expressions of the 28 distinct second-order response sensitivities with respect to the model parameters while the 2nd-CASAM-N methodology requires 7 large-scale computations for obtaining these 28 second-order sensitivities;(iii) the 3rd-FASAM-N methodology is even more efficient than the 3rd-CASAM-N methodology: only 2 large-scale computations are needed to obtain the exact expressions of the 84 distinct third-order response sensitivities with respect to the Nordheim-Fuchs model’s parameters when applying the 3rd-FASAM-N methodology, while the application of the 3rd-CASAM-N methodology requires at least 22 large-scale computations for computing the same 84 distinct third-order sensitivities. Together, the nth-FASAM-N and the nth-CASAM-N methodologies are the most practical methodologies for computing response sensitivities of any order comprehensively and accurately, overcoming the curse of dimensionality in sensitivity analysis.

Seismic response comparison and sensitivity analysis of pile foundation in liquefiable and non-liquefiable soils

Case history investigations have shown that pile foundations are more critically damaged in liquefiable soils than non-liquefiable soils.This study examines the differences in seismic response of pile foundations in liquefiable and non-liquefiable soils and their sensitivity to numerical model parameters.A two-dimensional finite element(FE)model is developed to simulate the experiment of a single pile foundation centrifuge in liquefiable soil subjected to earthquake motions and is validated against real-world test results.The differences in soil-pile seismic response of liquefiable and non-liquefiable soils are explored.Specifically,the first-order second-moment method(FOSM)is used for sensitivity analysis of the seismic response.The results show significant differences in seismic response for a soil-pile system between liquefiable and non-liquefiable soil.The seismic responses are found to be significantly larger in liquefiable soil than in non-liquefiable soil.Moreover,the pile bending moment was mainly affected by the kinematic effect in liquefiable soil,while the inertial effect was more significant in non-liquefiable soil.The controlling parameters of seismic response were PGA,soil density,and friction angle in liquefiable soil,while the pile bending moment was mainly controlled by PGA,the friction angle of soil,and shear modulus of loose sand in non-liquefiable soil.

Sensitivity Analysis of Multi-phase Seepage Parameters Affecting the Clayey Silt Hydrate Reservoir Productivity in the Shenhu Area,South China Sea

Natural gas hydrate(NGH)is an important future resource for the 21st century and a strategic resource with potential for commercial development in the third energy transition.It is of great significance to accurately predict the productivity of hydrate-bearing sediments(HBS).The multi-phase seepage parameters of HBS include permeability,porosity,which is closely related to permeability,and hydrate saturation,which has a direct impact on hydrate content.Existing research has shown that these multi-phase seepage parameters have a great impact on HBS productivity.Permeability directly affects the transmission of pressure-drop and discharge of methane gas,porosity and initial hydrate saturation affect the amount of hydrate decomposition and transmission process of pressure-drop,and also indirectly affect temperature variation of the reservoir.Considering the spatial heterogeneity of multi-phase seepage parameters,a depressurization production model with layered heterogeneity is established based on the clayey silt hydrate reservoir at W11 station in the Shenhu Sea area of the South China Sea.Tough+Hydrate software was used to calculate the production model;the process of gas production and seepage parameter evolution under different multi-phase seepage conditions were obtained.A sensitivity analysis of the parameters affecting the reservoir productivity was conducted so that:(a)a HBS model with layered heterogeneity can better describe the transmission process of pressure and thermal compensation mechanism of hydrate reservoir;(b)considering the multi-phase seepage parameter heterogeneity,the influence degrees of the parameters on HBS productivity were permeability,porosity and initial hydrate saturation,in order from large to small,and the influence of permeability was significantly greater than that of other parameters;(c)the production potential of the clayey silt reservoir should not only be determined by hydrate content or seepage capacity,but also by the comprehensive effect of the two;and(d)time scales need to be considered when studying the effects of changes in multi-phase seepage parameters on HBS productivity.

Numerical modeling and parametric sensitivity analysis of heat transfer and two-phase oil and water flow characteristics in horizontal and inclined flowlines using OpenFOAM

Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Prediction and sensitivity analysis of the surface quality in Ni60/WC coatings

For laser cladding a large temperature gradient easily weakened the surface quality by generating cracks and irregular coating surfaces,which in turn affected the bearing capacity and corrosion resistance of coatings in the rapid heating and cooling process.The response surface methodology(RSM)was used to predict coating cracks by changing the powder ratio,energy density,and preheating temperature,which obtained the relevant mathematical model.After that,the sensitivity of the crack length to process parameters was analyzed based on the sensitivity analysis method.The effect of Ni60/WC composite powder process parameters on the surface quality was revealed in laser cladding.The crack length first decreased and then increased,and the Smooth decreased with the increased powder ratio.The crack length and Smooth increased with the increased energy density.The crack length decreased and Smooth increased with the increased preheating temperature.Sensitivity analysis showed that the crack length and Smooth were the most sensitive to the powder ratio.Therefore,the process parameters were reasonably selected to control the surface quality.The mathematical model and sensitivity analysis method in the work could improve the surface quality,which provided a theoretical basis for the prediction and control of laser cladding cracks.

近红外无创血糖浓度的Label Sensitivity算法和支持向量机回归

近红外光谱分析技术在生物医学工程领域具有广阔应用前景。无创且持续性地测量能实时监控人体血糖水平,给糖尿病患者带来极大便利性、提高生存质量、降低糖尿病并发症发生率具有很大的社会效益。无创血糖监测的想法提出较早,但仍然存在预测精度低、预测值与标签值相关性不高等难点,至今没有达到临床要求。近年来,光谱检测技术发展迅猛且机器学习技术在智能信息处理方面具有明显优势,两者结合可以有效提高人体无创血糖医学监测模型的精度和普适性。提出了一种标签敏感度算法(LS),并结合支持向量机方法建立了人体血糖含量预测模型。使用近红外光谱仪采集了4名志愿者食指处动态血液光谱数据(每名志愿者28组数据),并使用多元散射矫正(MSC)方法消除了部分光散射的影响。考虑血糖对不同波长光的吸收有差异,提出了基于血糖浓度标签差的特征波长挑选方法,并构建了标签敏感度支持向量机(LSSVR)预测模型。设计实验,对比该模型与偏最小二乘回归(PLSR)和区分度支持向量机(FSSVR)算法。结果表明,LS算法的最佳特征波长数为32,经特征波长选择后的LSSVR表现最佳,其均方误差降低至0.02 mmol·L^(-1),明显优于全谱段PLSR模型,血糖浓度的预测值与标签值的相关系数提升至99.8%,预测值全部位于可容许误差的克拉克网格A区内。LSSVR模型的优异表现为早日实现血糖的无创监测提供了新思路。

Sensitivity analysis of spacecraft in micrometeoroids and orbital debris environment based on panel method

To reduce the risk of mission failure caused by the MM/OD impact of the spacecraft,it is necessary to optimize the design of the spacecraft.Spacecraft survivability assessment is the key technology in the optimal design of spacecraft.Spacecraft survivability assessment includes spacecraft impact sensitivity analysis and spacecraft component vulnerability analysis under MM/OD environment.The impact sensitivity refers to the probability of a spacecraft encountering an MM/OD impact while in orbit.Vulnerability refers to the probability that each component of a spacecraft may fail or malfunction when impacted by space debris.Yet this paper mainly analyzes the impact sensitivity and proposes a spacecraft sensitivity assessment method under the MM/OD environment based on a panel method.Under this panel method,a spacecraft geometric model is discretized into small panels,and whether they are impacted by MM/OD or not is determined through the analysis of the shielding or shadowing relationships between panels.The number of impacts on each panel is obtained through calculation,and accordingly the probability of each spacecraft component encountering MM/OD impact can be acquired,thus generating the impact sensibility.This paper extracts data from the NASA’s ORDEM2000,the ESA’s MASTER8 as well as the SDEEM2015(Space Debris Environmental Engineering Model developed by HIT),and uses the PCHIP(Piecewise Cubic Hermite Interpolating Polynomial)method to interpolate and fit the size-flux relationship of space debris.Compared with linear interpolation and cubic spline interpolation,the fitting results through the method are relatively more accurate.The feasibility of this method is also demonstrated through two actual examples shown in this paper,whose results are close to those from ESABASE,although there are some minor errors mainly due to different debris data input.Through the cross-check by three risk assessment software-BUMPER,MDPANTO and MODAOST-under standard operating conditions,the feasibility of this method is again verified.

A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application

Simulations and predictions using numerical models show considerable uncertainties,and parameter uncertainty is one of the most important sources.It is impractical to improve the simulation and prediction abilities by reducing the uncertainties of all parameters.Therefore,identifying the sensitive parameters or parameter combinations is crucial.This study proposes a novel approach:conditional nonlinear optimal perturbations sensitivity analysis(CNOPSA)method.The CNOPSA method fully considers the nonlinear synergistic effects of parameters in the whole parameter space and quantitatively estimates the maximum effects of parameter uncertainties,prone to extreme events.Results of the analytical g-function test indicate that the CNOPSA method can effectively identify the sensitivity of variables.Numerical results of the theoretical five-variable grassland ecosystem model show that the maximum influence of the simulated wilted biomass caused by parameter uncertainty can be estimated and computed by employing the CNOPSA method.The identified sensitive parameters can easily change the simulation or prediction of the wilted biomass,which affects the transformation of the grassland state in the grassland ecosystem.The variance-based approach may underestimate the parameter sensitivity because it only considers the influence of limited parameter samples from a statistical view.This study verifies that the CNOPSA method is effective and feasible for exploring the important and sensitive physical parameters or parameter combinations in numerical models.