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The robotic live-cube compact storage system can provide a higher storage capacity and more flexible throughput than other automated warehousing systems. The extant studies usually fix the input/output point at the bottom-left of such storage systems and have not investigated other configurations of the input/output point. In this study, we propose six policies for the configuration of the input/output point and evaluate their performance by using an analytical model and simulations. A sensitivity analysis is carried out by considering different shape-related factors, system heights, and energy consumption to explore the optimal configuration of the input/output point. For most input/output point policies, a shape factor (length divided by depth) of 0.5 results in good system performance in terms of retrieval time. When using optimal system dimension, the input/output point located in the upper-middle part along the length and depth performs best compared with other policies. Specifically, this configuration reduces the retrieval time by 40.23% and energy consumption by 42.31% compared with the bottom-left corner input/output point. We find that there exists a trade-off among the cost of setup, number of empty grids, and retrieval time when varying the number of input/output points. [ABSTRACT FROM AUTHOR]

Given the fast growth of available computational capacities and the increasing complexity of simulation models addressing agro-environmental issues, uncertainty analysis using stochastic techniques has become a standard modeling practice. However, conventional uncertainty/sensitivity analysis methods are either computationally demanding (Monte Carlo-based methods) or produce results with varying quality (Gaussian quadratures). In this article, we present a computationally inexpensive and reliable uncertainty analysis method for simulation models called informed rotations of Gaussian quadratures (IRGQ). We also provide an R script that generates IRGQ points based on the required input data. The results demonstrate that this method is able to produce approximations that are close to the estimated benchmarks at low computational costs. The method is tested in three different simulation models using different input data in order to demonstrate the independence of the proposed method on specific model types and data structures. This is a methodological paper for practitioners rather than theorists. [ABSTRACT FROM AUTHOR]

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2k factorial parameter sweep on Uri Wilensky's Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini-coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed. [ABSTRACT FROM AUTHOR]

A literature survey indicated that high impedance fault (HIF) detection methods employ simulation techniques using very low voltage signals generated using software such as PSCAD, and MATLAB, among others. While software platforms are used to derive satisfactory results, small fault current signals cannot be accurately tapped from high transformation ratio current transformers owing to the dominant load currents in real power systems. Therefore, a sensitivity analysis based on a power system background is considered essential. A set of well-known power system equations are used to analyse and select a parameter to tune this sensitivity, from several available parameters, such as source, transformer, line, neutral, and earth impedance. The analysis reveals that when the detection of HIF in the field is challenging, then fault or neutral impedance emerges as an important parameter for tuning the system. To achieve this, certain rigorous mathematical steps are derived to develop a suitable formulation for sensitivity analysis using differential calculus. Based on the analysis, the sensitivity results and tuning parameters are tabulated, and sensitivity dependency curves are plotted. [ABSTRACT FROM AUTHOR]

Tetrahydrofuran (THF) purification by distillation is difficult due to the existence of its homogeneous, minimum boiling azeotrope with water. Previously conducted extractive distillation runs were used in this work to validate a rigorous model. The validated model was then used to arrive at a feasible range of operating parameters by performing sensitivity analysis. It is shown through simulations that with the correct operating parameters, use of dimethyl sulfoxide can help obtain almost pure THF. [ABSTRACT FROM AUTHOR]

This paper attempts to apply both global and local sensitivity analyses (SA) to fully test the integrated activity-based model (ABM) within several experiments based on a Chinese medium-sized city, Baoding. MATSim (Multi-agent Transport Simulation) that is a typical integrated ABM is used as an example for the SA. The global SA, which is based on the elementary effect method, is firstly applied to identify the influential parameters. Then the once-at-a-time (OAT)-based Local SA is employed to further reveal the relationship between the influential parameters and the outputs of interest, such as traffic flow. The SA results show the extent to which and how three influential MATSim parameters (population scaling factor, the number of iterations and time step size) influence the outputs of interest. In addition, the SA results of MATSim suggest that the parameters of time mutation rate and performing utility can heavily influence the outputs of interest and properly setting them can optimize the daily plans of agents. Finally, this paper concludes with suggestions on how to wisely use the SA findings for both MATSim and other ABM users. [ABSTRACT FROM AUTHOR]

In this paper, we report on the use of Bayesian networks, BNs, learnt from data generated by physical and numerical models, to overcome to a certain degree a number of complications in traditional slope stability analyses that jointly consider the mechanical and hydraulic properties of soils. Discrete Bayesian networks resulted to be useful and efficient to acquire knowledge from simulated data and to identify significant factors by the combined use of backward inference and global sensitivity analysis. Further, BNs enable decision thresholds to be estimated quickly. Along with this, backward inference and global sensitivity analysis are performed in BNs at low computation costs. Moreover, under conditions in which knowledge is scarce, we show how a practitioner can be better informed using the proposed approach. All these previously under-reported modelling features in the specialised literature encourage the further application of the proposed approach to enhance slope stability analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of the track geometry irregularities on the wheel-rail dynamic interactions and the rail fatigue initiation through the application of the Dang Van criterion, that supposes an elastic shakedown of the structure. The irregularities are modelled, using experimental data, as a stochastic field which is representative of the considered railway network. The tracks thus generated are introduced as the input of a railway dynamics software to characterise the stochastic contact patch and the parameters on which it depends: contact forces and wheelset-rail relative position. A variance-based global sensitivity analysis is performed on quantities of interest representative of the dynamic behaviour of the system, with respect to the stochastic geometry irregularities and for different curve radius classes and operating conditions. The estimation of the internal stresses and the fatigue index being more time-consuming than the dynamical simulations, the sensitivity analysis is performed through a metamodel, whose input parameters are the wheel-rail relative position and velocity. The coefficient of variation of the number of fatigue cycles, when the simulations are performed with random geometry irregularities, varies between 0.13 and 0.28. In a large radius curve, the most influent irregularity is the horizontal curvature, while, in a tight curve, the gauge becomes more important. [ABSTRACT FROM AUTHOR]

Several dynamic rollover test procedures have been developed over the years; however, the repeatability of test results is often questioned. Also, there can be sensitivity of test and simulation results to small variations in the initial conditions. The objective of this study is to evaluate the response variation of three different crash dummy finite element (FE) models (two versions of a 50th percentile male Hybrid III (HIII) and a Test Device for Human Occupant Restraint (THOR) dummy) in a simulation of a controlled rollover test, using a late model vehicle FE model. A baseline model was established with each dummy, and then a global sensitivity analysis was performed for each dummy varying vehicle design and crash parameters. The simplified HIII dummy model was very sensitive to small variations in the initial conditions of the test. Both the THOR and detailed HIII models gave very consistent results presenting similar trends in the sensitivity analysis. [ABSTRACT FROM AUTHOR]

The degree of which the observable canopy bidirectional reflectance factors (BRF) express plant trait variation at leaf and canopy scales is the fundamental physical basis underlying the use of optical remote sensing data for discriminating tree species and estimating forest biophysical variables. In this study, we quantified the relative contribution of variations in leaf optical properties (LOP), canopy structural properties, and understory reflectance, to canopy BRF variability in a boreal forest, at the spatial and spectral resolutions of Sentinel-2 (S2) Multi-Spectral Instrument. Our approach was based on physically-based forest reflectance model and global sensitivity analysis (SA) parameterized entirely with field measurements. Results showed LOP had dominant contribution to canopy BRF in shortwave infrared (SWIR) in multispecies forest areas, while canopy gap fraction in sensor’s view direction (i.e. nadir) was consistently found as the main driver of canopy BRF in red. This implies the satellite-measured BRF in red is the most robust predictor of effective canopy cover (ECC), while BRF in SWIR are optimal for tree species classification based on interspecific differences in mean leaf traits. [ABSTRACT FROM AUTHOR]

We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large datasets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Operating room (OR) scheduling is a challenging combinatorial problem and hence most optimization-based OR scheduling research makes simplifying assumptions for tractability, including deterministic surgical durations, absence of dynamic emergency arrivals, and the existence of sufficient downstream resources. In this study, we use discrete event simulation to assess the performance of deterministically optimized OR schedules in a network of collaborating hospitals with shared resources, called distributed OR scheduling (DORS), in the face of uncertain surgical durations, emergency arrivals, and limited downstream resources. We quantify the individual and combined disruptive impact of these stochastic factors on the DORS schedule, using real data obtained from the University Health Network (UHN) in Toronto, Canada. We show that the schedule constructed by DORS results in higher OR utilization and lower average surgery cost compared to the simulated current UHN schedule. [ABSTRACT FROM PUBLISHER]

This paper presents a new methodology for estimating the expected energies and first impact distances at the base of a rock cliff, subject to the geometry and properties of the cliff and the representative block being known. The method is based on a sensitivity analysis, conducted by means of kinematic simulations and carried out for a large range of input parameters and their combinations, taking into account the uncertainty associated with their estimate. The proposed approach is validated by comparing predictions to experimental data and shows great potential for a quick qualitative hazard assessment. [ABSTRACT FROM PUBLISHER]

In a model of the formY=h(X1, …,Xd) where the goal is to estimate a parameter of the probability distribution ofY, we define new sensitivity indices which quantify the importance of each variableXiwith respect to this parameter of interest. The aim of this paper is to definegoal oriented sensitivity indicesand we will show that Sobol indices are sensitivity indices associated to a particular characteristic of the distributionY. We name the framework we present asGoal Oriented Sensitivity Analysis(GOSA). [ABSTRACT FROM AUTHOR]

A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder. We characterize the confounder through two parameters that describe the relationships between (a) the confounder and the treatment assignment and (b) the confounder and the outcome variable. Our approach has two primary advantages over similar approaches that are currently implemented in standard software. First, it can be applied to both continuous and binary treatment variables. Second, our method for binary treatment variables allows the researcher to specify three possible estimands (average treatment effect, effect of the treatment on the treated, effect of the treatment on the controls). These options are all implemented in an R package called treatSens. We demonstrate the efficacy of the method through simulations. We illustrate its potential usefulness in practice in the context of two policy applications. [ABSTRACT FROM PUBLISHER]

Global sensitivity analysis (GSA) can help practitioners focusing on the inputs whose uncertainties have an impact on the model output, which allows reducing the complexity of the model. Screening, as the qualitative method of GSA, is to identify and exclude non- or less-influential input variables in high-dimensional models. However, for non-parametric problems, there remains the challenging problem of finding an efficient screening procedure, as one needs to properly handle the non-parametric high-order interactions among input variables and keep the size of the screening experiment economically feasible. In this study, we design a novel screening approach based on analysis of variance decomposition of the model. This approach combines the virtues of run-size economy and model independence. The core idea is to choose a low-level complete orthogonal array to derive the sensitivity estimates for all input factors and their interactions with low cost, and then develop a statistical process to screen out the non-influential ones without assuming the effect-sparsity of the model. Simulation studies show that the proposed approach performs well in various settings. [ABSTRACT FROM PUBLISHER]

The Pettitt test is a non-parametric test that has been used in a number of hydroclimatological studies to detect abrupt changes in the mean of the distribution of the variable of interest. This test is based on the Mann-Whitney two-sample test (rank-based test), and allows the detection of a single shift at an unknown point in time. This test is often used to detect shifts in extremes because of the lack of distributional assumptions. However, the downside of not specifying a distribution is that the Pettitt test may be inefficient in detecting breaks when dealing with extremes. Here we adopt a Monte Carlo approach to examine the sensitivity of the Pettitt test in detecting shifts in the mean under different conditions (location of the break within the series, magnitude of the shift, record length, level of variability in the data, extreme vs non-extreme records, and pre-assigned significance level). These simulation results show that the sensitivity of this test in detecting abrupt changes increases with the increase in the magnitude of the shift and record length. The number of detections is higher when the time series represents the central part of the distribution (e.g. changes in the time series of medians), while the skill decreases as we move toward either low or high extremes (e.g. changes in the time series of maxima). Furthermore, the number of detections decreases as the variability in the data increases. Finally, abrupt changes are more easily detected when they occur toward the center of the time series. Editor D. Koutsoyiannis Associate editor K. Hamed [ABSTRACT FROM AUTHOR]

A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed model structure adaptation method starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model’s parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimisation in symbolic adaptation. The proposed method is applied to models of several virtual and real-world systems that demonstrate its potential utility. [ABSTRACT FROM AUTHOR]

In this paper, we propose the first attempt to perform shape sensitivity analysis for two-dimensional coupled atomistic and continuum problems using bridging scale decomposition. Based on a continuum variational formulation of the bridging scale, the sensitivity expressions are derived in a continuum setting using both direct differentiation method and adjoint variable method. To overcome the issue of discontinuity in shape design due to the discrete nature of the molecular dynamics (MD) simulation, we define design velocity fields in a way that the shape of the MD region does not change. Another major challenge is that the discrete finite element (FE) mass matrix in bridging scale is not continuous with respect to shape design variables. To address this issue, we assume an evenly distributed mass density when evaluating the material derivative of the FE mass matrix. In order to support accuracy verification of sensitivity results using overall finite difference method, we use regular-shaped finite elements and only allow shape change in one direction in our example problems, so that design perturbations can be made to the discrete FE mass matrix. However, the sensitivity formulation is sufficiently general to support irregular-shaped finite elements and arbitrary design velocity fields. The sensitivity analysis results, verified using overall finite difference method, reveal the impact of macroscopic shape design changes on microscopic atomistic responses. [ABSTRACT FROM PUBLISHER]

Sensitivity analysis (SA) of a numerical model, for instance simulating physical phenomena, is useful to quantify the influence of the inputs on the model responses. This paper proposes a new sensitivity index, based upon the modification of the probability density function (pdf) of the random inputs, when the quantity of interest is a failure probability (probability that a model output exceeds a given threshold). An input is considered influential if the input pdf modification leads to a broad change in the failure probability. These sensitivity indices can be computed using the sole set of simulations that has already been used to estimate the failure probability, thus limiting the number of calls to the numerical model. In the case of a Monte Carlo sample, asymptotical properties of the indices are derived. Based on Kullback–Leibler divergence, several types of input perturbations are introduced. The relevance of this new SA method is analysed through three case studies. [ABSTRACT FROM PUBLISHER]

In this study, we define the Pólya–Aeppli process of order k as a compound Poisson process with truncated geometric compounding distribution with success probability 1 − ρ > 0 and investigate some of its basic properties. Using simulation, we provide a comparison between the sample paths of the Pólya–Aeppli process of order k and the Poisson process. Also, we consider a risk model in which the claim counting process {N(t)} is a Pólya-Aeppli process of order k, and call it a Pólya—Aeppli of order k risk model. For the Pólya–Aeppli of order k risk model, we derive the ruin probability and the distribution of the deficit at the time of ruin. We discuss in detail the particular case of exponentially distributed claims and provide simulation results for more general cases. [ABSTRACT FROM AUTHOR]

Simulation of chemically reacting flows using detailed chemistry introduces a large number of chemistry model parameters. While not all significantly affect the target outcomes of a simulation, the parameters that do are not always knowna priori. In order to improve simulations for specified target outcomes, termed quantities of interest (QoIs), the sensitivity of these QoIs to the model parameters are needed. However, evaluating the sensitivities is computationally expensive, especially for complex fuels that may involve many parameters. For these simulations, the forward sensitivity method requires the solution of an additional number of governing equations proportional to the number of parameters. Here, an adjoint sensitivity approach is formulated where the computational cost scales as the number of QoIs and not the number of parameters. Specifically, adjoint equations are derived for laminar, incompressible, variable density reacting flow and applied to hydrogen flame simulations. From the solution of the corresponding adjoint equations, sensitivity of the QoIs to chemistry model parameters is calculated. The one-dimensional simulation results show that the adjoint sensitivity results closely match those of forward sensitivity methods, thus providing validation of the adjoint method. The two-dimensional simulation results indicate the most sensitive parameters for two QoIs, flame tip temperature and NOx emission. For these tests, the adjoint method reduces computational expense compared to forward sensitivity methods by a factor proportional to the number of QoIs over the number of parameters, here 2/172. Such savings can be more drastic for cases that involve complex fuels, such as combustion of jet fuel, requiring thousands of chemistry model parameters. Further, this sensitivity information can be used in development of experiments by pointing out which are the critical chemistry model parameters. [ABSTRACT FROM PUBLISHER]

Identification problems related to age dependent models of a settling process in the secondary settler of a wastewater treatment plant are considered. The basic model is governed by the nonlinear parabolic equationdescribing the scalar conservation law for the density of sludge mass, and by a nonlocal (integral form) additional condition. The problem is formulated as an identification problem, where the sludge concentrationis assumed to be a control. The mathematical analysis, based on the weak solution theory of PDEs, of the basic model shows the degree of sensitivity of the solutionof the parabolic equation with respect to the coefficient. The results obtained here are then applied to two widely accepted settling velocity models (and). An effective iteration algorithm for numerical solution of identification problems related to these models is proposed. The algorithm is tested for the both settling velocity models. Computational simulation permits one to show an influence of the settling velocity models to the behavior of the sludge concentration. [ABSTRACT FROM AUTHOR]

Sustainable concepts in building designs have potential to enhance building performance and to reduce operating cost. With investors in developing economies overlooking all considerations except profit, this paper aims at promoting green construction in Egypt by presenting possible savings that investors could earn. The paper introduces an integrated framework to allow building design stemming from basic sustainable approaches. The framework can function as an optimization/simulation platform to automatically improve building designs. Following the framework principles, a model was developed that functions through four modules: a System Builder to create various envelope systems alternatives; a Building Integrator to form a complete design; a Design Evaluator to assess costs and energy; and Design Adaptation that allows optimization and simulation. The model represents a comprehensive yet easy application tool for cost and energy trade-off analyses that suits early design stage. A comparative analysis showed that green designs exhibited lifecycle and annual energy savings reaching 16.3% and 50%, respectively. A sensitivity analysis was performed to show the relationship between changes in electricity prices and percentage saving in lifecycle cost, optimum design and payback period. Furthermore, simulation of possible building designs is conducted to offer trade-off comparison between initial and energy costs of different designs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cure rate survival model by assuming that the number of competing causes of the event of interest follows the Poisson distribution and the time to event has the Birnbaum–Saunders (BS) distribution. We define the Poisson BS distribution and provide two useful representations for its density function which facilitate to obtain some mathematical properties. Two closed-form expressions for the moments of the new distribution are given. We estimate the parameters of the model with cure rate using maximum likelihood. For different parameter settings, sample sizes and censoring percentages, several simulations are performed. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform a global influence study. We analyse a real data set from the medical area. [ABSTRACT FROM PUBLISHER]

Stress sensitivity plays an important role in the development of a tight low-permeability gas reservoir. With the constant low-permeability reservoir exploitation, formation pressure drop, rock net effective coverage pressure increase, the rock pore is resulted in deformation and the seepage flow of fluid in a low-permeability reservoir no longer followed Darcy flow law. With the further exploitation of reservoirs, reservoir stress sensitivity is enhanced, so evaluation on the stress sensitivity on reservoir development was a burning question. The authors present results of those experiments that simulated the permeability changes via the net confining pressure in the lab and give the influencing factors of stress sensitivity. [ABSTRACT FROM AUTHOR]

The objective of this work is to show how a sensitivity study based on a fractional factorial design can be helpful to quantify the impact of parameter variations on model predictions. These parameters have been carefully chosen due to their high variability in fire modeling and the analysis is conducted by simulating a compartment fire with a CFD model. Through a rigorous approach, it is demonstrated that this fractional design composed of eight simulations gives the same information as a standard full design of 64 runs. Physically, it is found that some turbulence and combustion parameters are significant for most the responses. [ABSTRACT FROM AUTHOR]

This paper presents forward simulation with MassMov2D code and sensitivity analysis of run out resulting from different scenarios of potential failure of a 40 million m3active rockslide hanging above the village of Trafoi, in South Tyrol (Italy). Five scenarios of potential failure zones were identified, with the larger one being the failure of the entire rock slide. Forward simulations showed that the consequences for the Trafoi village would be potentially destructive. A parametric sensitivity analysis was carried out in order to increase the significance of risk assessment. Results obtained by varying friction and turbulent coefficients showed quite unequivocally that although the variability of the run out might be quite large it does not change risk scenarios significantly except in the smaller case of failure. This confirms the usefulness of forward simulations even if they might be affected by uncertainties related to the impossibility to retrieve optimised parameters. [ABSTRACT FROM PUBLISHER]

The SWAT model was tested to simulate the streamflow of two small Mediterranean catchments (the Vène and the Pallas) in southern France. Model calibration and prediction uncertainty were assessed simultaneously by using three different techniques (SUFI-2, GLUE and ParaSol). Initially, a sensitivity analysis was conducted using the LH-OAT method. Subsequent sensitive parameter calibration and SWAT prediction uncertainty were analysed by considering, firstly, deterministic discharge data (assuming no uncertainty in discharge data) and secondly, uncertainty in discharge data through the development of a methodology that accounts explicitly for error in the rating curve (the stage−discharge relationship). To efficiently compare the different uncertainty methods and the effect of the uncertainty of the rating curve on model prediction uncertainty, common criteria were set for the likelihood function, the threshold value and the number of simulations. The results show that model prediction uncertainty is not only case-study specific, but also depends on the selected uncertainty analysis technique. It was also found that the 95% model prediction uncertainty interval is wider and more successful at encompassing the observations when uncertainty in the discharge data is considered explicitly. The latter source of uncertainty adds additional uncertainty to the total model prediction uncertainty.Editor D. Koutsoyiannis; Associate editor D. GertenCitation Sellami, H., La Jeunesse, I., Benabdallah, S., and Vanclooster, M., 2013. Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean watersheds. Hydrological Sciences Journal, 58 (8), 1635−1657. [ABSTRACT FROM PUBLISHER]

Computer simulators are widely used to describe and explore physical processes. In some cases, several simulators are available, each with a different degree of fidelity, for this task. In this work, we combine field observations and model runs from deterministic multifidelity computer simulators to build a predictive model for the real process. The resulting model can be used to perform sensitivity analysis for the system, solve inverse problems, and make predictions. Our approach is Bayesian and is illustrated through a simple example, as well as a real application in predictive science at the Center for Radiative Shock Hydrodynamics at the University of Michigan. The Matlab code that is used for the analyses is available from the online supplementary materials. [ABSTRACT FROM AUTHOR]

This study conducted a series of tests and simulations to estimate collision-attachment efficiency using a trajectory analysis method, to evaluate model sensitivity due to major impact factors in designing and operating dissolved air flotation (DAF) for the various types of wastewaters, and to find the possibility of model application in determining the flotation efficiency of DAF. Collision-attachment efficiency between a bubble and a particle in the model is described by hydrodynamics related to stream function in Stokes’ flow and surface forces based on a classic DLVO theory, encoded using Matlab language software, and performed numerical analysis using the 5th Runge-Kutta method. Model simulation examined the effects of two major factors, size and zeta potential, on collision-attachment efficiency during flotation and field experiment evaluated to model results. Both results from the simulation and field experiment indicated that the larger particles presented the better collision and removal efficiency and the effect of particle zeta potential on collision-attachment efficiencies was increased as the particle zeta potential increased from the negative domain to the positive domain. Therefore, the collision-attachment efficiency model using the trajectory analysis method may be used in estimating the flotation efficiency and in explaining its characteristics. [ABSTRACT FROM AUTHOR]

In the present work, simulations are performed in order to study a turbulent premixed flame stabilised behind a bluff body burner. Conditional Source-term Estimation (CSE) is the combustion model adopted for this purpose and it is coupled with a trajectory gene- rated low-dimensional manifold method for chemistry reduction: conditional-averaged chemical source terms are closed by conditional-averaged scalars, which are obtained by inverting an integral equation. Two regularisation methods are implemented. The optimal regularisation parameter is determined and a sensitivity analysis is performed related to the effect of the value of the regularisation parameter. In the present study, only small differences in the conditional and unconditional averages are noticed for values of the regularisation parameter located in the optimal range determined by the L-curve. Two-equationk–ε and Shear Stress Transport (SST) turbulence models are used to solve the flow field in either the reactive or non-reactive case. Different values of theconstant in the standardk–ε model and of γ2in the SSTk–ω model are tested. Mean axial velocity and progress variable profiles are compared with experimental data. It is found that SSTk–ω gives better results for the velocity profile in both the reactive and non-reactive cases. A value of γ2equal to 0.65 provides best predictions in the reactive case. The progress variable prediction exhibits similar profiles in the case of CSE and the flamelet combustion model approach. Very good agreement is achieved between the CSE predictions and experimental data. [ABSTRACT FROM PUBLISHER]

Wellbore stability is one of critical issues in drilling operations in all types of environments. The wellbore stability in heat sensitive formations, such as through gas hydrates bearing formations, is largely dependent upon the flow of heated mud into these formations and their dissociation. The objective of this study is to analyze the thermal disturbance due to the flow of heated mud. A wellbore temperature simulator is developed and sensitivity analyses of drilling parameters are made to monitor the factors affecting thermal disturbance. It is found that due to an increase in well depth, the mud becomes hot enough and greatly disturbs the in-situ formations temperature of shallow formations. This formation thermal disturbance is critical, particularly in gas hydrate bearing formations and in the permafrost regions where formation heating can cause severe wellbore stability problems. [ABSTRACT FROM PUBLISHER]

A numerical model to predict train-induced vibrations is presented. The dynamic computation considers mutual interactions in vehicle/track coupled systems by means of a finite and discrete elements method. The rail defects and the case of out-of-round wheels are considered. The dynamic interaction between the wheel-sets and the rail is accomplished by using the non-linear Hertzian model with hysteresis damping. A sensitivity analysis is done to evaluate the variables affecting more the maintenance costs. The rail–sleeper contact is assumed extended to an area-defined contact zone, rather than a single-point assumption which fits better real case studies. Experimental validations show how prediction fits well experimental data. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to derive the Greeks formulas of Delta, Gamma, Vega and Theta for derivative securities with both continuous and discontinuous payoff structures under asset price dynamics described by stable and tempered stable processes with presentation of their practical simulation methods. Our approach is based on the representation of stable distributions using an exponential distribution whose scaling property with respect to the Girsanov transform is used in the Malliavin calculus framework on the Poisson space. Numerical results are presented to illustrate the effectiveness of our formulas in Monte Carlo simulations relative to the finite difference method. [ABSTRACT FROM AUTHOR]

Vehicle safety has become a major task in the development process of passenger vehicles. Measures for occupant safety are developed by reference to different tests including legal tests and consumer tests. The different load cases for the considered frontal crash tests cause different vehicle kinematics involving all six degrees of freedom. Besides restraint systems, vehicle kinematics shows distinct influence on occupant loading. A sensitivity analysis of the dummy responses due to varied motion fractions has been performed by finite elements simulations of a well-validated simulation model. The influences of particular motion components are determined by objective assessment variables. These are well-known injury criteria as well as geometrical criteria such as distances between dummy and interior. The results of this sensitivity study can be used for additional measures in terms of occupant protection. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a planning model and a real-time adjustment model to help an international express company facing uncertain demands to plan courier routes and schedules and adjust the planned routes in actual operations. A solution procedure is then developed to efficiently solve the real-time adjustment model. A simulation-based evaluation method is also developed to compare the performance of the proposed models. The test results, related to an international express company's operations, show the good performance of the proposed models. Sensitivity analysis is also performed to gain better insights into knowing how several important parameters affect the solutions. [ABSTRACT FROM AUTHOR]

To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. [ABSTRACT FROM AUTHOR]

This work describes an analytical approach to determine what degree of accuracy is required in the definition of the rail vehicle models used for dynamic simulations. This way it would be possible to know in advance how the results of simulations may be altered due to the existence of errors in the creation of rolling stock models, whilst also identifying their critical parameters. This would make it possible to maximise the time available to enhance dynamic analysis and focus efforts on factors that are strictly necessary. In particular, the parameters related both to the track quality and to the rolling contact were considered in this study. With this aim, a sensitivity analysis was performed to assess their influence on the vehicle dynamic behaviour. To do this, 72 dynamic simulations were performed modifying, one at a time, the track quality, the wheel–rail friction coefficient and the equivalent conicity of both new and worn wheels. Three values were assigned to each parameter, and two wear states were considered for each type of wheel, one for new wheels and another one for reprofiled wheels. After processing the results of these simulations, it was concluded that all the parameters considered show very high influence, though the friction coefficient shows the highest influence. Therefore, it is recommended to undertake any future simulation job with measured track geometry and track irregularities, measured wheel profiles and normative values of the wheel–rail friction coefficient. [ABSTRACT FROM AUTHOR]

A sensitivity analysis has been performed to assess the influence of the elastic properties of railway vehicle suspensions on the vehicle dynamic behaviour. To do this, 144 dynamic simulations were performed modifying, one at a time, the stiffness and damping coefficients, of the primary and secondary suspensions. Three values were assigned to each parameter, corresponding to the percentiles 10, 50 and 90 of a data set stored in a database of railway vehicles. After processing the results of these simulations, the analysed parameters were sorted by increasing influence. It was also found which of these parameters could be estimated with a lesser degree of accuracy in future simulations without appreciably affecting the simulation results. In general terms, it was concluded that the highest influences were found for the longitudinal stiffness and the lateral stiffness of the primary suspension, and the lowest influences for the vertical stiffness and the vertical damping of the primary suspension, with the parameters of the secondary suspension showing intermediate influences between them. [ABSTRACT FROM AUTHOR]

Knowledge of the sensitivity of inverse solutions to variation of parameters of a model can be very useful in making engineering design decisions. This article describes how parameter sensitivity analysis can be carried out for inverse simulations generated through approximate transfer function inversion methods and also through the use of feedback principles. Emphasis is placed on the use of sensitivity models and the article includes examples and a case study involving a model of an underwater vehicle. It is shown that the use of sensitivity models can provide physical understanding of inverse simulation solutions that is not directly available using parameter sensitivity analysis methods that involve parameter perturbations and response differencing. [ABSTRACT FROM PUBLISHER]

A simple approach was carried out to study the effects of various parameters on high performance liquid chromatography (HPLC) performance of microcystin-LR (MC-LR) analysis in samples from some reservoirs and rivers in Taiwan under simulation conditions. Nature of column and its packing affected the instrument sensitivity. Varied column length indicated the longer column enhanced (C18, 5 μm, 4.6 × 150 mm) HPLC performance. Studies on mobile phase found that ratio and concentration of the mobile phase gradient greatly influenced the peak shape, retention time, and optimal conditions of mobile phase were set at acetonitrile and 0.01M ammonium acetate (30:70). Different compounds were added to mobile phase and their contribution to HPLC spectra was reported. Detection limits were carefully measured and reported. These investigations provide simple and important information that may be applied to HPLC performance for microcystin analysis from natural water systems. [ABSTRACT FROM PUBLISHER]

Nonparametric procedures based on the bootstrap were developed for testing two assumptions in a spatial-temporal model, i.e., constant temporal effect across locations/spatial units and constant spatial effect over time. Simulation studies indicate that the procedures can correctly verify the assumptions for reasonably sized data. Presence of spatial clustering can further improve the sensitivity of the test under non-constant spatial effect over time. Furthermore, the test procedure for constant spatial effect over time is robust to model misspecification. [ABSTRACT FROM AUTHOR]

This paper presents extended forward sensitivity analysis as a method to improve uncertainty quantification. By including the time step and potentially grid spacing as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step sensitivity information reflects global numerical errors. Discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool than other tools of its type to help uncertainty quantification. When the relative sensitivity of the time step to other physical parameters is known, the simulation is allowed to run at optimized time steps without affecting the confidence of the physical parameter sensitivity results. The time step forward sensitivity analysis method can also replace traditional time step convergence studies that are a key part of code verification, with much less computational cost. Two well-defined benchmark problems with manufactured solutions are utilized to demonstrate the method. [ABSTRACT FROM AUTHOR]

This paper presents a methodology based on transforming estimation methods in optimization problems in order to incorporate in a natural way some constraints that contain extra information not considered by standard estimation methods, with the aim of improving the quality of the parameter estimates. We include here three types of such information: bounds for the cumulative distribution function, bounds for the quantiles, and any restrictions on the parameters such as those imposed by the support of the random variable under consideration. The method is quite general and can be applied to many estimation methods such as the maximum likelihood (ML), the method of moments (MOM), the least squares, the least absolute values, and the minimax methods. The performances of the obtained estimates from several families of distributions are investigated for the ML and the MOM, using simulations. The simulation results show that for small sample sizes important gains can be achieved with respect to the case where the above information is ignored. In addition, we discuss sensitivity analysis methods for assessing the influence of observations on the proposed estimators. The method applies to both univariate and multivariate data. [ABSTRACT FROM AUTHOR]

This work considers two specific estimation techniques for the family-specific proportional hazards model and for the population-averaged proportional hazards model. So far, these two estimation procedures were presented and studied under the gamma frailty distribution mainly because of its simple interpretation and mathematical tractability. Modifications of both procedures for other frailty distributions, such as the inverse Gaussian, positive stable and a specific case of discrete distribution, are presented. By extensive simulations, it is shown that under the family-specific proportional hazards model, the gamma frailty model appears to be robust to frailty distribution mis-specification in both bias and efficiency loss in the marginal parameters. The population-averaged proportional hazards model, is found to be robust under the gamma frailty model mis-specification only under moderate or weak dependency within cluster members. [ABSTRACT FROM AUTHOR]

The subset simulation (SS) method is a probabilistic approach which is devoted to efficiently calculating a small failure probability. Contrary to Monte Carlo Simulation (MCS) methodology which is very time-expensive when evaluating a small failure probability, the SS method has the advantage of assessing the small failure probability in a much shorter time. However, this approach does not provide any information about the probability density function (PDF) of the system response. In addition, it does not provide any information about the contribution of each input uncertain parameter in the variability of this response. Finally, the SS approach cannot be used to calculate the partial safety factors which are generally obtained from a reliability analysis. To overcome these shortcomings, the SS approach is combined herein with the Collocation-based Stochastic Response Surface Method (CSRSM) to compute these outputs. This combination is carried out by using the different values of the system response obtained by the SS approach for the determination of the unknown coefficients of the polynomial chaos expansion in CSRSM. An example problem that involves the computation of the ultimate bearing capacity of a strip footing is presented to demonstrate the efficiency of the proposed procedure. The validation of the present method is performed by comparison with MCS methodology applied on the original deterministic model. Finally, a probabilistic parametric study is presented and discussed. [ABSTRACT FROM AUTHOR]

The article begins by reviewing a two-level hierarchical multicriteria routing model for Multiprotocol Label Switching networks with two service classes (QoS, i.e. with Quality of Service requirements, and Best Effort services) and alternative routing, as well as the foundations of a heuristic resolution approach, previously proposed by the authors. Afterwards a new variant of this heuristic approach, which includes a Pareto archive strategy, is described. In this archive, non-dominated solutions obtained throughout the heuristic are kept. At the end of the main procedure of the heuristic, these solutions are evaluated and a final solution for the routing problem is chosen using a reference point-based approach. The application of this procedure to two test networks will show, with analytic and discrete-event simulation models, that, in certain initial conditions, this approach provides improvements in the final results concerning the top-level objective functions, especially in more ‘difficult’ situations detected through sensitivity analysis. [ABSTRACT FROM AUTHOR]

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters, especially when the modelling process is being performed before construction and commissioning. Past efforts to perform sensitivity and uncertainty analysis have focused on tens of parameters, while in this work, we increase the size of analysis by two orders of magnitude (by studying the influence of about 1000 parameters). We extend traditional sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics, which identifies which internal or intermediate processes transmit the most uncertainty to the final output. We present these results as a method that is applicable to many different modelling tools, and demonstrate its applicability on an example EnergyPlus model. [ABSTRACT FROM AUTHOR]

Override simulations of two train units in frontal collision have been carried out using multi-body dynamics. The aim of this paper is to investigate the possible factors influencing the overriding behaviour throughout frontal collisions. In addition to the initial vertical offset defined in the standard EN 15227, the pitching motion of vehicles has a great effect on the overriding phenomenon. It depends on several factors, such as collision mass, pitching frequency, height of the centre of mass above the rail level. In this paper, it has been shown that the overriding phenomenon is more sensitive to variations in pitching frequency and height of the centre of mass, compared with the factor of the collision mass. Moreover, it has been demonstrated from simulation results that a 200-kN vertical force is required for the combined anti-climber devices (100 kN for each side anti-climber) to constrain the vertical relative motion between crashed train units. [ABSTRACT FROM AUTHOR]