Reliability sensitivity analysis based on system failure evolution

This paper used both methods of single parameter and Multi-parameter combination sensitivity analysis in groundwater numerical model parameter sensitivity analysis. With the examples of Jiaozuo, Henan province, choosing coefficient of permeability, porosity and longitudinal dispersion degree as factor, the paper analysis the main controlling factors of contaminant migration of Hexavalent chromium in groundwater. Single parameter sensitivity analysis results show that the permeability coefficient gives largest impact to the migration of hexavalent chromium. It is positively correlates with groundwater numerical model. The rest of the parameters assume the inverse correlation. Multi-parameter combination sensitivity analysis results show that the permeability coefficient has absolute advantage among various parameter combinations. Another conclusion is that longitudinal dispersion degree affects smaller on the process of hexavalent chromium pollutants migration, but the impact on the final density is bigger.

This study investigates the impact of melting/binding rates (referred to hereafter as the parameters) over the polymers and monomers on the dynamics of carbon-monoxide-mediated sickle cell hemoglobin (HbS) de-polymerization. Two approaches, namely the traditional sensitivity analysis (TSA) and the multi-parameter sensitivity analysis (MPSA), have been developed and applied to the mathematical model system to quantify the sensitivities of polymers and monomers to the parameters. The Runge-Kutta method and the Monte-Carlo simulation are employed for the implementation of the sensitivity analyses. The TSA utilizes the traditional sensitivity functions (TSFs). The MPSA enumerates the overall effect of the model input parameters on the output by perturbing the model input parameters simultaneously within large ranges. All four concentrations (namely, de-oxy HbS monomers, CO-bound HbS monomers, de-oxy Hbs polymer and CO-bound HbS polymer) as model outputs, and all four binding/melting rates (namely, the CO binding and melting rates for polymers and monomers) as input parameters are considered in this study. The sensitivity results suggest that TSA and MPSA are essentially consistent.

Numerical control (NC) machine tool is typical mechatronics system with complicated failure features. System reliability may be affected by the internal properties, external factors, dynamic physical effects and the complex interdependent relationship, and it is too complicated to accurately model or analyse by conventional methods. Improving the reliability of machine tool cannot rely solely on improving unit parts reliability level, and reliability design should be performed at the system level for a better profit. For this purpose, using agent-oriented modeling method, a system failure evolution model is proposed to simulate the failure dynamic process. With the model, not only system lifetime and reliability can be analyzed, system failure mechanism and impact of the internal properties, external factors and system parameters also can be researched. Unit failure-agent model, system-level failure-agent model, hierarchical model structure and simulation control method are described separately. Finally, an example of the machine tool's feed system model is given to simulate and analyze system life, and a further analysis indicate that the model is valid and close to the real data.

Improved understanding of dynamic hydrological performance of green roof (GR) design parameters towards different model responses is important for maximizing its target design goals at the unit-scale. Replication of an optimally designed GR unit at the catchment-scale significantly contributes to achieving its target design goals (i.e., surface runoff reduction, urban flood reduction, peak flow control, etc.). Moreover, adequate efforts are required to explore and provide appropriate knowledge about the categorization of influential and non-influential design parameters with their suitable design spaces to guide researchers, drainage engineers, and stormwater management practitioners for effective and efficient planning, designing and optimization of GR at catchment-scales.This study employs a robust and comprehensive global sensitivity analysis (GSA) method known as the variogram analysis of response surfaces (VARS) for sensitivity analysis of GR design parameters. Firstly, a total of 13,999 sample points for 14 GR parameters of three layers (i.e., surface, soil and drainage mat) are generated by using the latin hypercube sampling technique and their factor spaces are decided based on design guidelines in current SWMM manuals. Following that, the PySWMM is used to simulate these design samples in a Monte-Carlo-type setting on a conceptual catchment of 0.01km2 (100m2 × 100m2) with 50% treatment area of GR, and the model responses (e.g., surface infiltration, surface outflow, storage volume, and peak flow) are estimated and applied for sensitivity analysis. Finally, VARS evaluates different sensitivity analysis metrics by using different model responses corresponding to their designed samples.Overall, the senstivity analysis results demonstrate that 8 out of 14 design parameters are highly influential on different model responses, however, the parameters’ sensitivity varies towards different model responses under different perturbation scales and rainfall conditions. Moreover, the selection of an effective range of design space of design parameters is necessary as it has a higher influence on model responses, while the parameters’ rankings and contributions to total sensitivity indices change with the range of design spaces. Furthermore, this research also provides an opportunity through VARS directional variogram index (an integrated sensitivity index) to study and understand the underlying mechanisms of design parameters under different perturbation scales with no extra computational burden. Senstivity analysis results will be presented with insights and recommendations for other regions, which will be helpful for decision-makers for effective planning, designing and implementation of GR. The findings of this parametric study would be helpful for the calibration and optimization of design parameters of GR for different case studies. 

PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

Richardson extrapolation-based sensitivity analysis in the multi-objective optimization of analog circuits

Comprehensive environmental models such as the Soil and Water Assessment Tool (SWAT) are becoming an integral part of decision making processes for effective planning and management of natural resources. Before their use as decision making aid, however, models must be properly evaluated to improve their prediction accuracy and reduce the likelihood of making decisions that could lead to undesirable policy outcomes. Model evaluation refers to practices such as quality analysis of input data, sensitivity analysis, calibration and verification, and uncertainty analysis. Many methodologies have been developed for model evaluations over the years. One of the major limitations of the existing model evaluation methods, in particular model calibration methods, is their computational inefficiency, especially when used to calibrate comprehensive watershed simulation models. It may take weeks to months of CPU time, depending on the problem size, to successfully calibrate a comprehensive watershed simulation model on a standard PC. In this study, two sensitivity analysis methods and four calibration methods are used to evaluate SWAT to improve its streamflow prediction accuracy for the Morro Bay watershed located on the central coast of California. Parameter sensitivity analysis was performed using step-wise-regression analysis and the one-factor-at-a time screening method. Calibration was performed using PEST, Genetic Algorithms, the Shuffled Complex Evolution, and the Dynamically Dimensioned Search using observed data from multiple sites in the watershed. The model evaluation methods are compared in terms of their computational efficiency as well as effectiveness to determine “accurate” results. The developed SWAT model can be used to evaluate effectiveness of the Best Management Practices installed in the Morro Bay watershed, and to also prioritize sites where BMPs may be implemented in the future to further improve ecological integrity of the Morro Bay Estuary, which is one of the most important wetlands in California as it supports wide variety of habitats including numerous sensitive and endangered plant and animal species.

The aim of this study is to verify if the reliability of a digital four-channel RPS under the design phase satisfies the specified target and to identify the weakness of system design and potential solutions for system reliability improvement. The event-tree/fault-tree (ET/FT), which is the method used in the current probabilistic safety assessment (PSA) framework of nuclear power plants (NPPs), was adopted to developed reliability modeling for the RPS with the Top Events defined as the system failure to generate reactor trip signal and the system generating spurious trip signal. The evaluation results indicate that the probability of the system failure on demand and the frequency of spurious trip signal generation are 1.47 × 10−6 with a 95% upper bound of 4.63 × 10−6 and 7.94 × 10−4/year with a 95% upper bound of 2.50 × 10−3/year, respectively. The importance and sensitivity analyses were conducted and it was found that undetected unsafe common cause failures (CCFs) of signal conditioning modules (SCMs) dominate the system reliability. Two preliminary optimization schemes relative to reducing periodic test interval and adapting two kinds of diverse SCMs were proposed. Results of the quantitive evaluation of the schemes show that neither of them could determinedly improve the system reliability to the target level. In the future, more detailed optimization analysis shall be required to determine a feasible system design optimization scheme.

An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool

Using Bayesian networks in reliability evaluation for subsea blowout preventer control system

A Computer Visualization Method for Multi-Parameter Simulation, Sensitivity Analysis, and Identifiability of Distributed Capillary-Tissue Transport Models

A review of sensitivity analysis methods in building energy analysis

In the present study, the effective parameters of water-Al2O3 nanofluid flowing in flat tubes are investigated using the EFAST Sensitivity Analysis (SA) method. The SA is performed using GMDH type artificial neural networks (ANN) which are based on validated numerical data of two phase modeling of nanofluid flow in flat tubes. There are five design variables namely: tube flattening (H), flow rate (Q), wall heat flux ( ), nanoparticle diameter (dp) and nanoparticle volume fraction (φ) and there are two objective functions namely: pressure drop (∆P) and heat transfer coefficient (h). The results show that among design variables, the tube flattening has the highest effect on variations of pressure drop (74%) and heat transfer coefficient (40%). Except tube flattening, the flow rate and the nanoparticle volume fraction has the highest effect on pressure drop (24%) and heat transfer coefficient (25%) respectively. The effects of all of the design variables on objective functions are shown in the results.

This paper is concerned with the shape sensitivity analysis of a class of boundary control, constrained, optimal control problems for parabolic systems. The notion of Euler and Lagrange derivatives of a boundary optimal control in the direction of a vector field is introduced. The derivatives are obtained in the form of optimal solutions of auxiliary optimal control problems. The method of sensitivity analysis used in this paper is based on related results on the differential stability of metric projections in Hilbert space onto a convex, closed subset, combined with the material derivative method of shape sensitivity analysis. Parabolic initial-boundary value problems with Dirichlet and Neumann boundary conditions are considered in this paper.

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.