Multi-objective Sensitivity Analysis Research Articles

Multi-obective performance analysis of a wind power plant equipped with a PAT system

In conventional wind power plants, the wind turbine drives an electrical generator equipped with an AC/DC converter for battery charging purposes and with an inverter inverter to supply power to the grid / AC load. This approach exhibits some drawbacks like the energy losses associated to the power electronics and the cost and the waste management of the battery storage systems.To overcome such drawbacks, this work proposes a new plant scheme, where the battery system has been removed (or strongly reduced) and the storage task is accomplished by a pumping system equipped with a reversible hydraulic machinery which can operate both as centrifugal pump and a hydraulic turbine (called Pump as Turbine). In this scenario, when the wind velocity is quite high, the reversible hydraulic machinery, working as centrifugal pump, will store energy in the form of potential energy pumping fluid to an elevated water reservoir. On the contrary, in low wind conditions, the Pump as Turbine will operate as a hydraulic turbine, helping the wind turbine to overcome the external mechanical load. This solution results in a number of advantages: lower plant costs, longer Pump as Turbine life with lesser maintenance and reduced waste costs.The present work focuses on a multi-objective performance analysis of the proposed wind system power scheme. Specifically, this multi-objective sensitivity analysis will be addressed to the net potential energy stored by the reversible hydraulic machinery, the extracted wind energy and the torque supplied by the Pump as Turbine in hydraulic turbine mode. Such an analysis will considered as design variables the Pump as Turbine (centrifugal pump mode) specific speed (pump geometry), the gear box transmission ratio between the centrifugal pump and the wind turbine and the pump head. The aim of this analysis is to evaluate the design variables range which could lead to determine the set of the multi-objective wind systems optimal design.

Exergoeconomic Evaluation and Optimization of Reverse Brayton Refrigerator

Abstract In this article, by using exergoeconomic approach, an economic evaluation of reverse Brayton cycle-based refrigerator has been performed for 10 kW range cooling capacity at 65 K. One of the typical application domains of this refrigerator at the considered heat load is cooling of high-temperature superconductor for future power transmission lines that are in the developmental phase in different parts of the world. Multi-objective optimization and sensitivity analysis have been performed to investigate the sources of cost attached to the exergy destruction. Based on exergoeconomic evaluation parameters, recommendations have been provided for deciding the cost-effective design parameters for refrigerators. The significant finding of this analysis is that, for the basic reverse Brayton cryocooler, component performance, the turbine being the major one, needs to be improved to meet the economic criteria of 25 $/W as enumerated in the cryogenic road map for high-temperature superconductor cooling. The exergoeconomic design applied on the reverse Brayton refrigerator for high-temperature superconductor cables cooling can be adapted for other applications such as recondensation and liquefied natural gas cold energy utilization.

Multi-objective sensitivity analysis of shell-and-tube LHTES performance

In the present paper a sensitivity analysis has been carried out concerning the charging/discharging time and the stored energy performances of a shell-and-tube LHTES with respect to the number of tubes and the tube internal radius. The aim of this analysis is to investigate how the design variables affect the LHTES performance. this could lead to determine the thermal storage optimal design. Thus, the sensitivity analysis has a key role in the selection of several acceptable solutions. The considered LHTES exhibits a cylindrical shell geometry characterized by constant height and diameter. This aspect has allowed to employ simplified theoretical models able to predict the charging/discharging time and the stored energy performance. These models consider a constant heat exchange wall temperature whereas the heat exchange area and the whole PCM volume vary according to the design variables. This analysis represents the first step to solve the multi-objective optimization of the thermal storage design problem and then to determine the best solutions in both design variables and thermal storage performance domains.

Multi-Objective Optimization Design and Analysis of Double-Layer Winding Halbach Fault-Tolerant Motor

Double-layer winding halbach fault-tolerant motor (DLW-HFTM) combines the advantages of the permanent magnet motor and the fault-tolerant motor, which are beneficial to improve the characteristics of the joint robot. First, the structure of DLW-HFTM is proposed in view of the requirement of the fault tolerance of drive motor on the joint robot without degrading the other performances, such as the torque and the efficiency. Then, the working principle and initial design dimensions are depicted, and a finite element parameter model is established by Maxwell software. Furthermore, the design parameters are optimized by multi-objective sensitivity analysis. Finally, the electromagnetic performances before and after optimization are analyzed, including no-load back EMF, torque, loss, efficiency, etc. Meanwhile, the fault-tolerant performance of the motor is analyzed. The research results show that the DLW-HFTM can improve the fault-tolerance on the condition of meeting the design requirements.

Multiobjective sensitivity analysis and model parameterization approach for coupled streamflow and groundwater table depth simulations using SHETRAN in a wet humid tropical catchment

Physically based Distributed Hydrological Models (PDHM) are complex and exhibits extreme challenges for simulating the dynamics of coupled streamflow and groundwater table depths. The present work assesses the capabilities of a PDHM, SHETRAN in capturing streamflow and depth to groundwater table to represent the hydrologic processes within a wet tropical humid catchment. The most significant model parameters for the coupled simulations of streamflow and groundwater table depths are assessed through a multiobjective sensitivity analysis using Morris screening method and local sensitivity analysis. Horizontal and vertical soil saturated hydraulic conductivities, specific storage, as well as Actual/Potential evapotranspiration and Strickler overland coefficient, are highly sensitive for both the model responses considered. In order to arrive at the optimal model parameters for simulating depth to water table and streamflow, the effect of three model parametrization approaches were analyzed: with no spatial variation in soil properties, elevation based variation of only middle and bottom soil layer saturated hydraulic conductivity and elevation based variation of horizontal (kx), vertical (kz) soil saturated hydraulic conductivities and specific storage (Ss). Results indicate that for the studied watershed, the elevation based variation in kx, kz, and Ss resulted in a better prediction of depth to water table variations (in terms of correlation coefficient) with no significant improvements in streamflow simulation compared to the other two parameterization cases. The study suggests incorporating preferential flows in SHETRAN model to improve prediction of the water table dynamics in catchments belonging to the humid tropical region.

A hybrid stochastic sensitivity analysis for low-frequency vibration and low-velocity impact of functionally graded plates

This paper deals with the stochastic sensitivity analysis of functionally graded material (FGM) plates subjected to free vibration and low-velocity impact to identify the most influential parameters in the respective analyses. A hybrid moment-independent sensitivity analysis is proposed coupled with the least angle regression based adaptive sparse polynomial chaos expansion. Here the surrogate model is integrated in the sensitivity analysis framework to achieve computational efficiency. The current paper is concentrated on the relative sensitivity of material properties in the free vibration (first three natural frequencies) and low-velocity impact responses of FGM plates. Typical functionally graded materials are made of two different components, where a continuous and inhomogeneous mixture of these materials is distributed across the thickness of the plate based on certain distribution laws. Thus, besides the overall sensitivity analysis of the material properties, a unique spatial sensitivity analysis is also presented here along the thickness of the plate to provide a comprehensive view. The results presented in this paper would help to identify the most important material properties along with their depth-wise spatial sensitivity for low-frequency vibration and low-velocity impact analysis of FGM plates. This is the first attempt to carry out an efficient adaptive sparse PCE based moment-independent sensitivity analysis (depth-wise and collectively) of FGM plates under the simultaneous susceptibility of vibration and impact. Such simultaneous multi-objective sensitivity analysis can identify the important system parameters and their relative degree of importance in advanced multi-functional structural systems.

Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia

Understanding glacio-hydrological processes is crucial to water resources management, especially under increasing global warming. However, data scarcity makes it challenging to quantify the contribution of glacial melt to streamflow in highly glacierized catchments such as those in the Tienshan Mountains. This study aims to investigate the glacio-hydrological processes in the SaryDjaz-Kumaric River (SDKR) basin in Central Asia by integrating a degree-day glacier melt algorithm into the macro-scale hydrological Soil and Water Assessment Tool (SWAT) model. To deal with data scarcity in the alpine area, a multi-objective sensitivity analysis and a multi-objective calibration procedure were used to take advantage of all aspects of streamflow. Three objective functions, i.e., the Nash–Sutcliffe efficiency coefficient of logarithms (LogNS), the water balance index (WBI), and the mean absolute relative difference (MARD), were considered. Results show that glacier and snow melt-related parameters are generally sensitive to all three objective functions. Compared to the original SWAT model, simulations with a glacier module match fairly well to the observed streamflow, with the Nash–Sutcliffe efficiency coefficient (NS) and R2 approaching 0.82 and an absolute percentage bias less than 1%. Glacier melt contribution to runoff is 30–48% during the simulation period. The approach of combining multi-objective sensitivity analysis and optimization is an efficient way to identify important hydrological processes and recharge characteristics in highly glacierized catchments.

Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method

As weather-dependent distributed renewable energy resources (RERs) such as photovoltaic (PV) systems and wind farms have increasingly been connected to distribution networks, energy storage systems able to compensate intermittency in their power generation may be required. Moreover, such RERs can participate in reactive power control upon voltage regulation. Thus, the problem of optimizing the capacity of storage systems for RERs with the capability of reactive power control is necessary for planning, maintaining, or upgrading a distribution network. The objective of this study is to optimize the capacity of storage systems for RERs, particularly PV inverters with the capability of reactive power control in this study. For this purpose, this study proposes the power-flow algorithm able to optimize reactive power amount to be either consumed or injected by PV systems and a hybrid multi-objective sensitivity analysis algorithm that optimizes the capacity of PV and storage systems. The proposed algorithm includes an objective function that minimizes voltage variations and capital costs of PV and storage as well as maximizes energy savings and peak load reduction. Then, it successfully optimizes the capacity of PV and storage systems in the well-known IEEE test feeders.

Modelling and simulation of energy consumption of ceramic production chains with mixed flows using hybrid Petri nets

Ceramic production chain consisting of discrete flow and continuous flow energy-intensive processes consumes substantial amounts of energy. This study aims to evaluate energy consumption performance and energy-saving potentials of the ceramic production chain. According to the energy consumption characteristics of manufacturing processes and process interaction constraints in a ceramic production chain, an approach integrating the first-order hybrid Petri net (FOHPN) model, an objective linear programming model and a sensitivity analysis is proposed. The FOHPN model will simulate the energy consumption patterns of the ceramic production chain. Meanwhile, multi-objective linear programming model and sensitivity analysis will suggest the optimal specific energy consumption (SEC) of the production chain and identify the influences of input parameters (i.e. production rate of a process) on the SEC in the optimal production scheme. Finally, a real case study from bathroom ceramic plant validates the approach. It provides a tool for modelling and simulation of energy consumption of ceramic production chains with mixed flows and helps operators to perform energy-saving actions in the ceramic enterprise.

An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China

Ensemble Kalman Filter (EnKF) is potential in optimizing parameters of an environmental model, but may lead to a worse performance of the model in case that improper parameters were updated. To overcome this weakness, EnKF was improved by coupling with a dynamic and multi-objective sensitivity analysis. The improved EnKF was applied to update the parameters of a coupled phosphorus model for simulating phosphorus dynamics of Polder Jian located in Lake Taihu Basin, China. Two parameters that were most sensitive to particulate and dissolved phosphorus were identified at each sub-period, and were then updated using EnKF. To evaluate the performance of the improved EnKF, four simulations with different parameter update strategies were implemented, and compared with measured data. The simulation with the improved EnKF well simulated DP dynamics in Polder Jian with a d value of 0.65 and a RMSE value of 0.015mg/L. This model fit is better than that of other three simulations with different parameter update strategies, implying a success of the improved EnKF in updating parameters of the coupled phosphorus model. This improved EnKF has the advantage to update several parameters simultaneously, and can be applied in other models with minimal changes.

Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle

In this study, the optimal performance of a regenerative Brayton cycle is sought through power maximization and then exergoeconomic optimization using finite-time thermodynamic concept and finite-size components. Optimizations are performed using genetic algorithm. In order to take into account the finite-time and finite-size concepts in current problem, a dimensionless mass-flow parameter is used deploying time variations. The decision variables for the optimum state (of multi objective exergoeconomic optimization) are compared to the maximum power state. One can see that the multi objective exergoeconomic optimization results in a better performance than that obtained with the maximum power state. The results demonstrate that system performance at optimum point of multi objective optimization yields 71% of the maximum power, but only with exergy destruction as 24% of the amount that is produced at the maximum power state and 67% lower total cost rate than that of the maximum power state. In order to assess the impact of the variation of the decision variables on the objective functions, sensitivity analysis is conducted. Finally, the cycle performance curve drawing according to exergoeconomic multi objective optimization results and its utilization, are suggested.

Multi-objective generalised sensitivity analysis to TSEB model applied at irrigated olive orchard in semi-arid area

The results of the two source energy balance model TSEB Norman et al., 1995 are strongly dependent on most influencing parameters and also its adjustment. The present study is focused on multi-objective generalised sensitivity analysis MOGSA approach to identify the most sensitive parameters on TSEB outputs, in order to conduct their calibration thereafter to improve the performance of TSEB model. As expected, and conformably to previous studies, the results show that the changing of some soil and vegetation parameters has a significant influence on TSEB outputs. These results were found to be conforming to in situ semi-arid environments, thereby vegetation parameters have the most sensitive influence, and thus adequate attention to these can greatly enhance performance of TSEB, via calibration in order to report local environmental realism.

Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach ε-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more insight into parameter sensitivity and the conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provide an alternative way for future MOBIDIC modeling.

Multi-objective optimisation and sensitivity analysis of a paratransit bus structure for rollover and side impact tests

Paratransit buses are heavily used in the United States. A paratransit bus consists of custom passenger compartments mounted onto separate cutaway chassis. The lack of dedicated national crashworthiness standards, along with different construction methods used by paratransit fleet manufacturers, can result in a wide variance of passenger compartment structural strength. In August 2007, the Florida Department of Transportation (FDOT), to ensure adequate crashworthiness performance, introduced a standard stipulating that newly acquired buses must be tested for rollover and side impact conditions. The rollover test is performed using a tilt table test according to UN-ECE Regulation 66. The side impact test involves the impact of a bus by a common sport utility vehicle or pickup truck. In the current study, an original finite element model of a paratransit bus was used in LS-DYNA® simulations of both the rollover and the side impact testing procedures per FDOT standard. Using LS-OPT®, a metamodel-based approach was used to perform multi-objective optimisation of the bus structure for the rollover and the side impact tests. The linear ANOVA and the Sobol's indices approach were used for sensitivity analysis. The structural components of the bus having the greatest influence on the bus performance in the simulated test scenarios were identified. The simulation results show that the original bus design would pass the FDOT testing procedure. However, appropriate redistribution of the mass can noticeably increase its strength for the side impact case.

Multi-Objective Sensitivity Analysis of a Fully Distributed Hydrologic Model WetSpa

The application of fully distributed watershed models has the advantage of providing location-specific outputs. However, the calibration of these models is very challenging due to over-parameterization. A typical strategy is to aggregate parameters and screen out insensitive parameters in order to decrease the dimension of the problem for calibration. To ensure the validity of calibration, it is important to identify important physical processes and parameter interactions, and examine how different model setups affect model simulation. In this paper, a two-step multi-objective sensitivity analysis approach is applied to a distributed hydrologic model, the WetSpa (Water and Energy Transfer between Soil, Plant and Atmosphere), with case studies in the Chaohe Basin in China and the Margecany Basin in Slovakia respectively. This two-step global sensitivity analysis technique, incorporating the Morris method and the SDP (State Dependent Parameter) method, has proved to be effective in the two case studies. The results of two case studies show that (i) a warm-up period is essential for minimizing the impact of initial state variables to the model simulation, (ii) different objective functions lead to different sensitivity results, (iii) evapotranspiration is the most sensitive process to the model result in the two study watersheds followed by the groundwater and soil water process, and (iv) the sensitivity of snowmelt process is case dependent.

A MULTI-OBJECTIVE SENSITIVITY APPROACH TO TRAINING PROVIDERS' EVALUATION AND QUOTA ALLOCATION PLANNING

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multiobjective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

Integrated multi-objective robust optimization and sensitivity analysis with irreducible and reducible interval uncertainty

Uncertainty considered in robust optimization is usually treated as irreducible since it is not reduced during an optimization procedure. In contrast, uncertainty considered in sensitivity analysis is treated as partially or fully reducible in order to quantify the effect of input uncertainty on the outputs of the system. Considering this, and the usual existence of both reducible and irreducible uncertainty, an approach that can perform robust optimization and sensitivity analysis simultaneously is of much interest. This article presents such an integrated optimization model that can be used for both robust optimization and sensitivity analysis for problems that have irreducible and reducible interval uncertainty, multiple objective functions and mixed continuous-discrete design variables. The proposed model is demonstrated by two engineering examples with differing complexity to demonstrate its applicability.

Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modeling

In a previous paper, van Werkhoven et al. (2008b) demonstrated that the information content of streamflow observations at a watershed outlet is a dynamic entity and is dependent on the spatiotemporal dynamics of the causal precipitation event. This result has important consequences for distributed hydrological model calibration strategies and for the design of observation networks. However, the conclusions drawn were based only on the analysis of the model parameter sensitivities to the hydrograph peak fit because of the use of the root‐mean‐square error objective function. An unanswered question is how will the previous result change if alternative objective functions are used? Here we extend the earlier analysis by adding low‐flow and water balance objective functions. We study their impact on how much information can be extracted during calibration overall and for specific model components (parameters) using a synthetic rainfall‐runoff event. Results suggest that both vertical (within a model cell) and spatial (across cells) sensitivities vary greatly with the objective function used. Timing‐related objective functions show sensitivity largely focused on the area close to the outlet, while a volume‐based objective function shows sensitivity distributed more evenly across the watershed. These results demonstrate the importance of using multiple evaluation metrics when assessing distributed model predictions. The resultant multiobjective sensitivity maps provide helpful tools for assessing the actual information provided by gauges in observation networks and motivate the need for a new generation of dynamic calibration strategies that would consider how the spatial parameter controls on the model response of interest vary in time.

Multiobjective sensitivity analysis of sediment and nitrogen processes with a watershed model

This paper presents a computational analysis for evaluating critical non‐point‐source sediment and nutrient (specifically nitrogen) processes and management actions at the watershed scale. In the analysis, model parameters that bear key uncertainties were presumed to reflect the importance of natural processes and/or management actions that they represent. The multiobjective generalized sensitivity analysis and the tree‐structured density estimation procedures were used in combination to investigate correlation structure in the parameter space while accounting for multiple objectives. Using the Soil and Water Assessment Tool, this framework was applied to the Dreisbach watershed in Indiana in the Midwestern portion of the United States. Results showed that incorporation of parameter interactions and multiple objectives is essential to obtaining conclusive information about critical system processes and management actions. Interactions between surface runoff volume and within‐channel processes were critical to describe transport of sediments in the study watershed. Key management actions for total nitrogen control were found to be nitrogen fertilizer application and upland farming practices. The sensitivity analysis reported herein could be used to derive a list of key non‐point‐source best management practices for development of watershed management plans. Implications of the analysis that are relevant to calibration of complex watershed models are discussed.