Sensitivity Of Design Variables Research Articles

Optimization and knowledge discovery of profiled end walls in a turbine stage at a low Reynolds number

To comprehensively explore flow control method of profiled end wall for turbine stage at low Reynolds numbers, a surrogate model optimization platform including non-uniform rational B-spline surface parameterization method, support vector regression, and improved chaos particle swarm optimization algorithm is integrated. Optimization designs have been carried out for stator profiled end walls, rotor profiled end wall, and combined end walls, respectively. The results indicate that under the constraint of the output power, the application of various profiled end wall design cases all can effectively improve the aerodynamic performance of the turbine stage. By organizing the flow field of downstream rotor, the profiled end wall of stator can significantly affect the stage efficiency. The flow control benefits of the profiled end wall of the rotor is from the obstruction of the cross migration of the pressure side leg of the horseshoe vortex. The application of profiled end wall on stator has the most practical engineering value. Self-organizing maps and Shapley methods are used to explore potential correlation information of aerodynamic parameters and summarize design experience. The sensitive design variables of profiled end walls are extracted. Based on the local controllability of NURBS surfaces, the regions that affect the stage efficiency are mainly concentrated in the middle of the stator passage, near the stator trailing edge and near the rotor leading edge. The regions with a significant impact on the output power of the turbine stage are near the trailing edge of the rotor and stator. The corresponding design rules of end walls modeling are summarized.

Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Assisted living facilities with optimal summer thermal comfort are necessary to ensure improved quality of life for the older individuals in warming climates. Older individuals are exposed to higher mortality risks during periods of excessive heat. This study aims to identify and optimize key design variables for a passive house certified assisted living facility in Belgium with high comfort expectations using EnergyPlus to improve indoor thermal comfort during extreme climates. The sensitivity analysis used the Standard Regression Coefficient method with Latin Hypercube Sampling for eighteen design variables under four categories: layout, envelope, operation, and system. The analysis identified flat roof and external floor insulation as the most influential parameters, whereas lighting gain and cooling setback temperature were the least influential parameters. Furthermore, optimizing the reference building with the most sensitive design variables using a genetic algorithm based on the NSGA-II method found ideal configurations that would ensure minimum thermal discomfort hours during extreme summers. The study findings provide an evidence-based approach for building engineers and designers for early-stage design of assisted living facilities that maintain optimal comfort in mixed humid climates of Europe.

A Surrogate Modeling Space Definition Method for Efficient Filter Yield Optimization

Surrogate models are widely used in filter yield optimization methods to improve efficiency, which can be divided into online and offline. State-of-the-art offline surrogate model-based filter yield optimization methods are shown to be effective for filter cases with more than ten sensitive design variables. In these methods, a keystone is the appropriate definition of the space for building the surrogate model, deciding success/failure, or at least the efficiency of the yield optimization. However, there is a lack of systematic methods to achieve it. To address this challenge, a new method, called pattern search optimization-based surrogate modeling space definition method (PSOMSD), is proposed. The performance of PSOMSD is demonstrated by a real-world filter case with 14 sensitive design variables. Analysis shows the appropriateness of the defined surrogate modeling space and advantages compared to empirical methods.

Analysis and Research on No-Load Air Gap Magnetic Field and System Multiobjective Optimization of Interior PM Motor

In this article, considering the influence of iron core saturation and complex boundary conditions, a novel no-load magnetic field analytical method and system multiobjective optimization model for interior permanent magnet synchronous motor (IPMSM) are proposed. First, an analytical method based on magnetic potential permeance method is proposed. The complex cogging effect and equivalent air gap permeability function are fully considered in this method. Furthermore, on the basis of Taguchi optimization design based on Fuzzy theory, an improved optimization model considering multivariable interaction is proposed. Among them, in order to comprehensively consider the performance of electromagnetic, vibration, and temperature rise, five optimization objectives including torque, pulsation, cogging torque, loss, and flux density are optimized. The sensitivity of design variables and the interaction between variables are not ignored. Finally, a 6-pole 36-slot IPMSM is manufactured for prototype experiments. A large number of prototype experiments, finite element, and computational fluid dynamics simulation analysis including no-load magnetic field, back electromotive force, temperature rise, electromagnetic torque, and so on are carried out. The accuracy and rationality of the proposed no-load magnetic field analysis method and global optimization model are well-verified.

Effects of climate change in the thermal and energy performance of low-income housing in Brazil—assessing design variable sensitivity over the 21st century

To increase the accuracy of energy efficiency interventions over a building's lifetime, it is essential to predict the impact of climate change. Studies have analyzed building performance under future environmental conditions in Brazil, but the influence of design variables in the whole thermal and energy performance is a topic that still needs to be addressed. The objective of this study was to analyze the effects of climate change on the thermo-energetic performance of Brazilian low-income housing in the climates of two cities: Belém and São Paulo. For this, the research was based on computer simulations and Morris global sensitivity analysis, observing the influence of design variables in different periods. Weather files were generated to represent future environmental conditions according to the Intergovernmental Panel on Climate Change A2 scenario. The results prove that building design must necessarily consider climate change for a resilient built environment and highlight the importance of design variables, especially in the São Paulo climate. In view of future higher temperatures, the sensitivity indices indicate that natural ventilation is crucial for building performance improvements, followed by solar absorptance of the envelope and thermal transmittance of the walls. Finally, this study contributes to design decision-making for low-income housing in tropical climates and underscores the need for investigation of how to design resilient buildings, focusing on strategies to mitigate climate change and reduce pollutant emissions.

An efficient optimization of an irreversible Ericsson refrigeration cycle based on thermo-ecological criteria

• Thermo-ecological problem of Ericsson refrigeration cycle is developed and solved. • Multi-objective optimization of Ericsson refrigerating cycle is investigated. • Investigation of the effect of operating parameters on the system performance. • Investigation of the sensitivity of design variables on the thermo-ecological objective. In the present work, a multi-objective optimization study of an irreversible Ericsson cycle is carried out to evaluate a cryogenic refrigerator system based on thermal and ecological performance. The coefficient of performance, cooling load and input power are considered as objective functions along with an ecological objective function and the effect of variables like the effectiveness of the heat sink heat exchanger, the effectiveness of the heat source heat exchanger, the temperature ratio of heat sink and heat source and the capacitance rate of the heat sink and heat source. Objective functions are optimized using a heat transfer search optimization algorithm for two different scenarios, and the Pareto front is obtained. The decision making method namely TOPSIS is adopted to identify the best solution among numerous optimal solutions for each scenario. The effect of design variables on thermo-ecological objective functions is explored and presented. A maximum cooling load of 3.27 kW at heat sink temperature of 172 K is obtained for a cryogenic refrigerator operating on an irreversible Ericsson cycle at an input power of 3.91 kW with a COP of the system as 0.84. Finally, the distribution of the design variables during the optimization is identified and presented.

Earth pressure in narrow cohesive-fictional soils behind retaining walls rotated about the top: An analytical approach

There is currently a lack of an available design approach to estimate the earth pressure in narrow backfills behind retaining walls rotated about the top (RT). The considerations of some significant factors, primarily load transfer mechanisms (soil arching effect and horizontal shear stress in soils), failure mechanisms (shape and number of slip surfaces) and soil cohesion are often neglected for brevity in routine design. Such simplifications may lead to significant deviations from reality. This paper first uses the finite element limit analysis (FELA) technique to identify the underlying failure mechanisms and load transfer mechanisms. The results observed in FELA models indicate that active rotation of walls about the top develops one curved slip surface, which can be approximated by the log-spiral function. Under the soil arching effect, the upper intermediate passive zone with major principal stress rotation trajectory and the lower active zone with minor one can be defined. The arched differential element method (ADEM) is then introduced to formulate the earth pressure calculation. The results from newly published tests, existing analytical approaches, and FELA are compared to validate the accuracy of the proposed approach in both purely-frictional and cohesive-frictional soils. Parametric studies are further conducted to thoroughly understand the earth pressure problems, considering the effects of sensitive design variables (e.g. aspect ratio, soil strength parameters, and wall-soil interface friction angle). The analytical approach presented here would be a great extension to the design guidelines for the retaining structures with narrow backfills.

A Microwave Filter Yield Optimization Method Based on Off-Line Surrogate Model-Assisted Evolutionary Algorithm

Most existing microwave filter yield optimization methods target a small number of sensitive design variables (e.g., around 5). However, for many real-world cases, more than ten sensitive design variables need to be considered. Due to the complexity, yield optimization quality and efficiency become challenges. Hence, a new method, called yield optimization for filters based on the surrogate model-assisted evolutionary algorithm (YSMA), is proposed. The fundamental idea of YSMA is to construct a single high-accuracy surrogate model offline, which fully replaces electromagnetic (EM) simulations in the entire yield optimization process. Global optimization is then enabled to find designs with substantial yield improvement efficiently using the surrogate model. To reduce the number of necessary samples (i.e., EM simulations) while obtaining the required prediction accuracy, a customized machine learning technique is proposed. The performance of YSMA is demonstrated by two real-world examples with 11 and 14 design variables, respectively. Experimental results show the advantages of YSMA compared to the current dominant sequential online surrogate model-based local optimization methods.

A time-varying reliability-based robust design method considering overall efficiency of axial piston pump

Axial piston pumps (APPs) are the core energy conversion components in a hydraulic transmission system. Energy conversion efficiency is critically important for the performance and energy-saving of the pumps. In this paper, a time-varying reliability design method for the overall efficiency of APPs was established. The theoretical and practical instantaneous torque and flow rate of the whole APP were derived through comprehensive analysis of a single piston-slipper group. Moreover, as a case study, the developed model for the instantaneous overall efficiency was verified with a PPV103-10 pump from HYDAC. The time-variation of reliability for the pump was revealed by a fourth-order moment technique considering the randomness of working conditions and structure parameters, and the proposed reliability method was validated by Monte Carlo simulation. The effects of the mean values and variance sensitivity of random variables on the overall efficiency reliability were analyzed. Furthermore, the optimized time point and design variables were selected. The optimal structure parameters were obtained to meet the reliability requirement and the sensitivity of design variables was significantly reduced through the reliability-based robust design. The proposed method provides a theoretical basis for designers to improve the overall efficiency of APPs in the design stage.

Topology optimization of thermoelastic structures using MMV method

Thermoelastic problems refer to a coupled phenomenon existing in a wide range of practical applications where elastic responses are influenced by temperature variation. Meanwhile, Moving Morphable Void (MMV) approach receives extensive attentions owing to explicit topological geometric information. In MMV method, a closed B-spline curve defined by several design variables is adopted to describe void material in design domain. In present study, we intend to use the MMV method to topologically optimize the thermoelastic structures under uniform temperature increment and varying temperature field. The weak formulations of heat conduction and elasticity are combined to describe the thermoelastic problems. The optimization mathematical model with the aim of compliance minimization and volume constraint is constructed. The sensitivity of design variables is derived by material derivative method and adjoint sensitivity analysis method. The method of moving asymptote (MMA) algorithm is implemented to update the design variables for finding the best material distribution. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

Transmitter Module Optimization for Wireless Power Transfer Systems with Single Transmitter to Multiple Receivers

Most of the coil designs for wireless power transfer (WPT) systems have been developed based on the “single transmitter to a single receiver (S-S)” WPT systems by the empirical design approaches, partial domain searches, and shape optimization methods. Recently, the layout optimizations of the receiver coil for S-S WPT systems have been developed using gradient-based optimization, fixed-grid (FG) representation, and smooth boundary (SB) representation. In this paper, the new design optimization of the transmitter module for the “single transmitter to multiple receivers (S-M)” WPT system with the resonance optimization for the S-M WPT system is proposed to extremize the total power transfer efficiency while satisfying the load voltage (i.e., rated power) required by each receiver and the total mass used for the transmitter coil. The proposed method was applied to an application model (e.g., S-M WPT systems with two receiver modules). Using the sensitivity of design variables with respect to the objective function (i.e., total power transfer efficiency) and constraint functions (i.e., load voltage of each receiver module and transmitter coil mass) at each iteration of the optimization process, the proposed method determines the optimal transmitter module that can maximize the total power transfer efficiency while several constraints are satisfied. Finally, the optimized transmitter module for the S-M WPT system was demonstrated through comparison with experiments under the same conditions as the simulation environment.

Early stage design for an institutional net zero energy archetype building. Part 1: Methodology, form and sensitivity analysis

This paper is the first of two that examine the influence of building form, including courtyards, on the energy performance of medium-sized institutional solar net zero energy buildings incorporating building-integrated photovoltaic/thermal (BIPV/T) roof systems. The inclusion of BIPV/T at the early design stage along with passive design techniques extends to energy flexibility considerations with a smart grid. A two storey 3 000 m2 medium-sized archetype building was considered. Through simulations, different building plan shapes and BIPV/T roof tilt angles supported by a range of related building enclosure variables such as insulation level and window characteristics were analyzed and compared. The design configurations used BIPV/T heat recovery for winter ventilation air preheating in the Montreal region.In this first paper, the methodology is presented. Then, using annual net energy use intensity as the target, a global sensitivity analysis was performed to rank and quantify the most sensitive design variables at different BIPV/T roof slopes. Out of the nine input variables analyzed, the most influential by rank were the orientation, the window to wall ratio for the south and north façades, and the plan shape, for roof tilt angles of 35°, 40°, and 45°. For a roof tilt angle of 30°, the window to wall ratio of the south façade was the most influential, switching rank with the orientation. Although not an input variable to the sensitivity analysis, this indicates as well the influence of the BIPV/T roof tilt angle. Also, there was a significant interaction effect between the plan shape and orientation, highlighting the utility of analyzing interaction effects at the early design stage and the importance of these two factors in solar-enhanced buildings. At the other end of the spectrum, the window type, the wall and roof insulation level, and the window to wall ratios for the east and west facades were the least significant – at least approximately one order of magnitude less sensitive than the first ranked input.In the second paper, the methodology is applied in a case study for early stage design in which relative comparisons are made considering building form, net-zero energy, and extended to the grid considering load matching and energy flexibility.

Optimization-Based Strategies for Optimal Inverse Parameters Estimation for Heat Transfer Systems

Thermal design for electronic devices approached through the solution analysis of the Inverse Heat Transfer Problem (IHTP) has not been extensively explored. This article proposes an alternative strategy and a contrasting approach for the optimal inverse parameters’ estimation of heat transfer systems, particularly in designing heat sinks. A framework to tackle a Rectangular Microchannel Heat Sink (RMCHS) design modeled by the Entropy Generation Minimization (EGM) criterion is developed. This framework comprises two strategies to be compared. The serial proposal works sequentially depending on the parameters’ sensitivity into the RMCHS model, backpropagating estimated parameters to all processes. The parallel strategy processes all parameters simultaneously. Instead of focusing efforts on a typical optimization process, a sequential procedure takes advantage of the most influential parameters in the heat sink model and the excellent exploration-exploitation rate of Metaheuristic Optimization Algorithms (MOAs). The most sensitive design variables are prioritized in the serial strategy. The implemented estimation-optimization strategies are addressed through an IHTP’s inverse analysis. Thereby, global MOAs are implemented to solve the specific application and become an alternative to the gradient-based methods when their efficiency and effectiveness are at stake. MOAs show overall relative errors related to minimal entropy generation rate inferior to 0.07% for data with 30 dB of SNR and less than 7.63% of error for data with 10 dB of SNR compared with the Levenberg-Marquardt method. Numerical results show that serial strategy provided a stable and reliable design solution even with contaminated data, obtaining better performances than the multiparametric strategy. Additionally, parametric and nonparametric statistical tests were used to validate the appropriate optimization algorithm and the most reliable strategy. The statistical tests confirmed the optimal-inverse problem estimation and optimization improvement by combining the serial strategy and analyzed MOAs to design the RMCHS based on the EGM criterion.

Ascent trajectory design of small-lift launch vehicle using hierarchical optimization

An integrated algorithm to design the optimal ascent trajectory of a target Sun-synchronous orbit (SSO) for small-lift launch vehicles is studied in this paper. A general problem description is provided, including the specific pitch law designed for an ascent trajectory to SSO and the definitions of design variables and equality/inequality constraints. Then an integrated optimization scheme is introduced to solve this problem. In order to improve its efficiency, a preliminary determination method is to analytically generate two sensitive design variables related with the coast arc. With the help of the two design variables, an initial combination of design variables can be obtained by solving separately the optimization problems of two powered phases. Based on the predetermination method, an efficient integrated algorithm using hierarchical optimization is proposed. A simulation based on a type of small-lift Electron Rocket and Rutherford engine is performed using this hierarchical optimization algorithm and the feasibility and efficiency of this algorithm is evaluated. These systematic investigations on all the possible injected orbits give a full detail of launching capability for Electron Rocket.

Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search.

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

Magnetic–Mechanical Performance Analysis and Experimental Validation of Noncontact Coaxial Magnetic Gear for a Contra-Rotating Propeller in an Electric Outboard

To reduce carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emissions, it will be necessary to increase the efficiency of ships, and one approach to consider is the contra-rotating propeller (CRP). A CRP system can significantly improve the propulsion efficiency by recovering a part of the energy loss to the rotational flow and could reduce fuel consumption. Magnetic gears have significant advantages compared with mechanical gears in terms of low vibration, noise, abrasion, reliability and high efficiency driving, and overload protection, due to the physical isolation of the input and output shafts. Using the specifications of an electric outboard, the outer diameter and gear ratio of the magnetic gear were selected and a main effect analysis was performed to determine the sensitive design variables. Then, an optimal model was proposed using the response surface methodology, for effective design variables selected by the main effect analysis. In addition, the vibration stability of the optimal model was verified with a one-way coupled (magnetic-mechanical) performance analysis, based on the results of a 3-D magnetic field analysis.

Parametric design and analysis of a mortar base plate

In order to improve the efficiency of designing and analyzing a mortar base plate, parametric technology has been applied to the design and analysis of the base plate. By conducting characteristic analysis of the base plate, critical design parameters extracted have been driven as characteristics to perform secondary development on UG, so that the base plate could be automatically modeled in three dimensions. Meanwhile, Python has also been applied in the secondary development of Abaqus, so as to realize the automatic finite element modeling of the base plate. After conducting parametric analysis on the model established, sensitivities of design variables of the base plate have been analyzed using an approximate model in combination with the sample designed by the optimal Latin hypercube approach. Moreover, parametric optimization carrying out on results of sensitivity analysis could give feedback to parametric design for guiding parameter values of design variables to improve the design quality

Estimating the sensitivity of design variables in the thermal and energy performance of buildings through a systematic procedure

Abstract In the performance-based design context, buildings can be evaluated through simulation procedures to estimate their performance in different environmental criteria. In this context, a crucial issue is the identification of the most influent input variables in some specific performance criteria, which can only be appropriately assessed through a robust sensitivity analysis approach. Thus, the objective of this study is to develop a method to estimate the influence of design variables in the thermal and energy performance of buildings through a systematic procedure, using building simulation programmes and statistical tools. The method has a broad scope of combining a local with a global sensitivity analysis using the Morris elementary effects method, and uncertainty analysis of the performance criteria. A case study of a low-income house located in southern Brazil was evaluated and simulated in the EnergyPlus™ programme. Four different performance criteria were considered (such as the degree-hours for heating and cooling and the energy consumption for heating and cooling). The simulation experiment considered 21 design variables, such as the thermal properties of the construction components, openings characteristics, airflow parameters and solar orientation of the house. The study identified the most influential design variable in each criterion, highlighting the thermal transmittance and the solar absorptance of the roof, and the ventilation area of the windows. The combination of the local and global approaches leads to a better understanding of the behaviour of the model and can help the decision-maker to optimise the building performance more accurately.

Reliability Evaluation and Robust Design of a Sensor in an Entire Roller-Embedded Shapemeter.

The intermittence of the shape detection signal associated with an entire roller-embedded shapemeter roll, used in a seven-pass cold reversible rolling process, is considered. A transient interference at the sensor top surface and the distance between the sensor top surface and the roll outer surface are developed, and a sensor reliability evaluation model is derived. The reliability of the sensor is evaluated via the random perturbation method, and the reliability sensitivity of design variables is proposed. The analysis reveals that the reliability is smallest in the third rolling pass. Of the design variables considered, the initial interference exhibits the largest reliability sensitivity and has the greatest influence on the sensor reliability. A reliability robust design model of the initial interference is therefore developed. A new shapemeter roll is fabricated and tested in a 1050 reversible cold rolling mill. The test results are consistent with the theoretical results, thereby validating the proposed model. The selection of an appropriate initial interference provides an important means of overcoming the adverse effects associated with the thermal deformation of sensor contact surfaces.

A Novel Sequential-Stage Optimization Strategy for an Interior Permanent Magnet Synchronous Generator Design

For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.