Optimal Design Variables Research Articles

A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems

When dealing with optimization problems, the introduction of uncertainty will greatly increase the difficulty of solving the problem. The traditional reliability-based design optimization (RBDO) algorithm coupling uncertainty parameters and design variables, and solve optimization with optimization algorithms. The traditional RBDO scheme is easy to understand and realize, but it will greatly increase the calculation time, and it often does not get ideal results when solving some problems with high time complexity. The Kriging-based decoupled non-probability reliability-based design optimization (K-BRBDO) scheme proposed in this paper decouples uncertainty parameters and design variables, establishes a decoupled optimization system based on Kriging model, and proposes a new adaptive learning strategy which involves two stages of enrichment to improve the accuracy of the surrogate model in the region of interest. Finally, the effectiveness and accuracy of the optimization scheme are verified by two numerical examples and an engineering example.

Design and fabrication of a fixed-wing Unmanned Aerial Vehicle (UAV)

Unmanned Aerial Vehicles (UAVs) have been widely used both in military and civil across the world in recent years. Nevertheless, their design always involves complex design optimization variables and decisions. Therefore, this paper aims to guide through the designing, manufacturing, and testing of an electrically powered radio-controlled aircraft for achieving a take-off, cruise, safe landing and carrying the highest payload possible. The whole process involves several phases, design phase, structural analysis, performance analysis, materials used, manufacturing, and finally aircraft testing. The final aircraft was designed with an empty weight and maximum take-off weight of 15.43 Ibs and 33.07 Ibs respectively while the wingspan, cruising speed and maximum speed were 70.1 in., 46 ft/s and 78 ft/sec respectively with a total take-off distance of 100 ft.

An explicit structural optimization method for temperature-sensitive hydrogel actuation devices

Hydrogel actuation devices are commonly composed of responsive and passive materials, and their actuation effect depends on their unique structure. In this paper, an explicit optimization method is proposed for the design of temperature-sensitive hydrogel-based soft devices involving swelling and shrinking to achieve specific actuation effects. In this method, the outline of configuration and the responsive layer are explicitly described by the ordered B-spline curve and movable hydrogel patch, respectively. The parametric modeling method is proposed to construct the geometric model with local features, which can transform the complex patch stacking problem into the fusion problem of three-dimensional geometry. Based on the large deformation theory of temperature-sensitive hydrogel, the deformation behavior of soft device is accurately simulated. The adaptive mesh division method is applied to numerical simulation of complex structures to avoid mesh distortion. Furthermore, the optimization problem is effectively solved by combining the gradient optimization algorithm. Several numerical examples are presented to verify the accuracy of the method, and interesting structures such as the Miura-ori structure, switch and bionic structures are designed. The method can significantly reduce the optimization design variables, and the explicit optimization results can be used as the basis for the additive manufacturing of soft devices.

A feasibility study of a tube bundle exchanger with phase change materials: A case study

Latent heat thermal storage offers a flexible service to members of a heat district grid as the stored heat can be used to reduce the morning peak demand, limiting the consequences for the production facilities. This work investigates the optimized design of a latent heat thermal storage reactor, integrated in an existing building supplied by district heating, for demand-side management applications. The storage reactor was designed as a tube bundle heat exchanger in which a commercial-grade paraffin was used as the phase change material. The optimized design variables that maximized the use of the Phase Change Material were the tube pitches and the fin heights. The integration of the storage reactor with the energy system required the control and prediction of its state of charge and the power output. These parameters are usually evaluated through 3D numerical dynamic simulations that require a large computational effort, which is beyond the capability of basic microcontroller systems. A simplified parametric model has been used to overcome this issue. A load-tracking algorithm was embedded in an existing building program. The algorithm was able to reduce the thermal power peak by 62 kWh from 6:00 to 9:00 a.m. Decreasing the pitch from 95 mm to 82 mm led to a +34% increase in the use of the PCM. Similar results were obtained when the fin height was increased from 20 mm to 32 mm. The results of this investigation suggest that the proposed model could be applied to similar buildings fed by district heating network systems.

Topology Optimization of the IPMSMs Considering Both the MTPA and FW Controls Under the Voltage and Current Limitations

This study proposes a novel topology optimization which can maximize the average torque of the interior permanent magnet synchronous motors (IPMSMs) in both the maximum torque per ampere (MTPA) and field weakening (FW) regions. In principle, the back electromotive force (EMF), which is proportional to a rotating speed, should be suppressed below the DC-link voltage to protect a power supply system. However, because the design and current reference of the IPMSM are coupled to affect the back EMF, it has been challenging to simultaneously determine the optimal design and current reference under such a voltage limitation. To solve the above issue, this study derives the design-dependent FW current reference in terms of the elementwise relative densities, which are set as design variables in topology optimization. In addition, the selection criterion between the MTPA and FW operations is proposed under the same design parametrization. After estimating the motor-design parameters, the proposed method simultaneously determines the optimal design and the corresponding current reference in both the MTPA and FW regions. The simulation and experimental results clearly demonstrate the validity of the proposed method.

Multi-objective optimization design of shaftless rim-driven thruster

This paper proposes a multi-objective optimization design method for shaftless rim-driven thruster (RDT) based on the ISIGHT platform. The pitch ratio, the blade area ratio and the advance coefficient of RDT were considered as the design optimization variables, the thrust and efficiency of the RDT were the optimization objectives. The multi-objective optimization design method was based on the surrogate module and the optimization module in the ISIGHT platform. Two analytical methods (response surface methodology (RSM) and radial basis function Model (RBF)) were used to build the surrogate model. The Muti-Island GA optimization algorithm was adopted in the optimization module. The paper indicates that both RSM model and RBF model are feasible to build the surrogate model, the RBF model has better accuracy and reliability than RSM model. The blade obtained by RBF method has larger thrust and smaller torque than that of the blade obtained by RSM method. For the large advance coefficient ( J > 0.6), the efficiency of the blade obtained by RBF method is slightly higher than that of the blade obtained by RSM method.

The Effects of Near-fault Ground Motions on Optimized Triple Friction Pendulum in Asymmetric Superstructures

The effect of near-fault ground motions containing translational and rotational components is investigated by considering the analytic seismic response of a stiff single-story base-isolated asymmetric superstructure with triple friction pendulum bearing (TFPB). Firstly, six asymmetric base-isolated structures instrumented by optimum TFPBs were exposed to translational components of near-fault excitations with wide ranges of pulsive periods. Next, four single objective functions as target responses (including top floor absolute acceleration, maximum inter-story drift, base shear of structure, and isolator displacement), as well as a combined fitness function as a performance function, were determined to consider all the seismic responses simultaneously. Hence, the optimum design variables of TFPBs (including curvature radii, displacement capacity, and friction coefficient) were calculated using the Genetic Algorithm (GA) to minimize the fitness and performance functions separately for each defined eccentricity. Finally, the structural behavior in the presence of mass eccentricity on the optimum selected TFPB under transitional and transitional-rotational components was explored for a more careful comparison. Also, fifteen combinations of translational-rotational components were employed for a comprehensive investigation. The results demonstrate that roof acceleration could be increased by about 7.5% when mass eccentricity and rotational components are present at the same time.

Virtual data-based generative optimization using domain-adaptive designable data augmentation (DADDA): Application to electric vehicle design

In the field of virtual product development, product performance is evaluated at an early stage through metamodel-based optimization using simulation, reducing development time and cost, and improving product quality. However, it takes a lot of time to construct a simulation model. The model must go through calibration and validation process to reduce errors with actual systems. This study proposes a novel engineering design process that can perform virtual data-based generative optimization by adapting the design domain of existing systems to those of new systems without requiring many simulations. A domain-adaptive designable data augmentation (DADDA) algorithm is proposed using inverse generator-implemented data augmentation, domain adaptation, and design optimization techniques. The DADDA algorithm can quickly provide optimal design variables for new systems compared to the genetic algorithm-based approximate optimization. The proposed process was applied to electric vehicle design. Driving responses and design variables related to safety performance were selected, and a small amount of training data was obtained using Modelica-based electric vehicle. As a result, it was shown that DADDA could significantly reduce the time required to derive optimal design variables by approximately 53% compared with the existing method. In addition, it is possible to derive the optimal performance for a new electric vehicle, which is approximately 21% better than that of an existing vehicle.

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.

Liquid hydrogen storage and regasification process integrated with LNG, NGL, and liquid helium production

Hydrogen (H2) is a clean energy carrier and has recently gained significant attention. Efforts are being made to promote liquid hydrogen (LH2) owing to its long-time storage, transportation, and high-purity end-use requirements. LH2 storage and regasification is an essential step in the H2 supply chain. This study proposes a novel integrated energy system that produces liquefied natural gas (LNG), natural gas liquids (NGL), and liquid helium (LHe) utilizing the cold energy of LH2. A well-known process simulation software (Aspen Hysys® v11) is employed to simulate the integrated process. The optimal design variables are found through the Aspen Hysys® built-in Mixed optimization technique. The optimal design shows a specific energy consumption of 0.347 kWh/kg with exergy destruction of 43.3 MW. The required volume of the spherical tank for LH2 storage is 6.3 m3. The results of the lifecycle cost analysis show that the Levelized cost of producing LNG, NGL, and LHe is 0.50 USD/kg, 0.56 USD/kg, and 98.0 USD/kg, respectively. The conclusion of this study suggests that the proposed process can improve the overall H2 supply chain by effectively utilizing LH2 for LNG, NGL, and LHe production.

Dynamic concurrent topology optimization and design for layer-wise graded structures

There are many requirements in engineering fields to improve the structural performance. Thus, the hierarchical structures have attracted lots of attention because of the appealing material efficiency and functionality, e.g., the bearing capacity and interfacial strength from large aerospace structures to small device facilities. However, the dynamic topology optimization design of these structures composed of multiple microstructures remains challenge by the connectivity issue between different microstructures and the extensive computational cost when handling the interval-frequency problem during multiscale concurrent design. In this work, the connectivity model is proposed for improving the manufacturability of hierarchical structure, which includes the connectable layer scheme between different microstructures and the enlarged filter domain. To break the bottleneck of computation cost induced by the simultaneous optimization of macro-microscopic design variables, the combined method of modal superposition and model order reduction is incorporated to efficiently compute the frequency response. Two response vectors are presupposed before decoupling sensitivity analysis. In addition, in order to implicitly ensure the bearing capacity of the structure, the stiffness maximization design is integrated into the response minimization problem when the frequency interval range is high. The layer-wise graded cantilever beam is used as an example to demonstrate the effectiveness and accuracy of proposed method in handling dynamic concurrent topology optimization problem. The manufacturability and systematic performance of hierarchical structures are significantly improved when they simultaneously experience the interval-frequency harmonic and static loads.

Resistance reduction optimization of an amphibious transport vehicle

The resistance reduction optimal design of amphibious vehicles is essential to improve efficiency and reduce energy consumption. In this paper, the hydrodynamic characteristics of an amphibious transport vehicle are investigated by Computational Fluid Dynamics (CFD) method based on the Reynolds-averaged Navier-Stokes (RANS) solver. A set of towing tests are conducted to verify grid independence and computational accuracy. Four typical shape control parameters are selected as optimal design variables and the independent effects of each variable are analyzed. The sample set is obtained using Latin Hypercube Sampling (LHS), and corresponding three-dimensional models are generated based on the parameters of each sample point. CFD calculations are then used to obtain the resistance characteristics of the models. The Kriging method is utilized to establish an approximate model, and the credibility of the model is verified. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used to find a global optimal solution or set based on approximate models. Two optimized configurations, Opt1 and Opt2, are obtained, corresponding to single- and multi-objective optimization results, respectively. At the design speed of the amphibious transport vehicle, Opt1 has the highest resistance reduction rate of 12.7907% under the full load condition, while Opt2 has resistance reduction rates of 11.0863% and 8.5293% with and without load, respectively.

Data-driven non-intrusive shape-topology optimization framework for curved shells

Aiming at obtaining an optimal configuration of curved shells in the compact design space, it is an effective way to perform the shape and topology optimization simultaneously. Considering the complexity in solving the coupled shape and topology optimization problem, this paper presents an easy-to-implement data-driven non-intrusive shape-topology optimization framework for curved shells, including two steps. In the off-line step, the shape equation-driven mesh deformation method is firstly proposed, contributing to using a small number of design variables for fast shape change and optimization of the skin of curved shell without re-modeling and re-meshing. Latin hypercube sampling is used to generate sample points as the input data in the design space, and the corresponding skin shape is obtained. In each design domain determined by different skin shapes, the topology optimizations are performed in parallel or by multiple optimization solvers, after that above multi-source optimization results are collected into the output dataset. The mapping relationship is trained between the input data and output data via surrogate modeling technique, and this non-intrusive concept contributes to integrating the independent shape and topology optimization into a single-loop data-driven optimization. The cross validation-Voronoi adaptive model updating method is used to improve the prediction accuracy. In the on-line step, the single-loop data-driven optimization is carried out using covariance matrix adaptive evolution strategy and adaptive optimization updating method, which can obtain optimized results more efficiently than direct topology optimization or shape optimization method. In order to verify the effectiveness of the proposed data-driven non-intrusive shape-topology optimization framework, three optimization examples are carried out. Example results show that compared with the results by the single topology optimization, the strain energy results by the proposed framework can be reduced by 20.55%, 15% and 51.6%, respectively, highlighting the outstanding optimization ability of the proposed framework.

Thermal performance of solar photovoltaic panel in hot climatic regions: Applicability and optimization analysis of PCM materials

A numerical modeling methodology based on the finite element method is utilized in this investigation to predict the impact of the PCM characteristics on the system cooling performance and the solar module output power. The thermal analysis considers the transient nature and the nonlinearity of the problem. The boundary conditions include the cyclic variation with time of solar irradiance and ambient temperature in a hot climatic region, with peak temperatures close to 50 °C. Under these circumstances and owing to the various influencing parameters, choosing an appropriate PCM is a challenging task. By allowing the PCM characteristics to vary continuously over the applicable domains and not just by giving specified discrete values, a systematic approach is adopted in this work. This technique is applied to explore the relationships between system input parameters, as defined by the PCM properties, and output variables, as described by cell temperature and module efficiency. Due to the harsh boundary conditions, the obtained results demonstrated that PCM with high thermal conductivity is required to achieve efficient thermal regulation. The enhanced material characteristics can only be provided by composite PCMs with graphite or metal additives. The resulting optimized design variables of the suitable PCM, melting temperature, thermal conductivity, and thickness, can reduce the cell temperature to 57 °C (20% reduction). The corresponding efficiency and power output are raised by around 8%. The power output was found to increase from 125 W (reference case) to 135 W for the optimized case. The results suggest that using a passive cooling system based on PCM is a viable method for enhancing the power output of a solar PV module. The created coupled model predicts the thermal behavior of the PV module and significantly facilitates the PCM selection procedure to ensure its applicability.

Optimization of design variables and control rules in field development under uncertainty: A case of intelligent wells and CO2 water alternating gas injection

This paper focuses on life-cycle optimization of oil field development plan under uncertainty. The optimization problem included a wide range of design variables and control rules related to wells and platform in a realistic benchmark case (UNISIM–II–D) with a known ground truth reservoir model, UNISIM–II–R, that resembles Brazilian pre-salt fractured carbonate reservoirs in their early development stage. The design variables were the number and location of wells, the fluid processing capacity of the platform, and the location of internal control valves (ICVs), whereas the control rules were the setting of ICVs, production and injection rates, and the duration of the water-alternating-gas (WAG-CO2) cycle. An iterative sequential optimization framework was developed to deal with the massive search space and complex parameterization. The optimization further took into consideration the subsurface, operational and economic uncertainties, and used iterative discrete Latin hypercube method as the search algorithm. The robust optimization was carried out on a subset of representative models derived from the reduction of a large ensemble of data-assimilated models. As a low-fidelity representation of the compositional fluid model, a black-oil model was used to reduce simulation runtime. To validate our optimization framework, we applied the optimal development strategy to the ensemble of compositional simulation models, as well as the ground truth model. The true model's responses were within the ranges predicted by the compositional ensemble, confirming the optimization framework's reliability. The general methodology developed in this study, as well as our findings, can be used to optimize other similar real-world complex and high-risk field development projects, and are especially useful in closed-loop field development and management practices.

Techno-economic design of a solar combisystem in a residential building

A solar combisystem is utilized to supply domestic hot water (DHW) and heat a residential building. This system decreases the fossil fuel consumption and, hence, reduces air pollution and global warming. This paper proposes an innovative method to design and optimize a suitable solar combisystem for a residential building with respect to technical and economic parameters. First, different configurations of solar combisystems, including various components, are considered. Then, the optimum design variables are determined for each configuration using Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Total Solar Fraction (TSF) and Life Cycle Cost (LCC) are considered as objective functions. Finally, the optimum design is chosen for the solar combisystem. Typically, researchers have focused on evaluating the performance of a single solar combisystem configuration in each study. However, this research takes a different approach by optimizing multiple configurations, resulting in a significant improvement in the total solar fraction by 7.3%. This is the main novelty of this paper. Based on the results, the optimum configuration of the solar combisystem includes 17.91 m2 of evacuated-tube collectors with a tilt angle of 50°. Also, the volume of the hot water tank and heating buffer tank are, respectively, equal to 204 and 500 L (L). In this system, solar energy provides 94% of the required energy for supplying DHW and 23% of the energy for heating the building. Moreover, reduction of annual CO2 emissions is 1806 kg. This paper presents a guideline to design solar systems for the residential buildings considering technical and economic aspects.

Free vibration analysis and multi-objective optimization of lattice sandwich beams

The free vibration dynamic modellings of sandwich beams with periodic lattice truss cores are established by combining the Bernoulli–Euler beam theory and Timoshenko beam theory, and the fundamental frequency and unit cell mass of lattice sandwich beams are optimized based on the non-dominated sorting genetic algorithm-II (NSGA-II). Firstly, the vibration control equation of lattice sandwich beams is derived using Hamilton’s principle. Then, it is compared and verified by modal experiment and finite element numerical simulation. On this basis, the influence of different truss cores on the structure’s natural frequency is further analyzed. Finally, taking the sheet thickness, core thickness, and radius of the truss as the optimization design variables and taking the structural fundamental frequency maximization and mass minimization as the optimization objectives, the Pareto front is obtained by NSGA-II, and the optimization results are verified by theoretical and numerically. The research results show that the theoretical method in this article can well predict the natural frequencies of sandwich beams with different truss cores. The optimized sandwich structure has higher fundamental frequency with lighter weight, which effectively improves the anti-vibration ability.

Energy-saving potential assessment of a classroom building envelope through sensitivity analysis and multi-objective optimization under different climate types

The present work aims to optimize the thermal behavior of a building envelope by combining sensitivity analysis (SA) and multi-objective optimization (MOO). An existing classroom located in Marrakech city was considered a case study building. The building model was analyzed under six Moroccan climate zones. The SA was applied on 16 design variables and performed using the Morris method implemented in the tool Simlab to rank each design variable based on its influence on the objective function (overall energy demand). The SA results showed that the solar absorptance of the internal roof, wall, and ground floor and the ground hollow core slab thickness impacted less the overall energy demand. Therefore, the only remaining variables showing the most relevant effect will be optimized afterward. The optimization phase was conducted by coupling the generic optimization tool GenOpt with TRNSYS. The optimum solution was selected based on the Pareto front approach. The obtained results assessed the effectiveness of the adopted methodological approach in significant minimization of the required thermal loads. Furthermore, the values of each optimum design variables set differ from one climate zone to another; leading to energy demand reduction varying from 30 to 42%, in comparison with the original design building.

Multi-objective optimization of printed circuit heat exchanger with airfoil fins based on the improved PSO-BP neural network and the NSGA-II algorithm

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

Co-Optimization of Design and Control of Energy Efficient Hybrid Electric Vehicles Using Coordination Schemes

AbstractDesign and control co-optimization studies for hybrid vehicles have been proposed in the past. However, such works suffer from difficulties arising due to (a) diverse real- and integer-valued variables, (b) complex nonlinear powertrain dynamics and design interconnections, (c) conflicting objective functions with path constraints, and (d) high computational resources requirements. To meet these challenges, this study presents an efficient co-optimization framework for hybrid electric vehicles (HEVs) which is built using existing algorithms and coordination schemes. Particular emphasis is given to the simultaneous scheme and the decomposition-based scheme. The decomposition-based scheme with the problem decomposition proposed in this work can efficiently handle multitime scale state variables and both integer- and real-valued design and control optimization variables. This is demonstrated by solving the mixed-integer optimal design and control problem of a series hybrid vehicle over a 1-h long drive cycle with time discretization of 1 s. The problem complexity is elevated by using an increasing number of state variables (including battery state of charge, battery energy, and after-treatment system temperature), control variables (such as the engine power and engine on/off), and design parameters (such as the number of battery cells and the type and size of the engine). In addition, a multi-objective cost function is used to find a tradeoff solution between fuel consumption and emissions minimization. The results show that in terms of optimality of the solution, the decomposition-based scheme is comparable with the simultaneous but can give a 14% improvement in computational performance. The effectiveness of the proposed framework is demonstrated by comparing the co-optimization results against a baseline case in which only the optimal control problem is solved. The co-optimized solution yields up to 3.7% average genset efficiency improvement and a fuel consumption reduction to 1.6 kg from 2.5 kg, which is further reduced to 1.5 kg by adding the engine on-off control. Finally, a decision matrix is developed to provide guidance on the selection of the optimization algorithm and coordination scheme for any problem at hand.