Optimum Design Research Articles

Tri-objective optimal design of a hybrid electric propulsion system for a polar mini-cruise ship

The environmental impact, economic considerations, and passenger comfort of hybrid electric propulsion systems (HEPSs) are significantly influenced by the design phase. However, a design method solely focused on minimizing annual fuel consumption may inadvertently lead to increased life-cycle cost and decreased annual pure-electric sailing time. To address this issue, this paper presents a tri-objective optimization design method to achieve an optimal trade-off among the objectives for a polar mini-cruise ship. First, a scalable mathematical model is established for a hybrid diesel engines/batteries/shore power propulsion system. Then, a refined, rule-based energy management strategy is developed to control the power flow among energy sources. Next, a tri-objective optimization model with detailed descriptions of objectives, variables, constraints, and method is formulated. Finally, the performances of three optimal designs, obtained through tri-objective optimization, single-objective optimization, and conventional heuristics, are compared through simulations based on a typical real-world navigation case. The results highlight the clear advantages of the proposed method over the other two methods. Furthermore, sensitivity analysis underscores that the parameters of the diesel engine, battery, and gear ratio exert a greater impact on HEPS performances than other factors.

Silicon solar cell efficiency improvement employing electrostatically assembled polyelectrolyte–quantum dot multilayers

Down-shifting (DS) has proven to be an effective technique to address thermalization losses in photovoltaic devices. It involves the modification of the incident solar spectrum by luminescent species to better suit solar cell’s optimal absorption regions. Despite the number of favorable characteristics of quantum dots (QDs) as DS materials, their incorporation into highly ordered close-packed films remains the major drawback of QD-based DS applications. This limitation can be overcome by the sequential integration of QDs into a multilayered film via the layer-by-layer assembly technique, that allows the precise control of composition and thickness. The experimental results discussed herein indicate that upon optimization employing a model-assisted design approach, the spectral response of a silicon solar cell benefits from the down-shifting and antireflection capabilities of the QD film, as evinced by their quantum efficiency. For the optimum design, the incorporation of QDs triggered an increment of 26.1% in the power conversion efficiency driven by an increase of 29.2% in the short circuit current density. The controlled assembly of QD multilayers offers unique opportunities in light harvesting for new hybrid photovoltaic technologies.

Optimum Design of Reinforced Concrete Beams with Large Opening Using Neural Network Algorithm

It is quite rare to find researches in the literature connecting neural networks to design challenges in civil engineering, particularly with regard to beam with large openings. Therefore, to ascertain the best cost design for such a beam, an optimization technique is developed, written using MATLAB functions, and verified in this study. Because of the efficiency and dependability of the iterative Particle Swarm Optimization (PSO) process, the ACI 318-19 code method is adopted via this algorithm. Using four beam design (decision) variables, which are: beam width b, longitudinal tension reinforcement area As, longitudinal compression reinforcement area A’s, and vertical shear reinforcement area Av; the objective is to minimize the overall beam cost function. The constraints for each of the 60 randomly chosen particles are handled using the self-adaptive penalty function technique, which is applied in all the 100 iterations for each of the four proposed beam design case studies with identical openings. For the developed method, completing all iterations serves as a stopping criterion. Comparative studies are conducted to demonstrate how the compressive strength of concrete and the live load scheme affect the optimal overall beam cost and the associated design variables. The relations between cost of beams and the four design variables, which are presented through graphs and tables, indicate that the beam’s cost is significantly impacted by the loading condition. This is shown by increasing the cost of beam B1, with no sustained live loads, from 722 US dollars to 984 US dollars for beam B4, which has concentrated and distributed sustained live loads. Future research is advised to include modern strengthening techniques for beams with openings, and apply this research to a PSO algorithm with several objectives.

Optimum design of HNBR and PCR-based isolation system to protect a sensitive article stored in a naval vessel container subjected to shock load

In naval engineering, protecting sensitive articles from shock loads is a major concern, as it may lead to adverse consequences. This paper focuses on the design of a novel Hydrogenated Nitrile Butadiene Rubber (HNBR) and Polychloroprene Rubber (PCR)-based shock isolation system to protect a sensitive article stored in a hollow cylindrical naval vessel container (NVC). Firstly, NVC is modeled as a two-degree-of-freedom lumped parameter model (LPM), wherein isolators made of HNBR and PCR materials are modeled with 5-order polynomial nonlinear stiffness and linear damping. The optimal values of the isolator parameters are obtained using a multi-objective genetic algorithm, and corresponding responses to a shock load are found using MATLAB. Furthermore, a three-dimensional (3D) finite element model (FEM) of the NVC is generated in ABAQUS and subjected to shock loading in a transverse direction to conduct a comprehensive dynamic analysis of the isolation system. In 3D simulation, HNBR and PCR materials are modeled using a hyper-viscoelastic model developed based on experimental test data. The 3D simulation results of the NVC for the optimized design of HNBR and PCR isolators agree with the optimized results of the LPM. Moreover, the proposed HNBR and PCR isolators are very effective in shock reduction.

Influence of solar insolation on energy and exergy efficiency of a rectangular duct solar air heater with various types of V rib roughness: An analytical approach

In the current work, the energy and exergy evaluations for several V ribs in a rectangular duct SAH are evaluated and compared with smooth SAH for a heat flux range of 800 to 1200 W/m2. The analysis uses correlations for the friction factor and Nusselt number for different V ribs that have been developed by various researchers. The investigators' recommended optimum design parameters are employed to analyse the specific rib. The thermohydraulic and exergy efficiency of various V ribs are evaluated using the MATLAB code and comparisons of the findings are provided. The staggered discrete multi-V-shape rib configurations achieves the maximum thermal efficiency of 76.63% for Reynolds number of 21,000 and solar radiation heat flux of 800 W/m2. Corresponding to a Reynolds number of 21,000 and solar radiation heat flux of 1200 W/m2, the perforated multi-V-shaped rib gives a maximum thermohydraulic efficiency of 74.82%. At 800 W/m2 of heat flux, a maximum exergy efficiency of 5.17% is obtained for the staggered discrete multi-V-shape rib configuration at lower Reynolds number. Whereas, corresponding to higher Reynolds number, the maximum exergy efficiency is 4.18% for perforated multi-V rib.

Investigation of the geometric parameters on the performance of glass fiber reinforced polymer laminated mechanical joints under fatigue loading

AbstractThe present work optimizes the geometric parameters of glass fiber reinforced polymer (GFRP) laminated pin joints under fatigue loading conditions using response surface methodology (RSM). RSM is utilized to construct polynomial functions that optimally capture the behavior of experimental data, intending to make statistical inferences. Key geometric parameters, that is the edge and width dimensions were studied in pursuit of enhanced performance under fatigue loading of pin joints. The study aimed to validate the effectiveness of the optimized parameters, particularly for attaining load‐bearing capacity, fatigue life, and failure mode using numerical and experimental approaches. Optimal configurations were achieved, that is edge and width equal to 16 mm at which the failure mode shifts to bearing mode. Another configuration with an edge of 20 mm and a width of 22 mm was achieved for optimized fatigue life. These findings underscore the practical implications of the optimization process, with the potential for substantial gains in fatigue life and strength of GFRP laminated pin joints in engineering applications. The identified optimal configurations exhibited a non‐catastrophic bearing failure mode, emphasizing a crucial characteristic that provides prior‐warning before the occurrence of final failure. The experimental results align well with the numerical outcomes regarding fatigue life and the mode of failure.Highlights Parametric optimization of GFRP pin joint under cyclic loading. Optimization of GFRP pin joints via RSM for cyclic loading. Validation through numerical and experimental approaches. Comparison of fatigue life at optimum design points. Depiction of the failure modes using damage pattern under fatigue loading.

Optimum design of additively manufactured aerospace components with different lattice structures

Abstract Nowadays, the need for new technologies is increasing, especially to find solutions to the inadequacies in the production of complex structures. The additive manufacturing methods developed facilitate the production of complex parts and move the technology forward with factors such as cost and efficiency. With the optimization of new parts designed by additive manufacturing methods, it is possible to obtain the optimum product even in the most complex structures. At the end of the production process, the final product with the desired properties is obtained as a result of part size tolerance precision and optimizations. In this study, lattice optimization is applied to a passenger aircraft bracket. It is aimed to reduce the weight and, at the same time, increase the efficiency of the part by optimizing it with lattice structures. For this purpose, the Altair Inspire program was used, and the variation of mass, displacement, safety coefficient, and stress values of the part according to different lattice structures were investigated.

Optimizing mix proportions of blended cement composites through rheological and strength relations: A numerical modelling approach with curve fitting

The research presented is part of a larger project focusing on finding the optimum mix design for cement pastes using ternary blended binder containing cement, Calcium metasilicate (CMS), and colloidal nano-SiO2. Water absorption and sorptivity tests were also conducted to ascertain the porosity of the paste mixes. Compressive and flexural strength were used for the mechanical properties of different mixes, and rheology was conducted to find the flow characteristics of paste mixes. The numerical modeling employed involves regression analysis and curve fitting to find the significant factors affecting the properties of cement composites. The experimental data were subjected to analysis of variance (ANOVA) to determine the essential factors and their interactions. The results for strength parameters for high-volume replacement suggests that the optimum replacement levels for nano-SiO2 were 3% in both w/b ratios. The rheological parameter states that microfibres of CMS and nano-SiO2, increased the viscosity and decreasing the yield stress. The FESEM micrographs show the sequestration of CO2 and calcite formation due to high-volume CMS.

Multi-objective design optimization of a transonic axial fan stage using sparse active subspaces

In this paper, a multi-objective optimization strategy for efficient design of turbomachinery blades using sparse active subspaces is implemented for a turbofan stage design. The proposed strategy utilized sparse polynomial chaos expansion on a limited dataset to generate a function from which the differential and the covariance matrix can be obtained. Active subspace was used to compute the active variables via singular value decomposition and a hybrid polynomial correlated function expansion was used to construct a surrogate model on the active subspace. Coupled with freeform method and multi-objective genetic algorithm, an automated optimization loop was run at a single operating condition. An improvement in stage efficiency and total pressure ratio of 2.97% and 1.15% was achieved for the optimum design compared with the baseline. Additionally, total pressure loss coefficient decreased by 5.88%, exit flow angle by 34.65% and shock strength by 5.32%. The coupled effect of change in stagger angle, forward sweep, forward lean, and chord length reduced the recirculation at the hub, and blockage at the shroud due the tip leakage flow by decreasing the blade loading. The threshold value hyperparameter was found to be the most influential and must be accurately determined.

Research on the high temperature calibration of the electromagnetic damping generator applied to the rotary steerable system

The Electromagnetic Damping Generator (EDG) used for petroleum exploration is faced with the problem of high local temperature during its downhole work. To avoid the damage to its internal circuit caused by high temperature, the key EDG circuit is required to be heat-resistant based on the electromagnetic induction principle and the current heating effect. First of all, the working condition with the maximum heating power was figured out after studying the heating power characteristics of EDG. Then the multi-field coupling model of the temperature field, electromagnetic field and structure of the damper, as well as its iterative analytic model were established to identify the relationship between the temperature rise of the key location of the damper part and its working time in the downhole environment and the room-temperature experimental environment, which provided data support for EDG optimization design. Finally, a EDG prototype was developed, and the test bench was set up by replacing the load with resistors to verify the accuracy of the iterative analytical model. The results showed that the temperature value calculated by the model well fit the experimental value. Therefore, the downhole operation reliability of the EDG and the safety of corresponding bench tests are guaranteed, which has certain guiding significance for EDG optimization design and its experimental study.

Parametric shape optimization of pin fin arrays using a multi-fidelity surrogate model based Bayesian method

The current work aims to develop a multi-fidelity Bayesian optimization methodology to reduce computation time in CFD based design optimization of an internal cooling channel of a gas turbine blade. To build energy efficient gas turbine engines, it is important to increase the turbine inlet temperature, while preventing material failure of hot gas path components. Power lost due to pumping compressed air in turbine blade internal cooling channels can affect the overall efficiency of a power cycle. While designing such a cooling channel, it is important to ensure high heat transfer within a given pressure drop budget. Design optimization using CFD typically needs a sufficient number of computationally expensive high fidelity simulations to search for an optimum design. In the current work, a multi-fidelity Gaussian process surrogate based Bayesian optimization framework has been used to design a pin fin array channel, typically used for gas turbine blade trailing edge cooling, with the goal of maximizing Nusselt number at a constrained pressure loss. Data from a cheaper, low fidelity computation model has been leveraged, along with an expensive high fidelity one, in order to search for a design configuration that maximizes heat transfer at a given pressure drop constraint. The multi-fidelity approach improves upon single fidelity optimization results, at a low additional computational cost due to usage of simulation data from the cheap lowfidelity CFD model. A multifidelity approach outperformed the previous single fidelity case, with 6% higher objective while satisfying the pressure constraint, with 2% lower computational expense and 8 fewer high fidelity data point computations.

A Multifactor Combination Optimization Design Based on Orthogonality for a Two-Degree-of-Freedom Floating Machine Gun Vibration System

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%.

Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design

We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production.

Enhancing Steel Wheel Ventilation Efficiency Through Multi-Objective Optimization

AbstractThis study focuses on the optimization of ventilation hole design in steel wheels used for heavy commercial vehicles. The primary objective is to reduce the weight of the wheel while ensuring compliance with radial fatigue and cornering fatigue test requirements. Four distinct ventilation types were parametrized using ANSYS Mechanical, with the von Mises stress on the disk, number of ventilations, and wheel weight serving as design parameters. Stress analysis and weight comparisons were performed between wheels featuring different ventilation types and an ellipse ventilation wheel. Incorporating the design of experiment (DoE) and response surface optimization (RSO) module in ANSYS Workbench 2022 R1 was employed to compare and evaluate the obtained values. Subsequently, the multi-objective genetic algorithm (MOGA-II) method was employed for optimization, aiming to identify the optimal design. The optimization process, utilizing a maximum of 20 iterations, a convergence stability percentage of 2%, and a maximum allowable Pareto percentage of 70%, yielded 1, 3, 3, and 3 candidate design points for round, slot, trapezoid, and halfmoon-type ventilation holes, respectively. Among the various ventilation types considered, the halfmoon-type ventilation hole exhibited the most promising results. Compared to the current design, the optimized wheel achieved a weight reduction of 0.9 kg (2.05%). This outcome demonstrates the effectiveness of the proposed methodology. Although lighter designs were not attainable while maintaining the same stress values for the other three ventilation types, the halfmoon-type ventilation hole was ultimately selected as the preferred design.

Meta-Model Based Blade Optimization Design Considering the Fluid Characteristics of Vehicle Energy Harvesting

Meta-Model Based Blade Optimization Design Considering the Fluid Characteristics of Vehicle Energy Harvesting

Optimized Design of a H2-Powered Moped for Urban Mobility

Micro-mobility plays an increasingly important role in the current energy transition thanks to its low energy consumption and reduced contribution to urban congestion. In this scenario, fuel cell hybrid electric vehicles have several advantages over state-of-the-art battery electric vehicles, such as increased driving ranges and reduced recharge times. In this paper, we study the conversion of a commercial electric moped (Askoll eS3®) into a fuel cell hybrid electric vehicle by finding the optimal design of the components through an optimization methodology based on backward dynamic programming. This optimal design and operation strategy can also be implemented with a rules-based approach. The results show that a system composed of a 1 kW proton exchange membrane fuel cell, a 2000 Sl metal hydride hydrogen tank, and a 240 Wh buffer battery can cover the same driving range as the batteries in an electric moped (119 km). Such a hybrid system occupies considerably less volume (almost 40 L) and has a negligibly higher mass. The free volume can be used to extend the driving range up to almost three times the nominal value. Moreover, by using a high-pressure composite tank, it is possible to increase the mass energy density of the onboard energy storage (although compression can require up to 10% of the hydrogen’s chemical energy). The fuel cell hybrid electric vehicle can be recharged with green hydrogen that is locally produced. In detail, we analyze a residential scenario and a shared mobility scenario in the small Italian city of Viterbo.

Structural analysis and optimal design of a spherical thin-walled stainless steel water tank without reinforced tie ribs

A spherical thin-walled stainless steel water tank without reinforced tie ribs has been designed to address the issues of easy fracture and corrosion of the ribs, difficulty in maintenance and cleaning, and short service life exposed during the use of thin-walled stainless steel water tanks with reinforced tie ribs. Firstly, an analytical model of a flat steel thin-walled water tank without reinforced tie ribs was established and subjected to static analysis under water pressure. The deformation and stress distribution patterns of the side molded plate of the flat box were obtained. Secondly, a spherical non ribbed thin-walled stainless steel water tank structure was designed with circular cross-section under different box bulge parameters, and its mechanical response characteristics under water pressure load were analyzed. A strengthening scheme was designed for the bottom box molded plate. Once again, optimize the combination design of the box scheme and the reinforcement scheme, and analyze their static, thermodynamic, and thermal solid coupling performance. Finally, the Latin Hypercube Sampling method was used to generate experimental design samples, and a response surface approximation model of a spherical thin-walled stainless steel water tank without reinforced tie ribs was constructed. The wall thickness of the box molded plate, skeleton, and reinforcement were used as design variables, and the maximum deformation and maximum equivalent stress were used as constraints. The lightweight design was carried out with the goal of minimizing mass. The research results indicate that the design program and parameter selection method for spherical thin-walled stainless steel water tanks without reinforced tie ribs proposed in the article are efficient and feasible, and can provide technical reference and theoretical support for the layout and overall optimization design of non ribbed thin-walled stainless steel water tank structures.

Static Robust Design Optimization Using the Stochastic Frontier Method: A Case Study of Pulsed EPD Process on TiO2 Films

This paper aims to optimize a pulsed electrophoretic deposition (EPD) process for TiO2 films. This is accomplished by determining the optimal configuration of the coating parameters from a robust optimization perspective. The experimental study uses a composite central design (CCD) with four control factors, i.e., the initial concentration (x1 in g/L), the deposition time (x2 in s), the duty cycle (x3 in %), and the voltage (x4 in V). The process responses that should all be maximized are the photocatalytic efficiency of the thin film (De) and three critical charges, which characterize the adhesion failure, i.e., LC1: the load at which the first cracks occurred; LC2: the load at which the film starts to delaminate at the edge level of the scratch track; and LC3: the load when the damage of the film exceeds 50%. This paper compares the robust optimization design of the EPD process using two methods: the robust design of processes and products using the stochastic frontier (RDPP-SF) and the surface response and desirability function methods. The findings show that the RDPP-SF method is superior to the response surface–desirability method for the process responses De and LC2 because of non-natural sources of variation; however, both methods perform comparably well while analyzing the LC1 and LC3 responses, which are subjected to pure random variability. The parameters setting for the process robust optimization are met in run 25 (x1 = 14 g/L, x2 = 150 s, x3 = 50%, and x4 40 V).

Methods to Calculate Entropy Generation.

Entropy generation, formulated by combining the first and second laws of thermodynamics with an appropriate thermodynamic potential, emerges as the difference between a phenomenological entropy function and a reversible entropy function. The phenomenological entropy function is evaluated over an irreversible path through thermodynamic state space via real-time measurements of thermodynamic states. The reversible entropy function is calculated along an ideal reversible path through the same state space. Entropy generation models for various classes of systems-thermal, externally loaded, internally reactive, open and closed-are developed via selection of suitable thermodynamic potentials. Here we simplify thermodynamic principles to specify convenient and consistently accurate system governing equations and characterization models. The formulations introduce a new and universal Phenomenological Entropy Generation (PEG) theorem. The systems and methods presented-and demonstrated on frictional wear, grease degradation, battery charging and discharging, metal fatigue and pump flow-can be used for design, analysis, and support of diagnostic monitoring and optimization.

Development of an analytical solution for optimum design of RWH systems—A case study in Guangzhou

AbstractThe implementation of rainwater harvesting (RWH) has emerged as a key strategy to cope with the water crisis in urban areas. However, the required design parameters of the RWH system in different buildings vary widely because of the differences in architectural characteristics. In this paper, the relationships between the building characteristics parameters and the optimal tank (Tc) capacity are investigated by simulating the RWH system for more than 120 buildings at different locations in Guangzhou. Explicit expressions relating the optimal storage volume of RWH systems to building characteristics are derived based on nonlinear regression analysis. A set of bivariate exponential equations for estimating the optimal tank size under different building conditions was obtained. The model has a MER of less than 10% for buildings with a C/D ratio between 17.5 and 85. In Guangzhou, the unit cost of an RWH system can be reduced to CNY 2.62/m2 with optimally designed tanks.