Integrated Design Optimization Research Articles

Surrogate-based integrated design optimization for aerodynamic/stealth performance enhancements

The shape optimization considering both aerodynamic and radar stealth performances has been a considerable attention area for next-generation aircraft, with challenges involving balancing design contradictions and improving optimization efficiency. This paper develops a surrogate-based integrated design optimization method coupling Computational Fluid Dynamics (CFD) and Computational Electromagnetics (CEM) for aerodynamic/stealth performance enhancements. The topology-based 3D hexahedral mesh generation and 2D triangular mesh generation with adaptive refinement provide efficient preprocessing for CFD/CEM solvers. The surrogate-based optimization algorithm with parallel infill-sampling strategy and adaptive design space expansion is employed to search the global optimum and improve optimization efficiency while filtering out numerical noise. The Weighted Sum method is employed to address design contradictions in different disciplines. Optimizations considering aerodynamic, stealth, and aerodynamic/stealth performance of a flying wing aircraft are conducted to verify the effectiveness of the developed method. The optimization results indicate that exclusive consideration of a singular discipline leads to a significant deterioration in the performance of the other discipline. Balanced performance is achieved through coupling optimization. The numerical noise in the stealth discipline is markedly higher than in the aerodynamics discipline. The results demonstrate that the developed method is capable of addressing aerodynamic/stealth design optimization problems and significantly reducing the number of CFD/CEM evaluations while maintaining global optimization effectiveness.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Integrated design optimization of ducted lift fan for lift enhancement and noise reduction based on nested NFFD control body

In order to enhance the aerodynamic performance and reduce the aerodynamic noise of the coaxial dual-rotor ducted fan, this paper proposes a stepwise optimization strategy based on nested NFFD (Nurbs-Based Free-Form Deformations) control body parameterization. This strategy takes into account the optimization of aerodynamic performance, which involves significant deformations, and noise reduction, which requires fine deformations. By doing so, it effectively addresses the issue of drastically increased computational complexity and time consumption associated with conventional multi-objective optimization methods. By designing the aerodynamic shape of the blades, along with aerodynamic performance parameter calculation and aerodynamic noise calculation modules, it is possible to achieve lift enhancement and noise reduction for the ducted fan. The numerical simulation results demonstrate that the flow field over the optimized blade surface is improved significantly. The lift has increased from 3.45N to 3.54N, resulting in a 2.6% enhancement. Additionally, the noise level has decreased from 72.33dB to 69.52dB, achieving a noise reduction of 2.81dB.

Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties

As brazing devices become more sophisticated and service environments become more demanding, Cu-Ag-Zn-Mn-Ni-Si-B-P brazing material are subjected to higher wettability and brazed joint strength requirements. In this paper, a machine learning strategy is proposed. Firstly, the composition-process-properties dataset is obtained based on high-throughput experimental techniques. Then, the composition features are constructed by elemental chemical and physical parameters instead of the direct input of alloy composition, and the proposed novel feature selection method is used to effectively identify the key composition feature combinations that affect brazing properties. Then, the support vector regression algorithm was used to establish a joint strength model with an error of less than 3.47 % and a spreading area model with an error of less than 8.37 % using the key alloy composition features and brazing process features as inputs, respectively. Finally, two multi-objective optimization strategies, Bayesian optimization and NSGA-II genetic algorithm, were used for the integrated optimization design of composition and brazing process. The experimental results show that the Cu-14Ag-4Zn-24Mn-13.5Ni-0.4Si-0.3B-0.3 P alloy and the matched brazing process (945 °C and 12 min) were optimally designed using NSGA-II genetic algorithm, corresponding to the measured joint strength and spread area of 356 MPa and 412 mm2, respectively, which outperforms the high-throughput experimentally optimized data as well as commercialized data. The machine learning strategy established in this work provides a new research idea for the comprehensive improvement of brazing properties of brazing alloys with small data sets.

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.

Integrated optimization design of multiphase pump based on adaptive sparse grid method

Multiphase pump play a critical role in natural gas hydrate extraction. However, their stable and efficient operation in complex multiphase flow conditions remains a significant challenge. This study focuses on a “gas-liquid-solid” multiphase mixing pump and uses numerical simulations to analyze its single-stage pressure boosting unit under different conditions. The results revealed a significant decrease in pressure increment and efficiency under high gas content and solid phase content conditions. To address this, the study built an integrated optimization design platform based on an adaptive sparse grid surrogate model. This platform optimized critical hydraulic parameters of the pump through an adaptive sparse grid method and MOGA algorithm. The results show that under water conditions, pressure increment and efficiency can be increased by 12.805 KPa and 1.79%, respectively. Under working conditions of 30% GVF and 25% SVF, the pressure increment can reach 7.889 KPa, and the efficiency can be increased by 2.75%. Verification experiments demonstrated the reliability of the numerical methods utilized. Overall, the integrated optimization design platform saves computational resources, improves optimization efficiency and provides a foundation for ongoing research on the multi-gas co-production of natural gas hydrates.

Research on integrated technology of integral aerial building formwork equipment and large tower crane

The integrated aerial building formwork equipment is an independently developed support apparatus by China, which is primarily and importance for the construction of concrete structures in ultra high buildings exceeding 200 m. The novelty of this paper focuses on optimizing and improving the load transmission system and its critical large bearing support structure connecting the tower crane through theoretical analysis and finite element numerical calculation. By obtaining the stress ratio and maximum deformation before and after integrated optimization design, a local strengthening scheme for the tower crane support tube is proposed to simplify the structural connection while significantly reducing self-weight and steel consumption without compromising mechanical performance requirements, thus improving the overall performance of the integrated formwork equipment for aerial building construction.

Symmetrical complex serpentine channel introduction of secondary openings with open Y fins integrated optimization design

Symmetrical complex serpentine channel introduction of secondary openings with open Y fins integrated optimization design

Integrated optimization design and motion control of multi-configuration unmanned metamorphic vehicle

An unmanned metamorphic vehicle is a new deformable robot that can transform its configuration according to different road conditions in order to realize wheeled driving or legged walking. This paper proposes a method of multi-objective integrated optimization design based on the motion control of unmanned metamorphic vehicle. The aim of this method is to improve the stability and reduce energy consumption of multi-configuration motion. The vehicle state configuration and the humanoid state walking performance were designed. The kinematic model of reconfiguration and humanoid walking was established from screw theory, while the Lagrangian dynamics model for reconfiguration and humanoid walking using analytical mechanics. Reconfiguration and gait planning were carried out for an unmanned metamorphic vehicle, and a sliding mode controller was designed for position feedback control of the zero moment point in order to improve the stability of reconfiguration and walking. In order to improve the kinematic performance of an unmanned metamorphic vehicle under multiconfiguration, the NSGA-II intelligent optimization algorithm was used to optimize the multi-objective integrated design of the structural parameters, posture parameters, and control parameters of the sliding mode controller of the system. The simulation and test results showed that the integrated design performs higher reconfiguration stability, lower energy consumption, and higher control quality than the sequential design and pre-optimization under the reconfiguration condition; in the vehicle state, the transient steering stability and the steady-state steering stability of the system during driving are significantly improved; in the humanoid state, the energy consumption of the striding process and the movement of the center of mass to the single-legged support region in the walking process is reduced and the walking stability is improved, thus is more conducive to walking.

Net zero carbon rural integrated energy system design optimization based on the energy demand in temporal and spatial dimensions

The energy demand of rural residential buildings has not received sufficient attention in previous research on rural integrated energy system (IES) design. The predominant "full-time and full-space" design principle results in the IES capacity not corresponding to the actual energy demand of rural buildings, and the impact of energy demand on IES design capacity has not been quantified. Therefore, energy demands for four scenarios ("full-time and full-space", "full-time and partial-space", "partial-time and full-space", and "partial-time and partial-space") were obtained through field investigations and simulation. Meanwhile, a rural IES capacity configuration optimization model was constructed with the economic and net zero carbon emissions constraints. The two-stage fast and elitist multi-objective genetic algorithm (Top-NSGAII) was employed to determine the optimal design for four scenarios. Results showed that the design based on "partial-time and partial-space" performs best in terms of economics, carbon emissions, and autonomy, reducing annual total costs by 6.96 %, net carbon emissions by 3492.30kgCO2, and external electricity ratio by 8.32percentage pionts, compared with the "full-time and full-space". The study quantifies the impact of energy demand on capacity design and provides a new path to realizing net zero carbon rural residential buildings.

Integrated Design of Axial Slot Casing Treatment and Blades in a Low-Speed Single Stage Axial Compressor

Abstract Casing treatment has been used to enhance the stability of axial-flow compressor by extending the stall margin; it also induces strong interaction with compressor blades as demonstrated in the open literature. Revealing the mechanism of this interaction is a key to fully dig out the potential of the stability-enhancing capability of casing treatment without efficiency penalty. For this purpose, an integrated optimization design of rotor and axial slot casing treatment is implemented for an axial-flow compressor, and the stall margin is increased by 10.89% without penalty on the peak efficiency. Through detailed analysis two important driving factors for the improvement of stall margin are illustrated: the suction effect that decreases the axial momentum of tip leakage flow, and the injection effect that increases the axial momentum of main flow. Meanwhile, it is found that the combination of forward-deflection axial slot casing treatment and forward-sweep rotor can maximize the enhancement of the momentum exchange, thereby adequately reducing the blockage region induced by tip leakage flow. More specially, the stator is then redesigned based on the matching mechanism between the stator and rotor with axial slot casing treatment in a stage. This redesign can considerably reduce entropy generation and flow separation in the passage and achieve an improvement of 0.87% in efficiency. The results hence demonstrate the superiority of integrated design of axial slot casing treatment and blades in improving both stall margin and efficiency.

Multi-objective optimization of building planning energy saving based on genetic algorithm

The construction industry itself has led to a large amount of energy consumption, and this paper is based on genetic algorithm to optimize the planning and design of rural buildings with energy saving and multi-objective. Firstly, the multi-objective optimization problem and Pareto concept are discussed, the NSGA-II algorithm is applied to solve the problem of building energy saving integrated optimization, and the algorithm implementation of building energy saving integrated optimization design based on NSGA-II algorithm is given. Use incremental costs and incremental benefits as objective functions and make relevant assumptions to keep the model realistic while simplifying calculations. Second, a series of constraint functions are set up to ensure the superiority of the results. By constructing model assumptions, objective functions and constraint functions, the energy-saving optimization model is formed and solved by genetic algorithm. From the optimization results, it can be seen that the energy consumption of all the design schemes in the Pareto solution set is between 25.0 KWh/m2∼31.7 KWh/m2. Therefore, in the selection of architectural planning and design schemes, we should avoid simply pursuing low energy consumption and high comfort, and also use comprehensive consideration of economic costs to choose the most cost-effective scheme.

Structural Optimization Design of Dual Robot Gripper Unloading Device Based on Intelligent Optimization Algorithms and Generative Design.

The main aim of this paper is to explore new approaches to structural design and to solve the problem of lightweight design of structures involving multivariable and multi-objectives. An integrated optimization design methodology is proposed by combining intelligent optimization algorithms with generative design. Firstly, the meta-model is established to explore the relationship between design variables, quality, strain energy, and inherent energy. Then, employing the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the optimal frameworks of the structure are sought within the entire design space. Immediately following, a structure is rebuilt based on the principle of cooperative equilibrium. Furthermore, the rebuilt structure is integrated into a generative design, enabling automatic iteration by controlling the initial parameter set. The quality and rigidity of the structure under different reconstructions are evaluated, resulting in solution generation for structural optimization. Finally, the optimal structure obtained is validated. Research outcomes indicate that the quality of structures generated through the comprehensive optimization method is reduced by 27%, and the inherent energy increases by 0.95 times. Moreover, the overall structural deformation is less than 0.003 mm, with a maximum stress of 3.2 MPa-significantly lower than the yield strength and meeting industrial usage standards. A qualitative study and analysis of the experimental results substantiate the superiority of the proposed methodology for optimized structural design.

Integrated design optimization method for novel vapour-compression-cycle-based environmental control systems

The aircraft Environmental Control System (ECS) is the primary consumer of non-propulsive power at cruise conditions, hence, its performance optimization is crucial for the reduction of specific fuel consumption. A novel integrated system design optimization method is presented: thermodynamic cycle, component sizing and working fluid are taken into account simultaneously. This method was applied to the ECS of large rotorcraft based on a Vapour Compression Cycle system electrically driven by a high-speed centrifugal compressor. Steady-state and lumped parameter system component models have been developed using the Modelica acausal modelling language. The optimization design framework consists of an in-house code, featuring a Python-Modelica interface. The study case refers to a critical operating condition: the helicopter is on the ground during a hot and humid day. The working fluid is R-134a. The multi-objective optimization targets the maximization of the system efficiency and the minimization of system weight. The results show that more efficient systems can be designed only with heavier components. The design feasibility of high-speed centrifugal compressors is demonstrated. The advantage of an integrated system design optimization framework for complex energy systems is proved, allowing for the analysis of the impact of both component design and working fluid on system performance.

Integrated optimization in operations control and systems design for carbon emission reduction in building electrification with distributed energy resources

Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m2/year.

Integration design optimization of self-thermal methanol steam reforming microreactor for hydrogen production

To enhance hydrogen production (HP) capability of self-thermal methanol steam reforming microreactor (MSRM), the integration design of MSRM is optimized from the aspects of the structure of combustion catalyst support (CCS), the number of CCS, the relative flow direction between HP reactant and combustion reactant, and the capability amplification mode of the microreactor. The results show that, when the microreactor uses the CCS with optimized structure in its front-end and integrates 2 CCSs with non-optimized structure in its back-end, meanwhile adopting the opposite flow direction between HP reactant and combustion reactant, as well as employing the number-amplified capability amplification method, it can obtain higher HP capability, whose CH3OH conversion, H2 quantity and CO selectivity are up to 100%, 2.519 mol/h and 4.43%, respectively, at the feed quantity of 48 mL/h methanol-water compound (fuel for HP) and 3.2 mL/min methanol (fuel for combustion).

Integrated design optimization framework for green lightweight concrete

The increasing global concern for the environment and advancements in computational tools and techniques are transforming the way building structures are designed and constructed. The current economic climate and growth of the Architecture, Engineering, and Construction (AEC) industry demand the creation of environmentally sustainable and efficient buildings, starting from the design stage. Despite the AEC industry being a thriving sector in many countries, it is vulnerable to environmental degradation due to the impact of building materials. This paper presents a decision support framework for the optimal structural design of green concrete buildings using recycled plastic aggregates and lightweight concrete. The framework considers uncertainties such as design safety, architectural requirements, material efficacy, cost-effectiveness, and environmental impact. The approach evaluates four concrete aggregate materials with varying characteristics: Natural Weight Aggregate, Lightweight Volcanic Aggregate, and Plastic-Based Green Lightweight Aggregates. The proposed approach is demonstrated through a prototype model, which shows cost savings of up to 5% per floor compared to the conventional concrete mix. The results provide quantitative evidence of the direct financial benefits of using green concrete in lieu of the optimization framework, which is expected to encourage construction professionals to use environmentally friendly concrete and take part in greening the construction industry.

Integrated Design of Filter and Controller Parameters for Low-Switching-Frequency Grid-Connected Inverter Based on Harmonic State-Space Model

In high-power grid-connected inverter for new energy power generation system, low switching frequency makes the control loop, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filter resonant peak and sideband harmonics generated by modulation coupled, so that the parameter design of filter and controller affects each other. This increases the difficulty of design of filter parameters and controller parameters. To solve this problem, an accurate current harmonic calculation method based on harmonic state-space model and considering switching frequency and control delay is proposed in this article firstly. Secondly, based on the above model, the parameter optimization process considering current harmonic distortion rate, filter cost and so on is constructed. The integrated optimization design of filter and controller parameters of low-switching-frequency <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> grid-connected inverter is completed through intelligent algorithm. Thus the filter cost is minimized on the premise that inverter performances meet the specific requirements. In addition, considering the non-ideal characteristic of grid, a scheme of integrated parameter design which can adapt to the large-scale change of grid strength is proposed. Finally, the effectiveness of the proposed method and scheme is verified by simulation and experiment.

Structure-Circuit Resistor Integrated Design Optimization of Piezoelectric Energy Harvester Considering Stress Constraints

A piezoelectric energy harvester (PEH) transduces mechanical energy into electrical energy, which can be utilized as an energy source for self-powered or low-power devices. Therefore, maximizing the power of a PEH is a crucial design objective. It is well known that structural designs are firstly conducted for controlling resonance characteristics, and then circuit designs are pursued through impedance matching for improving power. However, a PEH contains solid mechanics, electrostatics, and even a circuit-coupled multi-physics system. Therefore, this research aims to design a PEH considering a circuit-coupled multi-physics. As a design process, a conceptual design is developed by topology optimization, and a detailed design is developed sequentially by applying size optimization as a post-processing step to refine the conceptual design results for manufacturable design. In the two optimization processes, design optimizations of a structure coupled with circuit resistor are performed to maximize the power, where the electrical and mechanical interactions between PZT, substrate, and circuit resistor are simultaneously considered. Additionally, stress constraints are also added for structural safety to ensure operational life of PEH. As a result of the proposed design methodology, a manufacturable design of PEH having maximum power and operational life is obtained with power density of 6.61 μWg−2mm−3.

Data-driven modeling of high-speed centrifugal compressors for aircraft Environmental Control Systems

The Environmental Control System (ECS) is the main consumer of non-propulsive power onboard aircraft. The use of an electrically-driven Vapor Compression Cycle (VCC) system, in place of the conventional air cycle machine, can lead to a substantial increase of the coefficient of performance. This work documents the development of an integrated design optimization method for VCC-based aircraft ECS, where the sizing of the system is performed along with the conceptual design of the compact heat exchangers and the high-speed centrifugal compressor. A data-driven model of the compressor has been developed to reduce the complexity of the VCC system model and the computational cost of the associated optimization problem. The model is based on artificial neural networks and has been trained on a synthetic dataset of 165k centrifugal compressor designs, generated with an in-house tool. The case study selected to demonstrate the capabilities of the proposed methodology is the multi-objective design optimization of an electrically-driven VCC system for the ECS of a single-aisle, short-haul aircraft, flying at cruise conditions. The results show that the number of function evaluations needed to identify the Pareto front reduces by a factor of three when using the data-driven model, in place of a meanline method. At the same time, the robustness of the numerical solver is improved, leading to the identification of optimal solutions covering a wider design space. Finally, the proposed methodology enables the analysis of the trends established between the system performance metrics and the design of the individual components.