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This paper aims to develop a new method of fuselage drag optimization that can obtain results faster than the conventional methods based on full computational fluid dynamics (CFD) calculations and can be used to improve the efficiency of preliminary design.

An efficient method for helicopter fuselage shape optimization based on surrogate-based optimization is presented. Two numerical simulation methods are applied in different stages of optimization according to their relative advantages. The fast panel method is used to calculate the sample data to save calculation time for a large number of sample points. The initial solution is obtained by combining the Kriging surrogate model and the multi-island genetic algorithm. Then, the accuracy of the solution is determined by using the infill criteria based on CFD corrections. A parametric model of the fuselage is established by several characteristic sections and guiding curves.

It is demonstrated that this method can greatly reduce the calculation time while ensuring a high accuracy in the XH-59A helicopter example. The drag coefficient of the optimized fuselage is reduced by 13.3%. Because of the use of different calculation methods for samples, this novel method reduces the total calculation time by almost fourfold compared with full CFD calculations.

To the best of the authors’ knowledge, this is the first study to provide a novel method of fuselage drag optimization by combining different numerical simulation methods. Some suggestions on fuselage shape optimization are given for the XH-59A example.

Computational efficiency is always the major concern in aircraft design. The purpose of this paper is to investigate an efficient aeroelasticity optimization design method. Analysis of composite wing elastic axis is presented in the current study and its application on aeroelasticity optimization design is discussed.

Elastic axis consists of stiffness centers. The stiffness centers of eight cross sections are analyzed and the wing elastic axis is obtained through least‐squares procedure. In the analysis of the cross section stiffness center, the wing model is approximated by assuming the wing cross section as a thin walled structure with a single cell closed section and assuming the composite material to be a 3D anisotropic material. In aeroelasticity optimization design, objective functions are taken to be the wing weight and elastic axis position. Design variables are the thickness and area of wing components.

After aeroelasticity optimization design, the wing weight decreases while the divergent velocity increases. Meanwhile, it can achieve an expected result but costs much less computational time than the conventional method.

The results can be used for aircraft design or as an initial value for the next detailed optimization design.

The computational time can be dramatically reduced through the aeroelasticity optimization design based on the elastic axis. It is suitable for engineering applications.

The purpose of this paper is to investigate the optimal geometry parameters in a dimple/protrusion-pin finned channel with high thermal performance.

The BSL turbulence model is used to calculate the flow structure and heat transfer in a dimple/protrusion-pin finned channel. The optimization algorithm is set as Non-dominated Sorting Genetic Algorithm II (NSGA-II). The high Nusselt number and low friction factor are chosen as the optimization objectives. The pin fin diameter, dimple/protrusion diameter, dimple/protrusion location and dimple/protrusion depth are applied as the optimization variables. An in-house code is used to generate the geometry model and mesh. The commercial software Isight is used to perform the optimization process.

The results show that the Nusselt number and friction factor are sensitive to the geometry parameters. In a pin finned channel with a dimple, the Nusselt number is high at the rear part of the dimple, while it is low at the upstream of the dimple. A high dissipative function is found near the pin fin. In the protrusion channel, the Nusselt number is high at the leading edge of the protrusion. In addition, the protrusion induces a high pressure drop compared to the dimpled channel.

The originality of this paper is to optimize the geometry parameters in a pin finned channel with dimple/protrusion. This is good application for the heat transfer enhancement at the trailing side for the gas turbine.

Determinative locating and riveting distortions are highly coupled at assembly locale. Recent methods only take every tested or assumed locating errors at the mating surface into the process planning for the assemblies in a simple form. However, the growth of part number makes it nearly infeasible to take every locating error at every mating surface into the dimensional precision calculation. This paper aims to provide a solid riveting process planning for the reduction of practical locating-related distortions.

Large-scale metrology firstly measures the determinative coordinates for the locating-deviated key points. Iterative finite element (FE) analyses then calculate the riveting-related key point distortions from every rivet upsetting directions (UDs) and assembly sequence. These key points on the actual assembly contour and relative FE nodes yield two virtual planes. Virtual plane manipulation adds the riveting distortions into the locating-deviated coordinates. Finally, optimal algorithm integrates the iterative FE analyses with virtual plane manipulation.

Case studies validate that the virtual plane manipulation coincides with the test well, and the proposed method has good compensation of practical locating distortion.

The optimized rivet UDs may be set in a chaotic distribution, which may complicate the abundant riveting operations and the assembly appearance. Therefore, the use of automatic riveting systems can overcome the operational complexity, and the industrial design of rivet UD distribution will improve the assembly appearance.

The optimized UDs and assembly sequence are for assembly workers or automatic riveting systems.

The proposed method is the first to reduce the determinative locating distortion by a novel and efficient solid riveting process planning in detail, and the solid riveting process designed is conservative and accurate for practice.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

An optimization method has been presented to simultaneously identify the bird’s constitutive model and its parameters automatically in the bird strike problems. The full contact‐impact coupling algorithm of finite element method (FEM) has been applied and the optimization objective is to minimize the square sum of percentage errors between testing results and FEM results. As an example, two bird’s material constitutive models, Plastic Kinematics model and hyper elastic Mooney‐Rivlin Rubber material model, have been used to testify the feasibility and reliability of the optimization method. The results show that the method presented in this paper can be used to study the bird strike problems.

In this paper, a turbine blade was optimized by multidisciplinary design optimization (MDO) method. This turbine blade optimization is based on the optimization frame software iSIGHT, in which four disciplines (aerodynamics, thermal dynamics, structural mechanics and structural dynamics) have been integrated. Two commercial discipline analysis soft wares, NUMECA and ANSYS, are coupled in the platform iSIGHT. The three dimensional (3‐D) model of a blade was firstly parameterized. And then a set of parameters are chosen to optimize the blade to obtain the better overall properties. The result shows that the overall performances of the turbine blade have been improved remarkably after it is optimized by using the MDO method.

Based on aerodynamic analysis, an optimization method for the profiles of turbine blade is studied in this paper. This method is capable of addressing multiple objectives and constrains without relying on user input. A quintic polynomial is used to build the three‐dimensional blade model and a three dimensional Navier‐Stokes solver was used to solve the flow field around the turbine blade. The objective functions are the turbine aerodynamic efficiency and total pressure ratio. The optimization is completed with the K‐S function technique and accelerated by approximation technique. Finally, the proposed method is applied to optimizing a true blade to validate its accuracy and efficiency. The obtained result shows that the approximation method is more efficient and accurate than the conventional method.

The purpose of this paper is to propose a numerical prediction method of buckling loads for shell structures under axial compression and thermal loads based on vibration correlation technique (VCT).

VCT is a non-destructive test method, and the numerical realization of its experimental process can become a promising buckling load prediction method, namely numerical VCT (NVCT). First, the derivation of the VCT formula for thin-walled structures under combined axial compression and thermal loads is presented. Then, on the basis of typical NVCT, an adaptive step-size NVCT (AS-NVCT) calculation scheme based on an adaptive increment control strategy is proposed. Finally, according to the independence of repeated frequency analysis, a concurrent computing framework of AS-NVCT is established to improve efficiency.

Four analytical examples and one optimization example for imperfect conical-cylindrical shells are carried out. The buckling prediction results for AS-NVCT agree well with the test results, and the efficiency is significantly higher than that of typical numerical buckling methods.

The derivation of the VCT formula for thin-walled shells provides a theoretical basis for NVCT. The adaptive incremental control strategy realizes the adaptive adjustment of the loading step size and the maximum applied load of NVCT with Python script, thus establishing AS-NVCT.

The purpose of this paper is to realize the lightweight of connecting rod and meet the requirements of low energy consumption and vibration. Based on the structural design of the original connecting rod, the finite element analysis was conducted to reduce the weight and increase the natural frequencies, so as to reduce materials consumption and improve the energy efficiency of internal combustion engine.

The finite element analysis, structural optimization design and topology optimization of the connecting rod are applied. Efficient hybrid method is deployed: static and modal analysis; and structure re-design of the connecting rod based on topology optimization.

After the optimization of the connecting rod, the weight is reduced from 1.7907 to 1.4875 kg, with a reduction of 16.93%. The maximum equivalent stress of the optimized connecting rod is 183.97 MPa and that of the original structure is 217.18 MPa, with the reduction of 15.62%. The first, second and third natural frequencies of the optimized connecting rod are increased by 8.89%, 8.85% and 11.09%, respectively. Through the finite element analysis and based on the lightweight, the maximum equivalent stress is reduced and the low-order natural frequency is increased.

This paper presents an optimization method on the connecting rod structure. Based on the statics and modal analysis of the connecting rod and combined with the topology optimization, the size of the connecting rod is improved, and the static and dynamic characteristics of the optimized connecting rod are improved.