Optimization research on arc height of running surface based on reducing partial wear of straddle-type monorail vehicle running wheel tire

This article proposes an ideal of reducing the partial wear of the running wheels by optimizing the arc height of the running surface to improve the wheel-rail contact state. To realize this idea, two kinds of concave and convex running surfaces were designed, the “running wheel-rail beam” finite element model of three kinds of rail surfaces of concave, convex, and plane were established. Taking the arc height of the running surface as the design variable, the total friction work and the friction work deviation (FWD) value as the dual optimization goal, an optimization model of arc height of running surface was established based on finite element model and multidisciplinary optimization platform Modefrontier. An improved genetic algorithm was used and an co-simulation optimization mode was put forward in the optimization. The optimization results show that when the concave height of the inner running surface is 22.62 mm, the total friction work and the FWD values are reduced by 11% and 11.8% respectively; When the convex height of the outer running surface is 11.81 mm, the objection values are reduced by 4.9% and 32.1% respectively. An ideal running surface was obtained and the life of the running wheel was extended by the research.

Metamodel-based multidisciplinary design optimization methods for aerospace system

The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

Hydrodynamic Characterization of Bodies of Revolution through Statistical-Empirical Prediction Modeling Using Machine Learning

Machine learning algorithms, namely artificial neural network modeling, were used to create prediction models for force and moment coefficients of axisymmetric bodies of revolution. These prediction models had highly nonlinear functional relationships to both geometric parameters and inflow conditions, totaling five input factors. A uniform experimental design was created consisting of 50 design points in these five factors and dictated which test points to simulate. Data was generated using computational fluid dynamic simulations, which were performed on all geometries using NavyFOAM at the experimental conditions prescribed by the designed experiment. The prediction models were validated by comparing behavioral trends in responses to previous research conducted by the author on a similar geometry. A test data sets was also created and used to ensure that the prediction models were not overfit to the training data and that they could accurately predict arbitrary geometries and inflow conditions within the experimental design region. Once the prediction models were validated, they were used to study the effects of varying the geometric parameters, inherent to the experiment, on each of the force and moment coefficients. Introduction Multidisciplinary optimization (MDO) schemes used in the early concept design phases for aero/hydrodynamic vehicles often use simplified planar maneuvering characteristics based on empirical or analytical relations in order to limit the computational cost of maneuverability prediction. This method leaves a more detailed analysis of the maneuvering behavior of a design to later in the process, where improvement or correction of an adverse behavior may be difficult to implement. The analysis of out-of-plane conditions or combined pitch-yaw conditions especially, are usually relegated to the detail analysis phase as empirical/ analytical descriptions of these conditions are lacking in the literature. It is therefore desired to develop a method to move these more detailed maneuvering analyses forward in the design phase.

Optimization of Low Noise Blade of Small Axial Fan at Low Reynolds Number

A multidisciplinary optimization design to simultaneously enhance the aeroacoustic and aerodynamic performance of an cooling fan is performed. The flow analysis of the cooling fan is conducted by solving three dimensional steady-state RANS equations with shear-stress transport turbulence model. Based on the results of the steady flow, aeroacoustic analysis is performed by using the Hanson and Brooks model. A multi-objective optimization is performed to simultaneously improve the efficiency and reduce the sound pressure level through an improved non-dominated sorting gentic algorithm. A Kriging surrogate model is used to approximate the function value while reducing computational cost. Series of optimum designs on the pareto front yielded increases in efficiency and decreases in the sound pressure level compared to the reference design. Through numerical analysis and experimental test, the aerodynamic efficiency is increased by 5% and the total sound pressure level is reduced by 4dB without loss of air volume for the selected optimized cooling fan. The thining of rotor boundary layer and inward load shift are the main factors to improve aerodynamic efficiency and reduce noise of the cooling fan.