I. The increasingly fierce market competition has made industrial product designers and manufacturers more and more aware that they can capture more markets by launching superior new products faster than others. To this end, the CAE method, as a powerful tool to shorten the product development cycle, has been introduced more and more frequently in all aspects of product design and production to enhance the competitiveness of the product. From the simple check of the performance of the designed product, to the accurate prediction of the product performance, and to the accurate simulation of the product work process, people are full of trust in the CAE method. However, improving product competitiveness requires not only improving the performance and quality of the product, but also reducing the cost of the product, so people need to find the most reasonable and most economical design. Although analysts can tirelessly modify design parameters in advance of the screen to find the optimal solution, the pressure to shorten the development cycle usually requires time and second, and people may not have more time to manually adjust the data parameters. The introduction of CAE methods by optimization techniques frees people from the tedious work of getting tried, and CAE has reached a new level.
Second, the optimization method and CAE Under the premise of ensuring that the product meets certain performance objectives and meet certain constraints, by changing some design variables that allow change, the product's index or performance can reach the most desired goal, which is the optimization method. For example, under the premise of ensuring that the structural strength just meets the requirements, the weight of the structure is lightest by changing certain design variables, which not only saves structural consumables, but also provides convenience in transportation and installation, and reduces transportation costs. Another example is to change the installation position of each heating component of the electrical equipment, so that the internal temperature peak of the equipment cabinet is minimized, which is an optimized example of a typical natural convection heat dissipation problem. In the actual design and production, examples like this are numerous. As a mathematical method, optimization usually uses the method of finding the extremum of the analytic function to achieve the purpose of seeking the optimal value. Based on the CAE method of numerical analysis technology, it is obviously impossible to obtain an analytic function for our target. The result obtained by CAE calculation is only a numerical value. However, the spline interpolation technique makes the optimization in the CAE possible. Multiple numerical points can use the interpolation technique to form a continuous curve or surface expressed by the available function, so that it returns to the mathematical extreme value optimization technique. The spline interpolation method is of course an approximation method. It is usually impossible to obtain an accurate surface of the objective function, but the result of the last calculation is interpolated again to obtain a new surface. The distance between the two adjacent surfaces will be closer and closer. When their distance is small enough, it can be considered that the surface at this time can represent the target surface. Then, the minimum value of the surface can be considered as the target optimal value.
The above is the optimization process in the CAE method. A typical CAE optimization process usually involves the following steps:
Parametric modeling: Using the parametric modeling function of CAE software, the data (design variables) to be involved in optimization is defined as model parameters, which provides the possibility for future software modification models. Solving: loading and solving the parametric model of the structure: extracting the state variables (constraints) and the objective function (optimization goals) for the optimization processor to evaluate the parameters. Optimization parameter evaluation: The optimization processor determines whether the loop objective function reaches the minimum, or the structure, based on the optimization parameters (design variables, state variables, and objective functions) provided by this loop and the optimization parameters provided in the previous loop. Whether the optimal is achieved, if optimal, complete the iteration, exit the optimization loop, otherwise, proceed to the next step. The design variables are corrected based on the completed optimization loop and the state of the current optimization variable, and the loop is re-introduced.
Third, the characteristics of the optimization technology in the CAE method From the above process we may have seen some of the basic features of the CAE optimization process, such as the parameterization of the calculation model, the automation of the iterative process. However, as a product of the perfect combination of optimization technology and CAE method, CAE optimization method must have more abundant features.
First of all, the development of modern CAE technology has extended the field of analysis to every corner of all walks of life. The depth and comprehensiveness of the research problems are gradually increasing. The researchers' eyes have shifted from single field analysis to multi-field coupling. Analyze to pursue more realistic simulation results. The adaptation scope of CAE software optimization technology will inevitably expand accordingly. It not only requires it to solve various single-field problems, but also can handle the optimization of multi-field coupling process. In the design process of automobiles, submarines, airplanes, etc., it is often considered to optimize the shape to make it more conducive to reducing fluid resistance at high speeds, and at the same time, it is necessary to consider whether the shape change is detrimental to other mechanical and thermal properties of the equipment. It can be seen that pure fluid dynamics optimization can only solve one problem, and only by coupling the mechanical or thermal problems of its internal equipment can the problem be solved completely.
Second, an optimization iterative process usually begins with pre-processing, modeling, meshing, loading, solving, and post-processing, while optimization problems usually require more iterations to converge.
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