aitopt -买球平台网址

aitopt — artificial intelligence topology optimization


aitopt is simulation software that is independently developed by nanjing tianfu software co., ltd for intelligent topology optimization. aitopt implements multi-objective optimization for industrial products by using the adjoint method, computational fluid dynamics (cfd) solver, and topology optimization technologies. multi-objective optimization includes quick reduction of flow pressure loss for pipes, optimization of flow evenness at the egress, and performance improvement for heat sinks and heat exchangers. aitopt supports importing mesh files in various formats and can automatically mesh imported geometries and find solutions. the final optimization and design results can be converted into computer-aided design (cad) files for further refinement. aitopt can significantly shorten the product design period, reduce the workload of design engineers, and generate satisfactory optimization and design results.


  • topology optimization using the variable density method the variable density method is widely applicable to multi-objective design optimization due to its natural adaptation to the design domains of complex geometries. users can use this method to quickly reduce the flow pressure loss of pipes, optimize flow evenness at the egress, and improve the performance of heat sinks and heat exchangers.

  • topology optimization using the level-set method: aitopt determines the distribution of structural materials based on the boundaries or isopleths of implicit functions in the next higher dimension. this method creates clear sections on the boundaries of structural materials to generate clear topology boundaries, thereby eliminating the grey areas that are prone to appear in topology optimization results. the generated design results can be directly used for manufacturing.

  • shape optimization and sensitivity analysis: aitopt uses a gradient-based solver to analyze, calculate, and design the sensitivity. users can use the sensitivity results of design variables to quickly optimize and design shapes and achieve optimization objectives, such as reducing the drag coefficient of transportation equipment and improving the performance of rotation machines.

  • sensitivity analysis based on the adjoint method: aitopt uses the adjoint method to obtain the accurate sensitivity of the target function. by performing only one additional round of cfd computing, users can obtain the sensitivity of the target function with any number of design variables. compared with the finite-difference method, the adjoint method can save more computing time.

  • gradient-based optimization: aitopt adopts leading topology optimization algorithms and supports topology optimization with millions of design variables, optimization with or without constraints, and multi-objective optimization.


1. optimization of pipe flows

optimization objective: minimize the pressure loss

constraint: the volume of fluid (vof) fraction


2. optimization of heat sinks

optimization objectives: minimize the average temperature in the solid domain and minimize the pressure loss


3. optimization of heat sinks in electronic components 

optimization objective: minimize the average temperature in the whole area


4. shape optimization

optimization objective: minimize drag