Computational Methods and Design Optimization for Engineering Systems

This research develops advanced computational methods and optimization techniques to address the increasing complexity and performance demands in modern engineering design. The work presents novel algorithms that combine finite element analysis with adaptive mesh refinement, multi-physics coupling, and high-order numerical schemes, enhanced by parallel computing and GPU acceleration for improved computational efficiency. Key innovations include gradient-based optimization with adjoint sensitivity analysis, evolutionary algorithms for global optimization, multi-objective optimization with Pareto frontier exploration, and machine learning-enhanced optimization strategies. The technical approach integrates systematic design methodology with modular software architecture, enabling seamless integration with commercial CAD and analysis software while supporting automated optimization workflows and batch processing for large-scale problems.

The research demonstrates significant impact across diverse engineering applications, from automotive component optimization and aerospace structural design to heat exchanger optimization and advanced manufacturing processes. Industrial case studies show substantial improvements in design performance, with quantifiable benefits including reduced development costs, accelerated time-to-market, and enhanced competitive advantage through superior design capabilities. The work includes comprehensive validation through benchmark studies and real-world industrial implementations, demonstrating optimization convergence improvements, computational efficiency gains, and manufacturing feasibility enhancements. The team seeks collaboration opportunities with industry partners for technology transfer, software commercialization, and practical validation in actual design and development processes.

For complete technical details and experimental results, please refer to the original publication: 23-asme-jcise-wentai-zhang.pdf