Search Results (2)

The Bi-directional Evolutionary Structural Optimization (BESO) method, owing to its algorithmic simplicity and strong scalability, has emerged as one of the most prevalent topology optimization methodologies in current research and industrial applications. To overcome the limitations of existing commercial finite element software (e.g., ABAQUS), particularly regarding the closed architecture of topology optimization modules and low efficiency in 3D complex structure optimization, this study proposes an ABAQUS–MATLAB cooperative framework. This innovative approach implements direct read/write operations via Python scripts on CAE/ODB model databases, coupled with MATLAB-based master control programs for sensitivity analysis, mesh filtering, and design variable updating. Compared with conventional integration methods employing INP/FIL file interactions, the proposed framework reduces computational time through MATLAB’s advanced matrix operations while maintaining solution accuracy. Validation cases including 2D cantilever beams and 3D wheel hubs demonstrate the method’s precision and computational efficiency. Practical applications in lightweight design of a hydraulic transmission test bench adapter support achieved 31% volume reduction while satisfying strength and stiffness requirements, significantly lowering material costs. The developed cooperative framework provides an extensible solution for high-efficiency topology optimization of complex engineering structures, balancing algorithmic transparency with practical applicability. Full article

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO₂ conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO₂/CH₄ conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications. Full article