In the present study, a bio-inspired computational intelligence technique is developed for finding the solutions of the celebrated Falkner-Skan equation arising in fluid mechanics problems using feed-forward Artificial Neural Networks (ANNs), Genetic Algorithms (GA), the Active-Set (AS) method and their combination namely a GA-AS approach.
We describe the development of a highly interactive approach to simulation of engineering multi-mechanics problems, using the smoothed particle hydrodynamics mesh-free method as the computational engine, for applications including ship survival, medical devices and Pelton turbines.
The goals the Injury and Orthopaedic Biomechanics group are to investigate clinically relevant biomechanical research problems using the principles of engineering mechanics.
Multi-scale mechanics problems require the use of an appropriate upscaling approach to link micro-scale architecture (e.g., grain structure, defect population, crystal orientation) to macro-scale effective response (e.g., stress, elastoplastic properties, damage).
Another important contribution was made by Erwin Schrödinger, who looked at the problem using wave mechanics.
Furthermore, we showed that high- and low-spatial visualizers generate qualitatively different mental images and use different strategies when solving mechanics problems.
A superconvergent alpha finite element method (SαFEM) is then formulated by using the constructed strain field and the Galerkin-like weakform for solid mechanics problems.
Applications to fluid mechanics problems are discussed.
What does it require to be a successful mechanic, to do all the mechanics problems?
Their function, fabrication, and reliability pose urgent mechanics problems.
