PARAMETRIC VARIATIONAL PRINCIPLE BASED ELASTIC-PLASTIC ANALYSIS OF COSSERAT CONTINUUM

A new algorithm is developed based on the parametric variational principle for elastic-plastic analysis of Cosserat continuum. The governing equations of the classic elastic-plastic problem are regularized by adding rotational degrees of freedom to the conventional translational degrees of freedom in conventional continuum mechanics. The parametric potential energy princi- ple of the Cosserat theory is developed, from which the finite element formulation of the Cosserat theory and the corresponding parametric quadratic programming model are constructed. Strain localization problems are computed and the mesh independent results are obtained.

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.

PMU Guided Structure Data-Layout Optimization

Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimization is not widely used. To overcome these drawbacks, a performance monitoring unit （PMU） sampling method was developed with much less overhead and better portability and usability. An algorithm was developed to correct incomplete and inaccurate PMU sampling. With the corrected PMU feedback, a structure data-layout optimizer achieved a 45.1% performance improvement compared to a design without data-layout optimization, which is 97.6% of the performance improvement achieved with instrumented feedback. Calculation of the PMU feedback increased the execution time by 12.3%, compared to the overhead for the instrumented feedback of 341.5%. Tests show that the PMU feedback is efficient and effective for structure data-layout optimization.