New speedup algorithms for nonlinear dynamic time history analysis of supertall building structures under strong earthquakes

摘要

Some speedup algorithms oriented to improving computational efficiency of nonlinear dynamic time history analysis of supertall buildings structures excited by strong earthquakes are proposed. In order to minimize Jacobian factorizations, the inexact Newton algorithm combined with the sparse Cholesky matrix factorization (INC) is suggested with an unbalance-independent relation for the determination of a forcing term in the INC. Further, some shared memory parallel computing techniques are incorporated into the state transformation procedures developed previously (parallel state transformation procedure) and the matrix factorization (parallelized factorization) for full utilization of the resources of an ordinary personal computer. All the algorithms are integrated in a finite element program specialized in time history analysis of aseismic supertall building structures, with some features from OpenSees. Computational efficiency, as well as accuracy, of the proposed speedup algorithms is demonstrated on one 12-story reinforced concrete frame (K1) and 4 frame-core-tube supertall buildings (S1-S4). The results from the demonstration indicate that, as the number of degrees of freedom increases, the proportion of time consumed in Jacobian factorization becomes relatively more dominant. The combination of the INC and the parallelized factorization can achieve better acceleration performance in this case. Factors associated with the forcing term of the INC have much influence on computational efficiency and should be selected based on building scale. Combination of all the algorithms in the biggest model (S4) yields an average of 28.72 of acceleration ratio even with peak ground acceleration equal to 3.2 g. In all cases considered, a desirable agreement in structural response is always reached between conventional time history analysis method and the method using the proposed speedup algorithms.