Cement mantle fatigue failure in total hip replacement: Experimental and computational testing

One possible loosening mechanism of the femoral component in total hip replacement is fatigue cracking of the cement mantle. A computational method capable of simulating this process may therefore be a useful tool in the preclinical evaluation of prospective implants. In this study, we investigated the ability of a computational method to predict fatigue cracking in experimental models of the implanted femur construct. Experimental specimens were fabricated such that cement mantle visualisation was possible throughout the test. Two different implant surface finishes were considered: grit blasted and polished. Loading was applied to represent level gait for two million cycles. Computational (finite element) models were generated to the same geometry as the experimental specimens, with residual stress and porosity simulated in the cement mantle. Cement fatigue and creep were modelled over a simulated two million cycles. For the polished stem surface finish, the predicted fracture locations in the finite element models closely matched those on the experimental specimens, and the recorded stem displacements were also comparable. For the grit blasted stem surface finish, no cement mantle fractures were predicted by the computational method, which was again in agreement with the experimental results. It was concluded that the computational method was capable of predicting cement mantle fracture and subsequent stem displacement for the structure considered. (C) 2006 Elsevier Ltd. All rights reserved.

Computational Verification of Two Universal Relations for Simple Ionic Liquids. Kinetic Properties of a Model 2:1 Molten Salt

Two semianalytical relations [Nature, 1996, 381, 137 and Phys. Rev. Lett. 2001, 87, 245901] predicting dynamical coefficients of simple liquids on the basis of structural properties have been tested by extensive molecular dynamics simulations for an idealized 2:1 model molten salt. In agreement with previous simulation studies, our results support the validity of the relation expressing the self-diffusion coefficient as a Function of the radial distribution functions for all thermodynamic conditions such that the system is in the ionic (ie., fully dissociated) liquid state. Deviations are apparent for high-density samples in the amorphous state and in the low-density, low-temperature range, when ions condense into AB(2) molecules. A similar relation predicting the ionic conductivity is only partially validated by our data. The simulation results, covering 210 distinct thermodynamic states, represent an extended database to tune and validate semianalytical theories of dynamical properties and provide a baseline for the interpretation of properties of more complex systems such as the room-temperature ionic liquids.

Structural similarity of directed universal hierarchical graphs: A low computational complexity approach

In the present paper we mainly introduce an efficient approach to measure the structural similarity of so called directed universal hierarchical graphs. We want to underline that directed universal hierarchical graphs can be obtained from generalized trees which are already introduced. In order to classify these graphs, we state our novel graph similarity method. As a main result we notice that our novel algorithm has low computational complexity. (c) 2007 Elsevier Inc. All rights reserved.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.