A Computational Method for Formulation and Solution of Dynamical Equations for Complex Mechanisms and Multibody Systems

This paper presents a computational method for formulating and solving the dynamical equations of motion for complex mechanisms and multibody systems. The equations of motion are formulated in a preconditioned form using kinematic substructuring with a heuristic application of Generalized Coordinate Partitioning (GCP). This results in an optimal split of dependent and independent variables during run time. It also allows reliable handling of end-of-stroke conditions and bifurcations in mechanisms, thereby facilitating dynamic simulation of paradoxical linkages such as Bricard’s mechanism that has been known to cause problems with some multibody dynamic codes. The new Preconditioned Equations of Motion are then solved using a recursive formulation of the Schur Complement Method combined with Sparse Matrix Techniques. In this fashion the Preconditioned Equations of Motion are recursively uncoupled and solved one kinematic substructure at a time. The results are demonstrated using examples.

Sparse Matrix for ECG Identification with Two-Lead Features

Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

Performance Analysis of a Kitting Process as a Paired Queue

Nowadays, customers request more variation in a company’s product assortment leading to an increased amount of parts moving around on the shop floor. To cope with this tendency, a kitting process can be implemented. Kitting is the operation of collecting the necessary parts for a given end product in a specific container, called a kit, prior to arriving at an assembly unit. As kitting performance is critical to the overall cost and performance of the manufacturing system, this paper analyses a two-part kitting process as a Markovian model. In particular, kitting is studied as a paired queue, thereby accounting for stochastic part arrivals, and kit assembly times. Using sparse matrix techniques, we assess the impact of kitting interruptions, bursty part arrivals and phase-type distributed kit assembly times on the behaviour of the part buffers. Finally, a cost-profit analysis of kitting processes is conducted and an approximation for a two-part kitting process is established.

Seismic imaging of reservoir flow properties: Time‐lapse pressure changes

Time‐lapse fluid pressure and saturation estimates are sensitive to reservoir flow properties such as permeability. In fact, given time‐lapse estimates of pressure and saturation changes, one may define a linear partial differential equation for permeability variations within the reservoir. The resulting linear inverse problem can be solved quite efficiently using sparse matrix techniques. An application to a set of crosswell saturation and pressure estimates from a CO2 flood at the Lost Hills field in California demonstrates the utility of this approach. The pressure and saturation estimates are mapped into reservoir permeability variations between the boreholes. The resulting permeability estimates agree with a permeability log in an adjacent well and are in accordance with water and CO2 saturation changes imaged in the interwell region.