Constantin Suciu

Design and Implementation of a Service-Oriented Architecture for the Optimization of Industrial Applications

A novel architecture for the field of industrial automation is described, the goals of which are: 1) computation of optimal production plans; 2) automated usage of the optimized plans; 3) flexibility and reusability at development and maintenance; and 4) seamless transition from current practice to the approach introduced herein. The architecture consists of three main components: 1) a set of OPC unified architecture (UA) servers, which are used to model the information from the device level; 2) a set of services organized into two layers (basic and complex services), which act as a link between the first and the third layer; and 3) a constraint satisfaction problem (CSP) layer for the computation of production plans. Extensive performance tests motivate the choice of the service development framework, and prove the effectiveness of the special adapter software solution for the integration of current devices and the ability of the UA server to manage a high number of UA connections. As a proof-of-concept, the architecture has been tested for a real manufacturing problem composed of four flexible manufacturing systems. The results show that the architecture is able to efficiently control and monitor a real manufacturing process according to an optimized schedule with over 99% of the time spent on the manufacturing.

A patient-specific reduced-order model for coronary circulation

We introduce a patient-specific model for coronary circulation, by combining anatomical, hemodynamic and functional information from medical images and other clinical observations. The main components of the coupled model are: a lumped heart model, a reduced-order model for hemodynamics in the arterial vessel tree (both healthy and stenosed), and a physiological model for the microvascular bed. The anatomy of the vessel tree is extracted from Coronary Computed Tomography Angiography (CTA) images, followed by an estimation of the impedance of the distal microvascular network. For the blood flow simulations, three states are modeled: rest, drug-induced hyperemia and intense exercise. The results show an excellent agreement with the literature and provide a model for virtual assessment of the flow and underlying functional measures in healthy and stenosed coronary arteries.

A framework for personalization of coronary flow computations during rest and hyperemia

We introduce a Computational Fluid Dynamics (CFD) based method for performing patient-specific coronary hemodynamic computations under two conditions: at rest and during drug-induced hyperemia. The proposed method is based on a novel estimation procedure for determining the boundary conditions from non-invasively acquired patient data at rest. A multi-variable feedback control framework ensures that the computed mean arterial pressure and the flow distribution matches the estimated values for an individual patient during the rest state. The boundary conditions at hyperemia are derived from the respective rest-state values via a transfer function that models the vasodilation phenomenon. Simulations are performed on a coronary tree where a 65% diameter stenosis is introduced in the left anterior descending (LAD) artery, with the boundary conditions estimated using the proposed method. The results demonstrate that the estimation of the hyperemic resistances is crucial in order to obtain accurate values for pressure and flow rates. Results from an exhaustive sensitivity analysis have been presented for analyzing the variability of trans-stenotic pressure drop and Fractional Flow Reserve (FFR) values with respect to various measurements and assumptions.

A parameter estimation framework for patient-specific hemodynamic computations

We propose a fully automated parameter estimation framework for performing patient-specific hemodynamic computations in arterial models. To determine the personalized values of the windkessel models, which are used as part of the geometrical multiscale circulation model, a parameter estimation problem is formulated. Clinical measurements of pressure and/or flow-rate are imposed as constraints to formulate a nonlinear system of equations, whose fixed point solution is sought. A key feature of the proposed method is a warm-start to the optimization procedure, with better initial solution for the nonlinear system of equations, to reduce the number of iterations needed for the calibration of the geometrical multiscale models. To achieve these goals, the initial solution, computed with a lumped parameter model, is adapted before solving the parameter estimation problem for the geometrical multiscale circulation model: the resistance and the compliance of the circulation model are estimated and compensated.The proposed framework is evaluated on a patient-specific aortic model, a full body arterial model, and multiple idealized anatomical models representing different arterial segments. For each case it leads to the best performance in terms of number of iterations required for the computational model to be in close agreement with the clinical measurements.

GPU accelerated blood flow computation using the Lattice Boltzmann Method

We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method (LBM). The study focuses on the application of the LBM for patient-specific blood flow computations, and hence, to obtain higher accuracy, double precision computations are employed. The LBM specific operations are grouped into two kernels, whereas only one of them uses information from neighboring nodes. Since for blood flow computations regularly only 1/5 or less of the nodes represent fluid nodes, an indirect addressing scheme is used to reduce the memory requirements. Three GPU cards are evaluated with different 3D benchmark applications (Poisseuille flow, lid-driven cavity flow and flow in an elbow shaped domain) and the best performing card is used to compute blood flow in a patient-specific aorta geometry with coarctation. The speed-up over a multi-threaded CPU code is of 19.42x. The comparison with a basic GPU based LBM implementation demonstrates the importance of the optimization activities.

Optimized three-dimensional stencil computation on Fermi and Kepler GPUs

Stencil based algorithms are used intensively in scientific computations. Graphics Processing Units (GPU) based implementations of stencil computations speed-up the execution significantly compared to conventional CPU only systems. In this paper we focus on double precision stencil computations, which are required for meeting the high accuracy requirements, inherent for scientific computations. Starting from two baseline implementations (using two dimensional and three dimensional thread block structures respectively), we employ different optimization techniques which lead to seven kernel versions. Both Fermi and Kepler GPUs are used, to evaluate the impact of different optimization techniques for the two architectures. Overall, the GTX680 GPU card performs best for a kernel with 2D thread block structure and optimized register and shared memory usage. We show that, whereas shared memory is not essential for Fermi GPUs, it is a highly efficient optimization technique for Kepler GPUs (mainly due to the different L1 cache usage). Furthermore, we evaluate the performance of Kepler GPU cards designed for desktop PCs and notebook PCs. The results indicate that the ratio of execution time is roughly equal to the inverse of the ratio of power consumption.

Graphics processing unit accelerated one-dimensional blood flow computation in the human arterial tree

One-dimensional blood flow models have been used extensively for computing pressure and flow waveforms in the human arterial circulation. We propose an improved numerical implementation based on a graphics processing unit (GPU) for the acceleration of the execution time of one-dimensional model. A novel parallel hybrid CPU-GPU algorithm with compact copy operations (PHCGCC) and a parallel GPU only (PGO) algorithm are developed, which are compared against previously introduced PHCG versions, a single-threaded CPU only algorithm and a multi-threaded CPU only algorithm. Different second-order numerical schemes (Lax-Wendroff and Taylor series) are evaluated for the numerical solution of one-dimensional model, and the computational setups include physiologically motivated non-periodic (Windkessel) and periodic boundary conditions (BC) (structured tree) and elastic and viscoelastic wall laws. Both the PHCGCC and the PGO implementations improved the execution time significantly. The speed-up values over the single-threaded CPU only implementation range from 5.26 to 8.10 × , whereas the speed-up values over the multi-threaded CPU only implementation range from 1.84 to 4.02 × . The PHCGCC algorithm performs best for an elastic wall law with non-periodic BC and for viscoelastic wall laws, whereas the PGO algorithm performs best for an elastic wall law with periodic BC.

GPU accelerated simulation of elliptic partial differential equations

This paper assesses the performance improvements of a GPU based implementation of an elliptic equation (steady heat conduction) over the CPU based version. An iterative method based on a finite difference approach has been used for the numerical solution (red black point successive over relaxation). The main idea is to move the computationally intensive parts of the algorithm onto the GPU. Because of the lack of communication between the blocks of the GPU grid, the computations have been included in two separate kernels. An important improvement to the GPU implementation has been the padding of the memory buffers which has led to fewer global memory transactions and faster kernel execution times. The performances of the two versions of the algorithm (CPU and CPU-GPU) have been compared on three different grained grids. The results indicate a speed-up which varies from around two for the coarsest grid up to over one order of magnitude for the finest grid.

Modelling a flexible manufacturing system using reconfigurable finite capacity Petri nets

This paper addresses the issue of modelling concurrent systems whose structure is subject to changes by using an extension of the Petri nets formalism. Within this scope, we introduce the concept of reconfigurable finite capacity Petri nets and we apply it to a flexible manufacturing system model. Besides providing the designer a facile means of expressing the dynamic character of the flexible manufacturing system, the formalism also simplifies the model. In the second part of the paper we present and also compare the Petri net model and the equivalent reconfigurable finite capacity Petri net model for a such a system and we evaluate these models using PetriNetExec, a software library supporting the integration of Petri nets into Java applications.

Design and implementation of a fully automated planner-scheduler constraint satisfaction problem

The idea of constraint programming is to solve problems by stating constraints (conditions, properties) which must be satisfied by the solution. This paper introduces a fully automated scenario for complex scheduling problems. There are two constraint satisfaction problems: the planner (determines which orders should be accepted) and the scheduler (determines the timetable for the products). The third main component of the architecture is an OPC UA server which uses the solutions of the scheduler in order to control the devices of the machine tools, on which the parts are manufactured. An important step has been the reduction of the solving time corresponding to the second CSP (the scheduler). Two important actions have been taken. First the model has been split into four distinct CSPs, one for each manufacturing stage. Thus locally optimum solutions are combined into a global solution which is comparable to the global optimum solution. Secondly, we have tested various search strategies and we have managed to reduce the solving time to less than half of the initial time.