Domain Decomposition Based High Performance Parallel Computing\ud

The study deals with the parallelization of finite element based Navier-Stokes codes using domain decomposition and state-ofart sparse direct solvers. There has been significant improvement in the performance of sparse direct solvers. Parallel sparse direct solvers are not found to exhibit good scalability. Hence, the parallelization of sparse direct solvers is done using domain decomposition techniques. A highly efficient sparse direct solver PARDISO is used in this study. The scalability of both Newton and modified Newton algorithms are tested

Finite element based adaptive neuro‐fuzzy inference technique for parameter identification of multi‐layered transportation structures

During the service life of a pavement, it is often required to conduct Non-destructive tests (NDTs) to evaluate its structural condition and bearing capacity and to detect damage resulting from the repeated traffic and environmental loading. Among several currently used NDT methods, the Falling Weight Deflectometer (FWD) is the most commonly used pavement NDT method applied by many transportation agencies all over the world. Non-destructive testing of pavements using FWD is typically accompanied by the prediction of the Young’s modulus of each layer of the pavement structure through an inverse analysis of the acquired FWD deflection data. The predicted pavement layer modulus is both an indicator of the structural condition of the layer as well as a required input for conducting mechanistic-based pavement structural analysis and design. Numerous methodologies have been proposed for backcalculating the mechanical properties of pavement structures from NDT data. This paper discusses the development of an Adaptive-Network-based Fuzzy Inference System (ANFIS) combined with Finite Element Modeling (FEM) for the inverse analysis of the multi-layered flexible pavement structures subjected to dynamic loading. First published online: 27 Oct 201

On-line cascading event tracking and avoidance decision support tool

Cascading outages in power systems are costly events that power system operators and planners actively seek to avoid. Such events can quickly result in power outages for millions of customers. Although it is unreasonable to claim that blackouts can be completely prevented, we can nonetheless reduce the frequency and impact of such high consequence events. Power operators can take actions if they have the right information provided by tools for monitoring and managing the risk of cascading outages. Such tools are being developed in this research project by identifying contingencies that could initiate cascading outages and by determining operator actions to avoid the start of a cascade.;A key to cascading outage defense is the level of grid operator situational awareness. Severe disturbances and complex unfolding of post-disturbance phenomena, including interdependent events, demand critical actions to be taken on the part of the operators, thus making operators dependent on decision support tools and automatic controls. In other industries (e.g., airline, nuclear, process control), control operators employ computational capabilities that help them predict system response and identify corrective actions. Power system operators should have a similar capability with online simulation tools.;To create an online simulator to help operators identify the potential for and actions to avoid cascades, we developed a systematic way to identify power system initiating contingencies for operational use. The work extends the conventional contingency list by including a subset of high-order contingencies identified through topology processing. The contingencies are assessed via an online, mid-term simulator, designed to provide generalized, event-based, corrective control and decision support for operators with very high computational efficiency. Speed enhancement is obtained algorithmically by employing a multi-frontal linear solver within an implicit integration scheme. The contingency selection and simulation capabilities were illustrated on two systems: a test system with six generators and the IEEE RTS-96 with 33 generators. Comparisons with commercial grade simulators indicate the developed simulator is accurate and fast.</p

MASTER: A JAVA Based Work-Stealing Technique For Parallel Contingency Analysis

In this paper, we present MASTER, a Java based multithreaded work-stealing technique for parallel contingency analysis in power systems. MASTER analyzes contingencies using time domain simulation and scales contingency analysis task to multiple cores using multithreading in Java. To achieve load balancing, MASTER uses efficient implementation of work-stealing algorithm. We discuss several implementation issues and design time choices which are crucial for achieving efficient implementation. Experiments performed with contingencies of a 13029 bus power system shows that MASTER provides high computational gains and provides much better load-balancing than the conventional scheduling techniques

Non-linear Inverse Analysis of Transportation Structures Using Neuro-adaptive Networks with Hybrid Learning Algorithm

The load-bearing capacity of pavement structures is a fundamental structural performance metric of transportation infrastructure networks in the context of safe and efficient movement of people and goods from one place to another. Non-destructive test (NDT) methods are typically employed to routinely evaluate the structural condition of pavement structures, their lifespan and the appropriate maintenance activities to be carried out. This involves computing the Young’s modulus of each layer of the pavement structure through inverse analysis of acquired NDT data. Over the past two decades, soft computing techniques such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Fuzzy Logic Approach (FLA) have been applied in numerous civil engineering fields for pattern recognition, function approximation, etc. This paper proposes the use of an Adaptive-Network-based Fuzzy Inference System (ANFIS) combined with Finite Element Modeling (FEM) for inverse analysis of multi-layered flexible pavement structures subjected to dynamic loading. Using the proposed approach, it will be possible for pavement engineers to characterize the non-linear, stress-dependent modulus of the pavement layers based on the NDT data in real time, identify the pavement defects, and better determine the appropriate rehabilitation strategy.This is a manuscript of an article from ANNIE 2009, ANNs in Engineering, St. Louis, Missouri, November 2-4, pp. 99-106. Posted with permission.</p

Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming

Genetic Programming (GP) is a systematic, domain-independent evolutionary computation technique that stochastically evolves populations of computer programs to perform a user-defined task. Similar to Genetic Algorithms (GA) which evolves a population of individuals to better ones, GP iteratively transforms a population of computer programs into a new generation of programs by applying biologically inspired operations such as crossover, mutation, etc. In this paper, a population of Hot-Mix Asphalt (HMA) dynamic modulus stiffness prediction models is genetically evolved to better ones by applying the principles of genetic programming. The HMA dynamic modulus (|E*|), one of the stiffness measures, is the primary HMA material property input in the new Mechanistic Empirical Pavement Design Guide (MEPDG) developed under National Cooperative Highway Research Program (NCHRP) 1-37A (2004) for the American State Highway and Transportation Officials (AASHTO). It is shown that the evolved HMA model through GP is reasonably compact and contains both linear terms and low-order non-linear transformations of input variables for simplification.This is a manuscript of an article in ANNIE 2010, artificial Neural Networks in Engineering, St. Louis, Missouri, November 1-3, 2010. Posted with permission.</p