A local lattice Boltzmann method for multiple immiscible fluids and dense suspensions of drops

The lattice Boltzmann method (LBM) for computational fluid dynamics benefits from a simple, explicit, completely local computational algorithm making it highly efficient. We extend LBM to recover hydrodynamics of multi-component immiscible fluids, whilst retaining a completely local, explicit and simple algorithm. Hence, no computationally expensive lattice gradients, interaction potentials or curvatures, that use information from neighbouring lattice sites, need be calculated, which makes the method highly scalable and suitable for high performance parallel computing. The method is analytic and is shown to recover correct continuum hydrodynamic equations of motion and interfacial boundary conditions. This LBM may be further extended to situations containing a high number (O(100)) of individually immiscible drops. We make comparisons of the emergent non-Newtonian behaviour with a power-law fluid model. We anticipate our method will have a range applications in engineering, industrial and biological sciences

Amorphous slicing of extended finite state machines

Slicing is useful for many Software Engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing Extended Finite State Machines (EFSMs). This paper introduces a set of dependency based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence based slicing. We use our tool to conduct experiments on ten EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence based program slicing for EFSMs. Compared to the only previously published dependence based algorithm, our average slice is smaller 40% of the time and larger only 10% of the time, with an average slice size of 35% for termination insensitive slicing

Applications of Broyden-based input space mapping to modeling and design optimization in high-tech companies in Mexico

One of the most powerful and computationally efficient optimization approaches in RF and microwave engineering is the space mapping (SM) approach to design. SM optimization methods belong to the general class of surrogate-based optimization algorithms. They are specialized on the efficient optimization of computationally expensive models. This paper reviews the Broyden-based input SM algorithm, better known as aggressive space mapping (ASM), which is perhaps the SM variation with more industrial applications. The two main characteristics that explain its popularity in industry and academia are emphasized in this paper: simplicity and efficiency. The fundamentals behind the Broyden-based input SM algorithm are described, highlighting key steps for its successful implementation, as well as situations where it may fail. Recent applications of the Broyden-based input space mapping algorithm in high-tech industries located in Mexico are briefly described, including application areas such as signal integrity and high-speed interconnect design, as well as post-silicon validation of high-performance computer platforms, among others. Emerging new applications in multi-physics interconnect design and power-integrity design optimization are also mentioned.ITESO, A.C

Uncertainty And Evolutionary Optimization: A Novel Approach

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Mobility Management in Industrial Iot Environments

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the Art Gallery problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a flexible Scheduling Function that can be easily adapted to meet the requirements of various IIoT applications. We analyze the considered Scheduling Function in different scenarios with varying traffic patterns and define an algorithm for sizing the system in such a way to guarantee the application requirements. Finally, we investigate the impact of mobility on the performance of the system. Our results show that the proposed solutions allow to manage node mobility very effectively, and without significant impact on the performance