A Survey on Intelligent Iterative Methods for Solving Sparse Linear Algebraic Equations

Efficiently solving sparse linear algebraic equations is an important research topic of numerical simulation. Commonly used approaches include direct methods and iterative methods. Compared with the direct methods, the iterative methods have lower computational complexity and memory consumption, and are thus often used to solve large-scale sparse linear equations. However, there are numerous iterative methods, parameters and components needed to be carefully chosen, and an inappropriate combination may eventually lead to an inefficient solution process in practice. With the development of deep learning, intelligent iterative methods become popular in these years, which can intelligently make a sufficiently good combination, optimize the parameters and components in accordance with the properties of the input matrix. This survey then reviews these intelligent iterative methods. To be clearer, we shall divide our discussion into three aspects: a method aspect, a component aspect and a parameter aspect. Moreover, we summarize the existing work and propose potential research directions that may deserve a deep investigation

Solcore: A multi-scale, python-based library for modelling solar cells and semiconductor materials

Computational models can provide significant insight into the operation mechanisms and deficiencies of photovoltaic solar cells. Solcore is a modular set of computational tools, written in Python 3, for the design and simulation of photovoltaic solar cells. Calculations can be performed on ideal, thermodynamic limiting behaviour, through to fitting experimentally accessible parameters such as dark and light IV curves and luminescence. Uniquely, it combines a complete semiconductor solver capable of modelling the optical and electrical properties of a wide range of solar cells, from quantum well devices to multi-junction solar cells. The model is a multi-scale simulation accounting for nanoscale phenomena such as the quantum confinement effects of semiconductor nanostructures, to micron level propagation of light through to the overall performance of solar arrays, including the modelling of the spectral irradiance based on atmospheric conditions. In this article we summarize the capabilities in addition to providing the physical insight and mathematical formulation behind the software with the purpose of serving as both a research and teaching tool.Comment: 25 pages, 18 figures, Journal of Computational Electronics (2018

Automated tuning for the parameters of linear solvers

Robust iterative methods for solving systems of linear algebraic equations often suffer from the problem of optimizing the corresponding tuning parameters. To improve the performance for the problem of interest, the specific parameter tuning is required, which in practice can be a time-consuming and tedious task. The present paper deals with the problem of automating the optimization of the numerical method parameters to improve the performance of the mathematical physics simulations and simplify the modeling process. The paper proposes the hybrid evolution strategy applied to tune the parameters of the Krylov subspace and algebraic multigrid iterative methods when solving a sequence of linear systems with a constant matrix and varying right-hand side. The algorithm combines the evolution strategy with the pre-trained neural network, which filters the individuals in the new generation. The coupling of two optimization approaches allows to integrate the adaptivity properties of the evolution strategy with a priori knowledge realized by the neural network. The use of the neural network as a preliminary filter allows for significant weakening of the prediction accuracy requirements and reusing the pre-trained network with a wide range of linear systems. The algorithm efficiency evaluation is performed for a set of model linear systems, including the ones from the SuiteSparse Matrix Collection and the systems from the turbulent flow simulations. The obtained results show that the pre-trained neural network can be reused to optimize parameters for various linear systems, and a significant speedup in the calculations can be achieved at the cost of about 100 trial solves. The algorithm decreases the calculation time by more than 6 times for the black box matrices from the SuiteSparse Matrix Collection and by a factor of 1.5-1.8 for the turbulent flow simulations considered in the paper

The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. The dichotomous trade-off between scalability and efficiency (SE) on the one hand and maintainability and portability (MP) on the other hand is one of the core challenges. We report on the SE/MP trade-off in the context of an ongoing systematic literature review (SLR). After characterizing the issue of the SE/MP trade-off using two examples from our own research, we (1) review the 33 identified articles that assess the trade-off, (2) summarize the proposed solutions for the trade-off, and (3) discuss the findings for SSE and future work. Overall, we see evidence for the SE/MP trade-off and first solution approaches. However, a strong empirical foundation has yet to be established; general quantitative metrics and methods supporting software developers in addressing the trade-off have to be developed. We foresee considerable future work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SEHPCCSE 2016

Wind Flow Simulation over Fish Farm Feed Barge

Master's thesis in Mechanical engineeringThere are approximate over 1000 fish farms in Norway, where half are connected to the grid and the rest are driven by diesel generators. Fish farms use large air compressors to feed the fish, which creates high power consumption. To reduce the diesel consumption and C02 emissions, created by the compressors, there are companies that specialize in providing green energy solutions. Gwind is a Stavanger based energy company that provide off-grid energy for this exact purpose. A master study done by H. Syse showed that a hybrid system with wind turbines, PV, Li-Ion batteries and two diesel generators over a 20-year period would reduce the CO2 emissions and lower the diesel consumption. Investigation of local wind flow and power generation with a wind turbine linked to the fish farm feed barge, was performed using the open source computational fluid dynamics (CFD) software OpenFOAM. The wind turbine is a vertical axis wind turbine (VAWT), modeled by an actuator line model (ALM). The ALM has been implemented with the use of a library called turbinesFoam. A framework for wind flow simulations over fish farm feed barges has been developed. This framework includes a ALM of a VAWT, simulated with OpenFOAM’s pimpleFoam solver, and k-epsilon turbulence model. The inlet is enriched with atmospheric boundary layer. The framework has been used on two fish farm cases, Tallaksholmen and Nordheim. These are owned by Grieg Seafood Rogaland, and in collaboration with Gwind a wind measurement campaign was conducted, and cross-referenced with nearby wind stations to set an approximately real inflow condition. The framework was used to investigate the optimal height placement of the VAWT on Tallaksholmen, coupled with a cost benefit analysis. To show the flexibility of the framework the second fish farm case, Nordheim, was setup and ready to run within a few hours. In this case the performance was increased, as a result of investigating the local wind flow before activating the turbine. Based on the results of this study, it is recommended to install a VAWT on the Tallaksholmen fish farm feed barge. The operational performance should be compared against the simulations to further verify the computational approach

Hardware Awareness for the Selection of Optimal Iterative Linear Solvers

Solving sparse systems of linear equations is a commonly encountered computation in scientific and high-performance computing applications. Applications that depend on solving sparse linear systems as part of their workflow can spend a large percentage of their total runtime solving sparse systems. However, selecting the best iterative solver and preconditioner for solving a given sparse linear system, especially for novice users, is not a simple task. To address this problem, previous works have used machine learning techniques to find similarities between sparse matrices and the corresponding performance that solver-preconditioner pairs have on solving the resulting linear systems. This dissertation expands on existing work by introducing new techniques that incorporate hardware information into the prediction of ideal iterative linear solver and preconditioners for sparse linear systems. By accounting for hardware, it is possible to create more specially tailored solver-preconditioner recommendations for a novice user

A Multidisciplinary Computational Framework for Topology Optimisation of Offshore Helidecks

Maintaining offshore steel structures is challenging and not environmentally friendly due to the frequent visits for inspection and repairs. Some offshore lighthouses are equipped with carbon steel helidecks fixed onto their lantern galleries in the 1970s to provide easy and safe access to maintenance staff and inspectors. Even though the helidecks supporting structures have maintained their integrity and are still functional in the offshore harsh environmental conditions, their inspection and maintenance remains a challenge due to the need of frequent visits which requires flying to the location of the lighthouse to bring the maintenance staff and equipment. We have developed a multidisciplinary computational framework to design new generation of aluminium helidecks for offshore lighthouses. We calculated the wind speed at the location of the Bishop Rock lighthouse based on the meteorological data, and the load distribution on the helideck due to such a wind condition, using computational fluid dynamic analysis. Then, we used the calculated wind load with other mechanical loads in the events of normal and emergency landings of a helicopter on this structure to find the best design configuration for this helideck. We generated a design space for different configurations of a beam structure and carried out, static, transient and buckling analysis to assess each case using finite element method. The selection criterion was set to find the structure with the minimum volume fraction and compliance while keeping the stress below the allowable stress. We found the structure with eight vertical and circumferential sections featuring two rows of diagonal bracing with one at the base and the other one at the third section from the base of the helideck was the optimum design for the considered loading in this work. This framework can be adopted for the design and optimisation of other offshore structures by other researchers and designers