Techniques to Understand Computer Simulations: Markov Chain Analysis

The aim of this paper is to assist researchers in understanding the dynamics of simulation models that have been implemented and can be run in a computer, i.e. computer models. To do that, we start by explaining (a) that computer models are just input-output functions, (b) that every computer model can be re-implemented in many different formalisms (in particular in most programming languages), leading to alternative representations of the same input-output relation, and (c) that many computer models in the social simulation literature can be usefully represented as time-homogeneous Markov chains. Then we argue that analysing a computer model as a Markov chain can make apparent many features of the model that were not so evident before conducting such analysis. To prove this point, we present the main concepts needed to conduct a formal analysis of any time-homogeneous Markov chain, and we illustrate the usefulness of these concepts by analysing 10 well-known models in the social simulation literature as Markov chains. These models are: • Schelling\'s (1971) model of spatial segregation • Epstein and Axtell\'s (1996) Sugarscape • Miller and Page\'s (2004) standing ovation model • Arthur\'s (1989) model of competing technologies • Axelrod\'s (1986) metanorms models • Takahashi\'s (2000) model of generalized exchange • Axelrod\'s (1997) model of dissemination of culture • Kinnaird\'s (1946) truels • Axelrod and Bennett\'s (1993) model of competing bimodal coalitions • Joyce et al.\'s (2006) model of conditional association In particular, we explain how to characterise the transient and the asymptotic dynamics of these computer models and, where appropriate, how to assess the stochastic stability of their absorbing states. In all cases, the analysis conducted using the theory of Markov chains has yielded useful insights about the dynamics of the computer model under study.Computer Modelling, Simulation, Markov, Stochastic Processes, Analysis, Re-Implementation

Kink fluctuation asymptotics and zero modes

In this paper we propose a refinement of the heat kernel/zeta function treatment of kink quantum fluctuations in scalar field theory, further analyzing the existence and implications of a zero energy fluctuation mode. Improved understanding of the interplay between zero modes and the kink heat kernel expansion delivers asymptotic estimations of one-loop kink mass shifts with remarkably higher precision than previously obtained by means of the standard Gilkey-DeWitt heat kernel expansion.Comment: 21 pages, 8 figures, to be published in The European Physical Journal

Substrate-Integrated Folded Waveguide Slot Antenna

In recent years a number of researchers have proposed novel techniques for fabricating rectangular waveguide using microwave integrated circuit techniques. These so-called substrate integrated guides have been fabricated using multilayer LTCC, multi- and single-layer microwave laminates and photoimageable thick films. All of these structures result in dielectric filled rectangular waveguide and as such have a width reduction of 1/square root of the relative permittivity over conventional waveguide. Furthermore, by their very nature they are easily integrated with planar transmission lines and circuits, allowing hybrid waveguide/microstrip systems to be fabricated on a single substrate. Several researchers have investigated slot antennas and arrays in substrate-integrated guide. In this paper we show a slot antenna in a folded substrate-integrated waveguide. These waveguides have half the width of the other types of substrate-integrated waveguide. As such the present structure allows arrays of slot antennas to be more highly integrated

Errors and Artefacts in Agent-Based Modelling

The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles

On the semiclassical mass of S2{\mathbb S}^2-kinks

One-loop mass shifts to the classical masses of stable kinks arising in a massive non-linear ${\mathbb S}^2$-sigma model are computed. Ultraviolet divergences are controlled using the heat kernel/zeta function regularization method. A comparison between the results achieved from exact and high-temperature asymptotic heat traces is analyzed in depth.Comment: RevTex file, 15 pages, 2 figures. Version to appear in Journal of Physics

On domain walls in a Ginzburg-Landau non-linear S^2-sigma model

The domain wall solutions of a Ginzburg-Landau non-linear $S^2$-sigma hybrid model are unveiled. There are three types of basic topological walls and two types of degenerate families of composite - one topological, the other non-topological- walls. The domain wall solutions are identified as the finite action trajectories (in infinite time) of a related mechanical system that is Hamilton-Jacobi separable in sphero-conical coordinates. The physical and mathematical features of these domain walls are thoroughly discussed.Comment: 26 pages, 18 figure

Optimal Technology Selection and Operation of Bio-methane CHP Units for Commercial Buildings

This paper explores the optimal implementation of bio-methane fuelled combined heat and power (CHP) systems to satisfy heat and electricity demands of commercial buildings; with the overarching goal of making cost-effective investments and decarbonizing building operations. The research work consisted in the development of a CHP technology selection and operation (TSO) optimization model. Its results can be utilized to develop a strategy for investment in bio-methane CHP projects for a portfolio of buildings. The TSO model enables a new approach for the selection and operation of CHP units that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available CHP technologies, the mathematical model simultaneously solves for an optimal CHP unit selection and operational strategy for a determined building based on a preferred objective: minimizing cost, minimizing GHG emissions, or a mix of both. Results of this model prove that attractive cost and emissions savings are possible through the optimal selection and operation of CHP technologies fuelled by bio-methan

Operational and Economic Analysis of GSHP Coupled with Refrigeration Systems in UK Supermarkets

Ground Source Heat Pumps (GSHP) are capable of reducing energy consumption by operating at higher efficiencies than conventional gas systems, especially if coupled with refrigeration units such as in supermarkets. In principle, the heat rejected by refrigerators can be harnessed to raise the efficiency of the heat pumps. This paper presents the results of an operational and economic analysis conducted on this innovative system. Overall, the efficiency of all the GSHP systems under consideration appears to be above the eligibility threshold for the UK Government’s incentive (Renewable Heat incentive, RHI), with the average Seasonal Coefficient of Performance (SCOP) of the stores being 3.0 in 2014. From an economic perspective, such average performance leads to more than £120,000 of operational savings per year compared to gas boiler systems. Calculations show an investment Payback Time (PBT) of less than 8 years. Finally, the paper highlights potential cost reductions achievable through operational and design modifications. Overall results show that GSHP coupled with refrigeration systems present sound fundamentals to be considered as an attractive investment opportunity for food retailers

Assessing the modelling approach and datasets required for fault detection in photovoltaic systems

Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets