Object-oriented Implementation Research Articles

BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis

This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pipelines, alarms, and visualization through control charts and diagnostic maps. BibMon also includes real and simulated datasets for benchmarking, comparative performance analysis of different models, and hyperparameter tuning. The package is designed to be highly extensible, allowing for easy integration of new models and methodologies through its object-oriented implementation. Currently, BibMon is in production at Petrobras, a major player in the energy industry, monitoring numerous industrial assets and enabling real-time detection and diagnosis of equipment and process faults. The software is open source and available at: https://github.com/petrobras/bibmon.

An object-oriented implementation of a recursive “quantum network” solver and its application to district heating networks

With the increasing importance of energy efficiency and sustainability, the demand for high-performance district heating networks is also on the rise. As traditional engineering methods were often no longer sufficient, several packages for numerical simulation have evolved. Many of them offer high accuracy at the cost of considerable manual preparation and computational effort. However, there is still no user-friendly software that is equally suitable for simplified studies in the early project phase, for the rapid optimisation of multiple concepts and for subsequent detailed planning and dimensioning. For this reason and in cooperation with some companies from the energy sector, we had conceived a novel, highly flexible modular approach, the so-called “quantum networks”, where all parts of the district heating network are appropriately abstracted into quantum elements. Now we present our recent implementation of this model in an object-oriented C++ library. Starting from a generalised base class and making use of the concepts of inheritance and polymorphism, the idea of different levels of detail for the same element type is directly realised and can always be further refined. In addition, as all elements are derived from the same base class, they all share the same outer appearance and can thus be easily combined or interchanged. Based on these prerequisites, a dedicated thermo-hydraulic solver has then been developed. Thanks to its recursive design requiring neither outer iterations nor matrix inversions, it proves to be extremely fast and is therefore suitable for rapid design or optimisation studies in which a vast number of configurations has to be computed. To conclude this part of the work, the developed C++ library was benchmarked on two use cases covering a full year of operation that can now be computed in approximately one second of runtime.

Rural And Urban Land And Building Tax Calculation Application (PBB-P2)

The PBB-P2 application, which focuses on the case study of Cangkuang Wetan Village, is specifically created to streamline and enhance the inputting and managing of rural and urban land and building taxes. The data-gathering methods employed, including thorough observation, comprehensive interviews, and extensive literature review, ensure the accuracy and reliability of the system. The system design was created with descriptive techniques and the object-oriented analysis and design (OOAD) development methodology, employing, which involves a process of analysis, system design, and object-oriented implementation outlinas ed by the Unified Modeling Language (UML). This design aims to provide benefits to Cangkuang Wetan Village by automating the tax computation process, managing citizen data, and generating time-saving reports.

An open-source implementation of tree-based scan statistics.

We develop an open-source R package to implement tree-based scan statistics (TBSS) analyses. TBSS are data mining methods used by the United States Food and Drug Administration and the Centers for Disease Control. They simultaneously screen thousands of hierarchically aggregated outcomes to identify unsuspected adverse effects of drugs or vaccines, accounting for multiple comparisons. The general structure of TBSS is highly adaptable, with four essential components: (1) a hierarchical outcome structure, (2) a test statistic to be computed for each element of the hierarchy, (3) an algorithm to generate data replicates under a null distribution, and (4) observed outcomes at the lower level of the hierarchy. We encode the general TBSS framework in a convenient R package that offers user-friendly functions for the most used TBSS methods. To illustrate the performance of our software, we evaluated two examples of archetypical TBSS analyses previously analyzed using proprietary, closed-source TreeScan™ software. The first considers the risk of congenital malformations associated with first-trimester exposure to valproate, and the second compares exposure to newly prescribed canagliflozin with a dipeptidyl peptidase 4 inhibitor in adults affected by type 2 diabetes. The results of the original studies are replicated. The diffusion of an open-source implementation of TBSS can enhance innovation of TBSS methods and foster collaborations. We offer an intuitive R package implementing standard TBSS methods with accompanying tutorials. Our unified object-oriented implementation allows expert users to extend the framework, introduce new features, or enhance existing ones.

Double inclined cracks overlapping effect on mixed stress intensity factors using XFEM object-oriented implementation

The object of research is the Mixed Mode Stress Intensity Factor (MMSIF) of a two-dimensional (2D) plate. With the emergence of modern technologies and advanced innovations which contribute to the development and improvement of the design, implementation and management of construction projects, it has become easier. However, it is very difficult to manufacture components free from unavoidable defects, such as cracks, which lead to material deterioration and ultimately shorten its service life. Based on the process of local enrichment region using partition of unity concept, the extended finite element method (XFEM) has overcome the limitations of the standard FEM method in terms of modeling and numerical simulation of discontinuities (cracks) while gaining its general advantages. This makes XFEM a powerful and widely used digital tool in recent years. One of the most frequently raised problems in the discontinuities field (cracks) is the phenomenon of juxtaposition of multiple cracks in a cracked isotropic plate, which must be studied to determine the extent of its effect on the crack stress intensity factor in order to obtain higher safety reliability. On this basis, an improved object-oriented programming (OOP) with extended finite elements was used because of its great importance and well-known benefits. In this paper, the MMSIF of a 2D plate is determined to show the effect of the out-of-phase orientation of the angle, as well as the effect of the juxtaposition of two inclined cracks. As a result of the research, it is shown that, the convergence between the results obtained in this study with those reported in the literature, and to theoretical values is remarkable, and their close agreement was noted. In the future, based on the object-oriented approach characteristics represented by flexibility, scalability, and modularity, which were explained in this research, this proposed approach can be enriched to include heterogeneous materials modeling, whether linear or nonlinear, crack propagation in dynamics, in addition to Complex 3D industrial problems.

Parametric Information Geometry with the Package Geomstats

We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher–Rao Riemannian manifolds of widely used parametric families of probability distributions, such as normal, gamma, beta, Dirichlet distributions, and more. The module further gives the Fisher–Rao Riemannian geometry of any parametric family of distributions of interest, given a parameterized probability density function as input. The implemented Riemannian geometry tools allow users to compare, average, interpolate between distributions inside a given family. Importantly, such capabilities open the door to statistics and machine learning on probability distributions. We present the object-oriented implementation of the module along with illustrative examples and show how it can be used to perform learning on manifolds of parametric probability distributions.

High-Performance Uniform Damping Model for Response History Analysis in OpenSees

ABSTRACT A realistic and practical damping model is essential for structural dynamics. In this study, the second-order accurate time discretization of a high-performance uniform damping model for response history analysis is derived. Its stability and accuracy are proven, and its object-oriented implementation is introduced in OpenSees. The proposed time discretization accurately represents energy dissipation in individual components with minimal additional computational costs. It applies to various time integrators, element formulations, and structural systems, allowing for high-fidelity evaluation of structural performance and ensuring public safety. These advantages are illustrated through detailed case studies.

Comparing Flowchart and Swim Lane Activity Diagram for Aiding Transitioning to Object-Oriented Implementation

Object Oriented Programming (OOP) paradigm is one of the programming styles that emerged in response to the challenge of designing complex software. However, students find it hard to conceptualize objects when they were already accustomed to non Object Oriented approach to programming. This paper hypothesizes that introducing Object Oriented (OO) notations to students during the design phase will smoothen their transition to Object Oriented Programming. To test the hypothesis, an experiment was conducted with the students of Al-Qalam University Katsina, Nigeria. The participating students were divided into two groups: (i) Flowchart group - representing the classical approach where flowcharts were used to design solutions. (ii) Activity Diagram group - which represents the control group in which swim lane activity diagram, as Object Oriented notation, was introduced to them at the design phase. Both groups were later introduced to Class Responsibility Collaborators (CRC) cards as an Object Oriented implementation model. The students were tested, four different times, on how well they converted flowcharts or activity diagrams, as the case may be, into Class Responsibility Collaborators cards, and their performances were recorded. The results were analyzed using Repeated Measure Analysis of Variance (ANOVA). Unexpectedly, the Flowchart group outperformed the Activity Diagram group but the results were not statistically significant. Similarly, there was no statistical difference between males’ and females’ performances.

GenASiS Mathematics: Object-oriented manifolds, operations, and solvers for large-scale physics simulations (version 2)

GenASiSMathematics provides modern Fortran classes furnishing extensible object-oriented functionality for the solution of fields governed by selected partial differential equations. The initial release included extensible object-oriented implementations of simple meshes and the evolution of generic conserved currents thereon. This revision—Version 2 of Mathematics—includes significant reorganization and streamlining of these classes, higher-order reconstruction by a different method, a Poisson solver, coarsening to avoid Courant time step limitations near coordinate singularities, and the offloading of computational kernels to GPUs. New version program summaryProgram Title:HomogeneousSpheroid, RectangularContraction, RectangularExpansion, SphericalContraction, SphericalExpansion (example problems illustrating GenASiSMathematics)CPC Library link to program files:https://doi.org/10.17632/mzvxngwtw6.2Developer's repository link:https://github.com/GenASiSCode Ocean capsule:https://codeocean.com/capsule/4106782Licensing provisions: GPLv3Programming language: Modern Fortran; OpenMP (tested with recent versions of GNU Compiler Collection (GCC), Cray Compiler Environment (CCE), IBM XL Fortran compiler)Journal reference of previous version: Computer Physics Communications 222 (2018) 384Does the new version supersede the previous version?: YesReasons for the new version: This version includes significant reorganization and streamlining, higher-order reconstruction by a different method, a Poisson solver, coarsening to avoid Courant time step limitations near coordinate singularities, and the offloading of computational kernels to GPUs.Summary of revisions: The left part of Fig. 1 shows the revised structure of GenASiSMathematics. The Algebra and Calculus divisions contain relatively simple stand-alone functionality for such tasks as root finding, interpolation, numerical integration, and the solution of ordinary differential equations. The major functionality currently provided in Mathematics, which focuses on the solution of selected classes of partial differential equations, is now collected in the CauchyProblems division.The structure of CauchyProblems is displayed in the right part of Fig. 1. The solution of partial differential equations presumes the existence of Manifolds (or ‘spaces’) and Fields thereon whose configuration we seek. The two basic types of Cauchy problems are boundary value problems, or Constraints; and initial value problems, or Evolutions.The structures of Manifolds, Fields, Constraints, and Evolutions are displayed in Fig. 2. Individual coordinate patches or Charts are combined into Atlases in order to represent Manifolds. As for Fields, the most basic functionality for a set of fields on a manifold (including I/O) is representedby classes in FieldSets. Specialized sets of fields needed for Cauchy problems are Geometries, including coarsening functionality to avoid Courant time step limitations near coordinate singularities; and CurrentSets, which represent ‘conserved’—or more properly, when source terms are present, ‘balanced’—currents, that is, densities and their corresponding fluxes. CalculusFields provides integrals of fields on manifolds, including volume and surface integrals used to tally conserved and balanced quantities, as well as spherical and azimuthal averaging. New to this version of Mathematics is the solution of PoissonEquations via multipole expansion, the first capability for Constraints or boundary value problems.The solution of balance equations is the type of initial value problem currently handled by Evolutions. As in Version 1, finite-volume discretization results in a large set of ordinary differential equations solved by the Runge-Kutta method. Now however, the right-hand sides, or Slopes, are handled by variations of a recursive class that allows the construction of hierarchies of terms. Thus complicated equations can be computed and included in I/O in pieces representing various terms, with results assembled in an extensible and quasi-automated fashion. For example, the partial derivatives, and geometric source terms from connection coefficients, can be computed and visualized separately, while being automatically combined to give the full divergence of an energy-momentum tensor. Third-order reconstruction (with limiting) to discretize cell interfaces is now included. Multi-stage Runge-Kutta Steps are then taken by Integrators in order to advance the solution of fields from their initial condition.Relative to Version 1 of Mathematics, the Manifolds and Fields classes are significantly reorganized and streamlined. Previously, classes for fields, geometry, and I/O were interleaved with those of charts and atlases. This provided for chart and atlas classes with self-contained geometry and I/O functionality, but in a manner that proved unwieldy and indeed unnecessary. This has now been disentangled: classes for fields, geometry and I/O are built entirely on top of full manifolds (i.e. classes for atlases).All computational kernels (except those specifically needed for checkpointing, that is, I/O) are coded for offloading to GPUs via OpenMP.Nature of problem: By way of illustrating GenASiSMathematics functionality, solve an example potential problem and imposed advection problems.Solution method: Multipole expansion for solution of the Poisson equation; finite-volume discretization; second- and third-order reconstruction with limiting; HLL Riemann Solver; Runge-Kutta integration.Additional comments including restrictions and unusual features: Uses the MPI [1] and Silo [2] libraries. The example problems named above are not ends in themselves, but serve to illustrate the functionality available though GenASiSMathematics. In addition to these more substantial examples, we provide individual unit test programs for the classes comprised by GenASiSMathematics.GenASiSMathematics is available in the CPC Program Library and also at https://github.com/GenASiS.

Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability

Abstract. The Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. This toolbox can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. While improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition from which all models inherit. The new modelling framework maintains and improves on several good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats that simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub (https://github.com/wknoben/MARRMoT, last access: 12 May 2022; https://doi.org/10.5281/zenodo.6484372, Trotter and Knoben, 2022b).

Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data.

Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity data sets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query data sets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme–substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step toward standardizing data analysis across the field and in chemical biology and biochemistry at large.

Obtaining the most exact Jacobian of the time modelling of power electronics structures for gradient-based optimisations

PurposeThe paper aims to deal with the exact computation of the Jacobian of a time criteria from a numerical simulation of power electronics structures, for the sizing by gradient-based optimization algorithm.Design/methodology/approachRunge Kutta 44 is used to solve the state equations. The generic approach combines numerical and symbolic approaches. The modelling of the static converter is based on ideal switches.FindingsThe paper extends the state equations to derivate any state variable according a sizing parameter. The integral expressions used for some sizing performances (e.g. average or RMS values) mix symbolic and numerical approaches. Choices are made for the derivatives of the extrema of which the search is not a continuous process. The use of an object-oriented implementation allows to have generic formulation of some design performances.Research limitations/implicationsThe paper aims to propose and to test formulations of sizing criteria and their gradients; so, the modelling of the study case is carried out manually. Due to generic modelling approach used for the power electronics, the model is not completely continuous. So, the derivatives according some parameters (e.g. switch controls) must be carried out by finite differences. However, as the global behaviour is continuous, it is not critical.Practical implicationsThe proposed formulations can be easily applied on simple static converter applications. For applications with large state equations, it should be possible to use the basic model of switches used in simulation tools of power electronics. The solving process and the sizing criteria formulation (with their derivatives) are generic and can be instantiate for any study.Originality/valueThe approach proposes formulations giving a numerical sizing dynamic model with a Jacobian computed, if possible, by an exact derivation useful for optimization studies. The approach gives fast simulation and fast computation of the derivatives by combining numerical and analytical approaches.

PICNIK: A python package with isoconversional computations for non-isothermal kinetics

Isoconversional computations are widely-used methods to determine activation energies for thermally stimulated processes. pICNIK (python Isoconversional Computations for Non-Isothermal Kinetics) is an open-source module designed with the purpose of being a seed to a complete Python package to facilitate kinetic computations. It is object oriented with two classes: DataExtraction and ActivationEnergy. Five isoconversional methods were implemented: Friedmann's method, the Kissinger-Akahira-Sunose method, Ozawa-Flynn-Wall method, Vyazovkin and advanced Vyazovkin. This module allows to compute the activation energy from thermogravimetric data in minutes and the results can be exported as spreadsheet format or comma separated values files instead of traditional tedious and time consuming data processing. The module was validated with simulated data and two study cases: vaporization of n-decane and the thermal degradation of polypropylene. Program summaryProgram Title: pICNIKCPC Library link to program files:https://doi.org/10.17632/dpwtnmjpj5.1Developer's repository link:https://github.com/ErickErock/pICNIKCode Ocean capsule:https://codeocean.com/capsule/0740039Licensing provisions: MITProgramming language: PythonNature of problem: Numerical determination of the parameters of the Arrhenius function and the reaction model under the isoconversion hypothesis.Solution method: A Python module with implemented isoconversional methods. The algorithms implemented include several computations such as linear regressions, numerical integration and differentiation, and minimizing functions, all over several data sets.Additional comments including restrictions and unusual features: The initial data processing is focused on thermogravimetry and may not work for calculating conversion to other properties. However, the object-oriented implementation allows to overcome this limitation easily. Methods for calculating the Arrhenius pre-exponential factor and the reaction model remain to be implemented. It is a small module conceived to be further developed by the community. Another limitation of the code is that it does not include a graphical interface.

Algorithm 1020: Computation of Multi-Degree Tchebycheffian B-Splines

Multi-degree Tchebycheffian splines are splines with pieces drawn from extended (complete) Tchebycheff spaces, which may differ from interval to interval, and possibly of different dimensions. These are a natural extension of multi-degree polynomial splines. Under quite mild assumptions, they can be represented in terms of a so-called multi-degree Tchebycheffian B-spline (MDTB-spline) basis; such basis possesses all the characterizing properties of the classical polynomial B-spline basis. We present a practical framework to compute MDTB-splines, and provide an object-oriented implementation in Matlab . The implementation supports the construction, differentiation, and visualization of MDTB-splines whose pieces belong to Tchebycheff spaces that are null-spaces of constant-coefficient linear differential operators. The construction relies on an extraction operator that maps local Tchebycheffian Bernstein functions to the MDTB-spline basis of interest.

Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

Linear systems arising in interior methods for convex optimization: a symmetric formulation with bounded condition number

We provide eigenvalues bounds for a new formulation of the step equations in interior methods for convex quadratic optimization. The matrix of our formulation, named , has bounded condition number, converges to a well-defined limit under strict complementarity, and has the same size as the traditional, ill-conditioned, saddle-point formulation. We evaluate the performance in the context of a Matlab object-oriented implementation of PDCO, an interior-point solver for minimizing a smooth convex function subject to linear constraints. The main benefit of our implementation, named PDCOO, is to separate the logic of the interior-point method from the formulation of the system used to compute a step at each iteration and the method used to solve the system. Thus, PDCOO allows easy addition of a new system formulation and/or solution method for experimentation. Our numerical experiments indicate that the formulation has the same storage requirements as the traditional ill-conditioned saddle-point formulation, and its condition is often more favourable than the unsymmetric block formulation.

Studies in object-oriented programming backbone implementations

This work is a combination of conceptual and hands on based study aimed at laying a foundation for practical Object-Oriented software construction. First it presents a conceptual study of a number of backbone concepts of modern Object-Oriented Programming (OOP) languages. Secondly, it attempts to demonstrate real-life implementations of these concepts using Python Programming Language. This work touches on practical issues on Class and Object Creation, especially on the syntax and creation, and demystifies the subject matter using a simple table of rules. The OOP concept of Inheritance was studied, with focus on the three major types of inheritance. The self-argument, and constructors were studied, with focus on the three constructors - default, parameterized, and non-parameterized constructors. A brief discussion, and pictorial illustration was also made on the disparity between normal mathematical functions and OOP method calls. Further areas of studies are the concept of overriding between the parent and child class, as well as the OOP puzzle commonly known as Diamond Problem, including code segment and diagrammatic illustration of Python-based solutions. There are a number of other back-bone concepts in OOP not covered in this study, such as Encapsulation, Abstraction, Meta-Programming, among others, which will form areas of focus in future studies. Effort was made to enhance the overall presentation through practical illustrations using source codes, annotated diagrams, and discussions. It is hoped that this work will be very useful to researchers and other practitioners in Object Oriented implementations.

Stochastic Control With Affine Dynamics and Extended Quadratic Costs

An extended quadratic function is a quadratic function plus the indicator function of an affine set, i.e., a quadratic function with embedded linear equality constraints. In this article, we show that, under some technical conditions, random convex extended quadratic functions are closed under addition, composition with an affine function, expectation, and partial minimization, i.e., minimizing over some of its arguments. These properties imply that dynamic programming can be tractably carried out for stochastic control problems with random affine dynamics and extended quadratic cost functions. While the equations for the dynamic programming iterations are much more complicated than for traditional linear quadratic control, they are well suited to an object-oriented implementation, which we describe. We also describe a number of known and new applications.

An Abaqus plugin for efficient damage initiation hotspot identification in large-scale composite structures with repeated features

Identifying the hotspots for damage initiation in large-scale composite structure designs presents a significant challenge due to the high modelling cost. For most industrial applications, the finite element (FE) models are often coarsely meshed with shell elements and used to predict the global stiffness and internal loads. Because of the lack of detailed descriptions for the composite materials and 3D stress states, most of the established failure criteria are not applicable. In this work we present an Abaqus plugin tool which implements a framework to identify the hotspots by using a pre-computed database generated for specific, heavily-repeated feature types based on a given structural model. Developed with an object-oriented implementation in Python, this software is split into two main parts, specifically for feature generation and structural analysis. The pre-computed model presents a full 3D description for the considered feature and works as a submodel to the coarse structure model driven by a one-way transfer of the boundary conditions. The presented framework is an analysis tool for efficient sizing of large-scale composite structures, as it enables 3D damage analysis of the structures in critical zones with significant savings of the modelling and computational cost. The results are compared with conventional FE modelling and satisfactory agreement is observed. In addition, the software also enables the pre-computed database to be stored in an HDF5 data file for further reuse on new structures with the same feature.

An agent-based model of a betting exchange via a single-threaded and object-oriented implementation

The sports betting industry attracts millions of participants to gamble on a wide variety of sports across the world. Horse racing is inextricably linked to gambling, and one of the most popular methods of placing bets is via a betting exchange, such as Betfair. A population of bettors will have a diverse range of beliefs, opinions, and experiences to help guide them to the bets they want to place. These prior beliefs are then converted to likelihoods, represented by betting odds, of certain outcomes occurring during a sporting event, either before it has started or while it is running, the latter known as in-play betting. Trading a bet through an exchange involves matching a back bet and a lay bet together to form a near zero-sum game, where one bettor’s profit is another bettor’s loss without taking into account the exchange’s commission fees. This mechanism allows various possible strategies for a bettor to turn a profit gambling on a horse race, with automated betting agents proven to be a popular method of discovering and exploiting strategies. Creating and training these programs requires huge quantities of data, and since horse racing is the most lucrative sports betting market, historical data can be very expensive to obtain and time-consuming to understand. This thesis presents an implementation of a possible solution to this issue, as an alternative to historical data. The Bristol Betting Exchange (BBE), a novel proposal for sports betting research devised by Cliff (2021) [8], is a model simulation of a betting exchange, that is composed of a population of automated betting agents, as well as a horse race simulator that produces compelling horse race scenarios. My version of the BBE is entirely written in the Python 3 programming language, is single-threaded and provides a simple Command-Line-Interface, all to aid accessibility for future researchers who may be non-expert programmers. I also designed and developed a prototype, cloud-based version of this implementation that can reduce computational cost for users and increase the speed of generating large datasets via the use of multiple virtual machines provided by Amazon Web Services. Future work to improve this implementation will include making it even more accessible, through possible deployment of a cloud application with a web-based Graphical User Interface as a controller, as well as rigorous analysis to compare simulated data against its historical counterpart.