Multidomain Simulation Model for Analysis of Geometric Variation and Productivity in Multi-Stage Assembly Systems

Nowadays, the new era of industry 4.0 is forcing manufacturers to develop models and methods for managing the geometric variation of a final product in complex manufacturing environments, such as multistage manufacturing systems. The stream of variation model has been successfully applied to manage product geometric variation in these systems, but there is a lack of research studying its application together with the material and order flow in the system. In this work, which is focused on the production quality paradigm in a model-based system engineering context, a digital prototype is proposed to integrate productivity and part quality based on the stream of variation analysis in multistage assembly systems. The prototype was modelled and simulated with OpenModelica tool exploiting the Modelica language capabilities for multidomain simulations and its synergy with SysML. A case study is presented to validate the potential applicability of the approach. The proposed model and the results show a promising potential for future developments aligned with the production quality paradigm

Performance Modelling and Optimisation of Multi-hop Networks

A major challenge in the design of large-scale networks is to predict and optimise the total time and energy consumption required to deliver a packet from a source node to a destination node. Examples of such complex networks include wireless ad hoc and sensor networks which need to deal with the effects of node mobility, routing inaccuracies, higher packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the computational limitations of the nodes. They also include more reliable communication environments, such as wired networks, that are susceptible to random failures, security threats and malicious behaviours which compromise their quality of service (QoS) guarantees. In such networks, packets traverse a number of hops that cannot be determined in advance and encounter non-homogeneous network conditions that have been largely ignored in the literature. This thesis examines analytical properties of packet travel in large networks and investigates the implications of some packet coding techniques on both QoS and resource utilisation. Specifically, we use a mixed jump and diffusion model to represent packet traversal through large networks. The model accounts for network non-homogeneity regarding routing and the loss rate that a packet experiences as it passes successive segments of a source to destination route. A mixed analytical-numerical method is developed to compute the average packet travel time and the energy it consumes. The model is able to capture the effects of increased loss rate in areas remote from the source and destination, variable rate of advancement towards destination over the route, as well as of defending against malicious packets within a certain distance from the destination. We then consider sending multiple coded packets that follow independent paths to the destination node so as to mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium and obtain the time-dependent properties of the packet’s travel process, allowing us to compare the merits and limitations of coding, both in terms of delivery times and energy efficiency. Finally, we propose models that can assist in the analysis and optimisation of the performance of inter-flow network coding (NC). We analyse two queueing models for a router that carries out NC, in addition to its standard packet routing function. The approach is extended to the study of multiple hops, which leads to an optimisation problem that characterises the optimal time that packets should be held back in a router, waiting for coding opportunities to arise, so that the total packet end-to-end delay is minimised

An enhanced compressor sub-idle map generation method

Several techniques have come about for the mathematical extrapolation of compressor maps from the idle region down to zero speed. Relatively little work has been done on methods which attempt to extract compressor sub - idle performance from physical grounds. This paper focuses on the design of an axial compressor rig to obtain sub - idle data in the form of locked rotor and windmill characteristics. The rig design is presented and the results obtained discussed. The data gathered is used to generate physics - based sub - idle compressor maps which are then compared to existing method s for sub - idle map generation. Interpolation from the locked rotor characteristic is shown to improve map generation over extrapolation methodologies, while the windmilling characteristic is shown to be an important addition to the interpolation process

Layout Optimization of a repair facility using discrete event simulation

Technological advancements in the field of simulation have enabled production managers to model and simulate their facilities under various scenarios, in order to optimize system performance. In particular the reconfiguration of factory layouts can be time consuming and expensive; Discrete Event Simulation (DES) can be used to model and assess various scenarios to assist production managers with layout planning. Significant benefits can be achieved through the use of DES for factory layout optimization including: decreased lead times, reduced manufacturing costs, efficient materials handling and increased profit. This paper presents the development of a DES model in WITNESS for the analysis and factory layout optimization of a repair facility. The aim of the model is to allow decision makers to assess various layouts and configurations with a view to optimize production. The model has been built with a link to an Excel spreadsheet to enable data input and the visualization of Key Performance Indicators (KPIs). Specific functions have been built into the simulation model to set and save new layouts within Excel to facilitate layout optimization. The model will be used to optimize the factory configuration

A hierarchical model for novel schemes of electrodialysis desalination

A new hierarchical model for the electrodialysis (ED) process is presented. The model has been implemented into gPROMs Modelbuilder (PSE), allowing the development of a distributed-parameters simulation tool that combines the effectiveness of a semi-empirical modelling approach to the flexibility of a layered arrangement of modelling scales. Thanks to its structure, the tool makes possible the simulation of many different and complex layouts, requiring only membrane properties as input parameters (e.g. membrane resistance or salt and water permeability). The model has been validated against original experimental data obtained from a lab scale ED test rig. Simulation results concerning a 4-stage treatment of seawater and dynamic batch operations of brackish water desalination are presented, showing how the model can be effectively used for predictive purposes and for providing useful insights on design and optimisation

Influence of centrifugal compressor system components on its general rotordynamic characteristics

Nowadays most countries are depending on Oil and Gas for their energy supply. In such operations, centrifugal compressors are dominating most of the used critical machines hence it is important to give these turbomachines more consideration in terms of their technical performance and reliability. Centrifugal compressors are one of many turbomachines that require technical solutions for Enhanced Oil Recovery (EOR). The oil and gas fields have different production environments which require adequate selection of compressors to handle the variance in gas and oil specifications and this in turn force the equipment manufacturers to revise their currently used design specifications. This research presents different types of compressors and their work principles with an emphasis on centrifugal compressor components The literature review carried in this research describes different cases in turbomachinery rotordynamics where failures were encountered at the commissioning and operation stages. Also the literature shows how these machines are improved technically by improving the compressor components performance such using Pocket Damper seals and tilting type bearings. The aim of this research is to study the factors affecting Rotordynamic behaviour of large natural gas centrifugal compressors. The study will review the influence of various conditions of rotor components such as bearings, seals, impellers, etc on the overall Rotordynamic stability at various process conditions ... [cont.]

Design and Development of Micro Turbine

Micro turbines are a relatively new type of combustion turbine that produces both heat and electricity on a small scale. Micro turbines offer an efficient and clean solution to direct mechanical drive markets such as compression and air-conditioning. This report focuses on the design and development of a micro turbine driven by compressed nitrogen gas. The available literature regarding the design aspects of micro turbine were reviewed in detail. Gas turbine cycle and operation of micro turbine was studied and reported. The turbine blades and nozzles were designed with the help of Gambit software using a given set of cylindrical coordinates. The turbine has a radial inlet and axial outlet. A proper meshing scheme was used to mesh the turbine and nozzle assembly. CFD analysis was carried out by Fluent software to get the velocity vectors using a set of suitable inputs

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research