Academic literature on the topic 'Life-cycle optimisation models'

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN).

Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.

To enable decision makers to select sustainable wastewater treatment systems, insight into the sustainability of a wide variety of systems should be provided in a transparent way leaving room for adaptation and interpretation according to the local situation. To provide this insight a structured methodology comparing wastewater treatment systems with respect to sustainability is defined. Similar to life cycle assessment (LCA) three phases can be distinguished: (1) goal and scope definition, (2) inventory analysis, and (3) optimisation and results. In the goal and scope definition we set the system boundaries to include most of the water cycle and part of the food cycle. Furthermore, we defined a multi-disciplinary set of sustainability indicators including technical, economic, environmental, and socio-cultural aspects. In the inventory analysis these sustainability indicators are quantified using simple static models of wastewater unit operations. Selection of unit operations results in a model of a complete wastewater treatment system. In the optimisation phase the decision maker can weigh the different sustainability indicators and select sustainable options through integer programming.

Building performance evaluation is generally carried out through a non-automated process, where computational models are iteratively built and simulated, and their energy demand is calculated. This study presents a computational tool that automates the generation of optimal building designs in respect of their Life Cycle Carbon Footprint (LCCF) and Life Cycle Costs (LCC). This is achieved by an integration of three computational concepts: (a) A designated space-allocation generative-design application, (b) Using building geometry as a parameter in NSGA-II optimization and (c) Life Cycle performance (embodied carbon and operational carbon, through the use of thermal simulations for LCCF and LCC calculation). Examining the generation of a two-storey terrace house building, located in London, UK, the study shows that a set of building parameters combinations that resulted with a pareto front of near-optimal buildings, in terms of LCCF and LCC, could be identified by using the tool. The study shows that 80% of the optimal building’s LCCF are related to the building operational stage (σ = 2), while 77% of the building’s LCC is related to the initial capital investment (σ = 2). Analysis further suggests that space heating is the largest contributor to the building’s emissions, while it has a relatively low impact on costs. Examining the optimal building in terms compliance requirements (the building with the best operational performance), the study demonstrated how this building performs poorly in terms of Life Cycle performance. The paper further presents an analysis of various life-cycle aspects, for example, a year-by-year performance breakdown, and an investigation into operational and embodied carbon emissions.

Renewable energy systems are especially challenging both in terms of planning and operation. Energy system models that take into account not only the costs but also a wide range of environmental impacts support holistic planning. In this way, burden-shifting caused by greenhouse gas mitigation can be identified and minimised at an early stage. The Life cycle Assessment based ENergy Decision support tool LAEND combines a multi-criteria optimising tool for energy system modelling and an integrated environmental assessment for the analysis of decentral systems. By a single or multi-objective optimisation, considering costs, environmental impact indicators as well as weighted impact indicator sets, the model enables the determination of optimal investment planning and dispatch of the analysed energy system. The application of LAEND to an exemplary residential quarter shows the benefit of the model regarding the identification of conflicting goals and of a system that compensates for the different objectives. The observed shift of environmental impacts from the use phase to the production phase of the renewable electricity generators points further to the importance of the integration of the entire life cycle.

The goal of the study is to develop a model that focuses on managing logistics costs at all stages of a product’s life cycle. The model includes several different cost components and provides a wider coverage of individual processes, as logistics costs are present in different areas of a company’s operations. The applicability of the proposed method was tested in a multinational company that manufactures furniture fittings on a randomly selected product. The test results provide a theoretical and practical confirmation of the necessity to manage the logistics costs for an individual product, since other models are focused exclusively on the cost optimisation of individual logistics processes. The model therefore complements the existing knowledge and represents a practical tool for logistics professionals that enables more efficient logistics costs planning at an early stage in the development of a product, which can result in the long-term reduction in the total costs of logistics and improve the quality of business processes.

Popularisation and application of Public Private Partnerships (PPP) model is an important measure used to deepen the investment and financing reform of China. The transportation industry actively explores and promotes the wide application of PPP models as they have injected new impetus into the construction and development of transportation investment. For PPP projects of expressways that are subsidised by government in the construction or operation period, since the traffic flow in the project construction period or the early operation period is low, the government subsidy is more concentrated and the payments are high, which aggravates pressure on the fiscal purse in the current period and is not conducive to the realisation of smooth fiscal expenditure in the project cooperation period. In this regard, aiming at the features of the investment revenue of expressway projects, a whole life-cycle government optimisation of subsidy mode is proposed through the analysis of the problems existing in the general subsidy mode of the PPP model in the present work. The results show that the proposed model can not only smooth government expenditure in the project’s cooperation period, but also fully exploit the advantages of higher income in the middle and later stages of project operation and maximise the growth potential of government revenue. The results of the research can provide decision-making reference for the development of innovative PPP models for transportation infrastructure in the new Era.

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

Doctor of Philosophy(PhD)
Retirement income policy in Australia has undergone significant changes over the last two decades, including the introduction of the Superannuation Guarantee [SG] with mandatory contributions in 1992 and the 2007 superannuation changes with the benefit tax abolition. Numerical implications of adopted pension reforms and reform proposals such as further increases in the SG contribution rate, changes to superannuation taxation and to means-testing of the age pension have been examined mainly by micro-simulation models. These models, often criticized for their lack of theoretical content, provide an incomplete picture of pension policy effects because of no or limited behavioural responses to underlying policy changes. In this thesis, models based on the life-cycle theory of saving pioneered by Modigliani and Brumberg (1954) are applied to simulate behavioural, welfare and macroeconomics effects of proposed changes to Australia’s pension policy. In particular, this thesis develops the following computable models: a life-cycle, single household model, a partial equilibrium, household model and a general equilibrium model with overlapping generations [OLG]. The single household model describes lifetime behaviour of the utility-maximising single household with uncertain lifespan. The model features perfect capital markets, endogenous labour supply and retirement decisions, and it incorporates main aspects of Australia’s pension and income tax policy settings. The simulated policy changes are (i) increase in the SG contribution rate, (ii) superannuation tax changes and (iii) abolition of the age pension means test. The results indicate higher retirement consumption and welfare gains from all the analysed pension policy changes. Partial equilibrium and general equilibrium models introduced in this thesis are built on lifetime behaviour of the single household. Both models distinguish many generations of households by age and, therefore, are capable of studying behavioural and welfare effects of policy changes for different generations. The partial equilibrium model examines behaviour of the household sector in the environment of the fixed factor prices. It is shown, for instance, that welfare gains from the investigated pension policy changes are not uniformly distributed across generations. The general equilibrium OLG model extends the partial equilibrium analyses by incorporating production, government and foreign sectors in addition to household and pension sectors. The model is a small open economy version of Auerbach and Kotlikoff’s (1987) OLG model. The simulation results are significantly different from those in the partial equilibrium framework, driven mainly by the changes in aggregate labour supply. For instance, the higher SG rate policy increases aggregate assets and saving. However, the saving increases are exported abroad rather than invested in the domestic capital stock. Hence, the implications of this policy change for the capital stock and output are minimal. Younger cohorts and future born generations experience consumption and welfare gains but older cohorts are negatively affected by a higher consumption tax rate resulting from this hypothetical policy change.

Aujourd'hui, la fabrication additive (FA) fait évoluer le monde de la fabrication grâce à ses capacités de production de formes complexes couche par couche. L'approche de conception pour la fabrication (DFM) aide à considérer les contraintes de FA et à maîtriser les caractéristiques du produit dans la gestion de son cycle de vie. Plusieurs études sont consacrées à l'approche de conception intégrée pour la FA, mais aucune approche ne prend en compte toutes les étapes du cycle de vie du produit dans le niveau d'optimisation de sa conception et de sa fabrication. Ainsi, cette thèse fournit une approche DFM pour la FA afin d'étudier simultanément différents attributs, contraintes et critères de conception et de fabrication dès la définition du produit. L'approche Peau-Squelette modélise la première définition du produit. Il contient une analyse fonctionnelle, un modèle d'usage et un modèle de fabrication. Dans ce travail, un nouveau moteur de résolution, qui agit à l’interface du modèle de produit et du modèle de fabrication, est proposé grâce à l'analyse des technologies FA et de leurs paramètres et critères. Ce moteur repose sur un problème d'optimisation bi-objectif pour minimiser le temps de production et la masse du matériau en proposant les solutions optimales pour les propriétés mécaniques et la rugosité du produit. Cette méthodologie permet de définir le modèle de produit. L'approche est mise en œuvre à travers une première technologie de dépôt par fil fondu (FDM) pour la production de deux études de cas
Nowadays, Additive Manufacturing (AM) evolves the manufacturing world by its capabilities for production of the complex shapes layer by layer. Design For Manufacturing (DFM) approach helps to overcome the AM constraints and mastering product features in product lifecycle. Several studies are devoted to integrated design approach for AM, but there is no approach that considers all product life cycle steps in optimization level for product and manufacturing process. So, this thesis provides a DFM approach for AM to investigate simultaneously different attributes, constraints, and criteria of design and manufacturing in product definition. Skin-Skeleton approach models the first definition of product and AM. It contains functional analysis, usage model, and manufacturing model. In this work, a novel interface processing engine as an interface between product and manufacturing model is developed through analysis of AM technologies and their parameters and criteria. This engine relies on a bi-objective optimization problem to minimize production time and material mass under limitation of mechanical properties and roughness of the product to obtain the optimal manufacturing parameters. This methodology permits to define the product model. The approach is implemented into Fused Deposition Modeling to verify the methodology through two case studies

Utility Computing is one of the most discussed business models in the context of Cloud Computing. Service providers are more and more pushed into the role of utilities by their customer's expectations. Subsequently, the demand for predictable service availability and pay-per-use pricing models increases. Furthermore, for providers, a new opportunity to optimise resource usage offers arises, resulting from new virtualisation techniques. In this context, the control of service quality and profit depends on a deep understanding of the representation of the relationship between business and technique. This research analyses the relationship between the business model of Utility Computing and Service-oriented Computing architectures hosted in Cloud environments. The relations are clarified in detail for the entire service life cycle and throughout all architectural layers. Based on the elaborated relations, an approach to a delivery framework is evolved, in order to enable the optimisation of the relation attributes, while the service implementation passes through business planning, development, and operations. Related work from academic literature does not cover the collected requirements on service offers in this context. This finding is revealed by a critical review of approaches in the fields of Cloud Computing, Grid Computing, and Application Clusters. The related work is analysed regarding appropriate provision architectures and quality assurance approaches. The main concepts of the delivery framework are evaluated based on a simulation model. To demonstrate the ability of the framework to model complex pay-per-use service cascades in Cloud environments, several experiments have been conducted. First outcomes proof that the contributions of this research undoubtedly enable the optimisation of service quality and profit in Cloud-based Service-oriented Computing architectures.

L'étude du désassemblage des produits en fin de vie (EOL) sous incertitude est un sujet de recherche important en raison de ses avantages en termes de réduction de déchets, d'économie de ressources non-renouvelables et de protection de l'environnement. Les travaux existants sur les lignes de désassemblage supposent que l'information incertaine peut être estimée par des lois de probabilité ou par des fonctions et se focalisent seulement sur des problèmes stochastiques d'équilibrage de ces lignes. Cependant, il n'est pas toujours possible d'obtenir l'information incertaine complète dû à un manque de données historiques ou d'un volume excessif de données. De plus, le problème intégré des lignes de désassemblage a rarement été abordé. Dans cette thèse, quatre nouveaux problèmes stochastiques des lignes de désassemblage avec seulement à disposition l'information incertaine partielle sont investigués. L'objectif est de proposer des modèles et des méthodes efficaces pour ces problèmes. Les principaux travaux apportés par cette thèse sont les suivants :Premièrement, un nouveau problème stochastique d'équilibrage des lignes de désassemblage (SDLBP) est étudié dans le but de minimiser le coût des lignes avec les durées incertaines d'exécution des tâches, en ne tenant compte que de la moyenne, de l'écart-type et de la borne supérieure du taux de changement. Un modèle avec chance-contrainte est d'abord établi, qui est par la suite transformé approximativement en un modèle distribution-free basé sur l'analyse des propriétés. Ensuite, une heuristique est développée pour résoudre le modèle transformé. Des résultats expérimentaux montrent que le modèle distribution-free peut résoudre efficacement ce SDLBP avec de l'information incertaine partielle.Dans la plupart des travaux existants, le temps de cycle qui représente le temps d'achèvement maximal des postes de travail est prédéterminé. Cependant, le coût des lignes de désassemblage et le temps de cycle sont deux critères de performance contradictoires et s'impactent mutuellement. Un nouveau SDLBP bi-objectif est investi pour minimiser le coût des lignes et le temps de cycle, où seule l'information partielle des durées des tâches est requise. Un modèle bi-objectif distribution-free est construit et une méthode ε-contrainte améliorée est conçue. Des expériences montrent que la méthode ε-contrainte proposée peut réduire de plus de 90% le nombre d'itérations par rapport à la méthode ε-contrainte classique.Ces lignes pourraient générer des pollutions pendant le processus de désassemblage des produits EOL, néanmoins ce facteur n'a pas été considéré dans les précédents travaux. Nous nous intéressons à un nouveau SDLBP vert afin de minimiser le coût des lignes et la pollution, dans lequel des postes de travail avec des prix d'achat différents peuvent générer des quantités différentes de pollution. Un nouveau modèle bi-objectif est formulé et une méthode ε-contrainte spécifique au problème est proposée. Des résultats expérimentaux révèlent qu'un choix approprié des postes de travail peut effectivement réduire la pollution d'une ligne de désassemblage.L'optimisation intégrée d'équilibrage et de planification des lignes de désassemblage, qui n'a pas été étudiée auparavant, pourrait améliorer l'efficacité du système de désassemblage et réduire ses dépenses. Un problème stochastique intégré d'équilibrage et de planification des lignes de désassemblage (ISDLBPP) est adressé pour minimiser le coût global du système, où les ratios d'obtention des composantes et leur demande sont supposés être incertains. Une programmation stochastique à deux étapes est construite et des inégalités valides sont proposées pour réduire l'espace de recherche. Puis, des méthodes sample average approximation (SAA) et L-shaped sont proposées pour ce problème. Des expériences montrent que le temps de calcul moyen de la méthode L-shaped est seulement de 60% de celui de la méthode SAA, avec une qualité de solution comparable
Studying the disassembly of End-of-Life (EOL) products under uncertainty is becoming a hot research topic due to its benefits in reducing waste, saving non-renewable resources, and protecting the environment. Existing disassembly line works assume that stochastic information can be estimated as probability distributions or functions and most of them focus on stochastic disassembly line balancing problems. However, it is not always possible to obtain complete stochastic information due to a lack of historical data or excessive data volume, and the integrated disassembly line problem has been rarely addressed. In this thesis, four novel stochastic disassembly line problems with only partial stochastic information are investigated. The purpose is to propose effective models and solution methods for the considered problems. The main works of this thesis are:Firstly, a new stochastic disassembly line balancing problem (SDLBP) is studied to minimize the disassembly line cost under stochastic task processing times, given only the mean, standard deviation, and change-rate upper bound. For the problem, a chance-constrained model is first formulated, which is further approximately transformed into a distribution-free model by property analysis. Then, a fast heuristic is devised to solve the transformed model. Experimental results demonstrate that the distribution-free model can effectively solve the SDLBP with only partial stochastic information.In most existing literature, the cycle time that represents the maximum completion time among workstations is given. However, the disassembly line cost and cycle time are two conflicting performance criteria and impact mutually. In this thesis, a new bi-objective distribution-free SDLBP is studied to minimize the disassembly line cost and cycle time, where partial information of task processing times is required. For the problem, a bi-objective distribution-free model is constructed, and an improved ε-constraint method is designed. Numerical experiments show that the proposed method can reduce more than 90% computation rounds, compared with the basic ε-constraint method.Disassembly lines may generate pollution during separating EOL products, but this factor has not been considered in the previous SDLBP works. In this thesis, we study a new green-oriented distribution-free SDLBP to minimize the disassembly line cost and pollution emission simultaneously, in which workstations with different purchase prices can have different amounts of pollution emissions. For the problem, a new bi-objective model is formulated and a problem-specific ε-constraint method is devised. Experimental results show that selecting appropriately workstations can effectively reduce the pollution emission of a disassembly line. Besides, some managerial insights are discussed.The integrated optimization of disassembly line balancing and planning may enhance the efficiency of the disassembly system and reduce its expenses, which has not been studied before. In this thesis, an integrated stochastic disassembly line balancing and planning problem (ISDLBPP) is addressed to minimize the overall system cost, where component demands and component yield ratios are assumed to be uncertain. For the problem, a two-stage stochastic programming model is established and valid inequalities are devised to reduce the search space. Then, the sample average approximation (SAA) method and the L-shaped method are applied to solve the model. Numerical experiments show that the L-shaped method can save more than 60% computation time than the SAA method, without sacrificing solution quality

In traditional aircraft wing structural design, the emphasis has been on pursuing the minimum weight or improved performance. The manufacturing complexity or cost assessments are rarely considered because it is usually assumed that the minimum weight design is also the minimum cost design. However, experience from industry has shown that this is not necessarily the case. It has been realised that in the cases where no manufacturing constraints are imposed, the extra machining cost can erode the advantages of the reduced weight. As manufacturing cost includes material cost and machining cost, whilst reducing weight can reduce the material cost, if the manufacturing complexity increases greatly as a result the overall cost may not go down. Indeed, if the manufacturing complexity is not checked, the machining cost could increase by more than the amount by which the material cost reduces. To enable the structural manufacturing complexity to be controlled, manufacturing constraints are established in this thesis and integrated into the optimisation of the aircraft wing structural design. As far as the manufacturing complexity is concerned, attention has been paid to both 3-axis and 5-axis machining. The final designs of optimisations with manufacturing constraints prove the efficiency of these constraints in guiding the design in the manufacturing-feasible direction.

Over the last two decades, implementation of membrane bioreactors (MBRs) has increased due to their superior effluent quality and low plant footprint. However, they are still viewed as a high-cost option, both with regards to capital and operating expenditure (capex and opex). The present thesis extends the understanding of the impact of design and operational parameters of membrane bioreactors on energy demand, and ultimately whole life cost. A simple heuristic aeration model based on a general algorithm for flux vs. aeration shows the benefits of adjusting the membrane aeration intensity to the hydraulic load. It is experimentally demonstrated that a lower aeration demand is required for sustainable operation when comparing 10:30 to continuous aeration, with associated energy savings of up to 75%, without being penalised in terms of the fouling rate. The applicability of activated sludge modelling (ASM) to MBRs is verified on a community-scale MBR, resulting in accurate predictions of the dynamic nutrient profile. Lastly, a methodology is proposed to optimise the energy consumption by linking the biological model with empirical correlations for energy demand, taking into account of the impact of high MLSS concentrations on oxygen transfer. The determining factors for costing of MBRs differ significantly depending on the size of the plant. Operational cost reduction in small MBRs relies on process robustness with minimal manual intervention to suppress labour costs, while energy consumption, mainly for aeration, is the major contributor to opex for a large MBR. A cost sensitivity analysis shows that other main factors influencing the cost of a large MBR, both in terms of capex and opex, are membrane costs and replacement interval, future trends in energy prices, sustainable flux, and the average plant utilisation which depends on the amount of contingency built in to cope with changes in the feed flow.

Les Energies Marines Renouvelables (EMR) se développent aujourd’hui très vite tant au niveau de la recherche amont que de la R&D, et même des premiers démonstrateurs à la mer. Parmi ces EMR, l'énergie des vagues présente un potentiel particulièrement intéressant. Avec une ressource annuelle brute moyenne estimée à 40 kW/m au large de la côte atlantique, le littoral français est plutôt bien exposé. Mais l’exploitation à grande échelle de cette énergie renouvelable ne sera réalisable et pertinente qu'à condition d'une bonne intégration au réseau électrique (qualité) ainsi que d'une gestion et d'un dimensionnement optimisé au sens du coût sur cycle de vie. Une première solution de génération tout électrique pour un houlogénérateur a d’abord été évaluée dans le cadre de la thèse de Marie RUELLAN menée sur le site de Bretagne du laboratoire SATIE (ENS de Cachan). Ces travaux ont mis en évidence le potentiel de viabilité économique de cette chaîne de conversion et ont permis de poser la question du dimensionnement de l’ensemble convertisseur-machine et de soulever les problèmes associés à la qualité de l’énergie produite. Puis une seconde thèse a été menée par Judicaël AUBRY dans la même équipe de recherche. Elle a consisté, entre autres, en l’étude d’une première solution de traitement des fluctuations de la puissance basée sur un système de stockage par supercondensateurs. Une méthodologie de dimensionnement de l’ensemble convertisseur-machine et de gestion de l’énergie stockée fut également élaborée, mais en découplant le dimensionnement et la gestion de la production d’énergie et de ceux de son système de stockage. Le doctorant devra donc : 1. S’approprier les travaux antérieurs réalisés dans le domaine de la récupération de l’énergie des vagues ainsi que les modèles hydrodynamiques et mécaniques réalisés par notre partenaire : le LHEEA de l’Ecole Centrale de Nantes - 2. Résoudre le problème du couplage entre dimensionnement/gestion de la chaîne de conversion et dimensionnement/gestion du système de stockage. 3. Participer à la réalisation d’un banc test à échelle réduite de la chaine électrique et valider expérimentalement les modèles énergétiques du stockage et des convertisseurs statiques associés - 4. Proposer une méthodologie de dimensionnement de la chaine électrique intégrant le stockage et les lois de contrôle préalablement élaborées 5. Déterminer les gains en termes de capacités de stockage obtenus grâce à la mutualisation de la production (parc de machines) et évaluer l’intérêt d’un stockage centralisé - 6. Analyser l’impact sur le réseau d’une production houlogénérée selon divers scenarii, modèles et outils développés par tous les partenaires dans le cadre du projet QUALIPHE. L’exemple traité sera celui de l’Ile d’Yeu (en collaboration avec le SyDEV
The work of this PhD thesis deals with the minimization of the per-kWh cost of direct-drive wave energy converter, crucial to the economic feasibility of this technology. Despite the simplicity of such a chain (that should provide a better reliability compared to indirect chain), the conversion principle uses an oscillating system (a heaving buoy for example) that induces significant power fluctuations on the production. Without precautions, such fluctuations can lead to: a low global efficiency, an accelerated aging of the fragile electrical components and a failure to respect power quality constraints. To solve these issues, we firstly study the optimization of the direct drive wave energy converter control in order to increase the global energy efficiency (from wave to grid), considering conversion losses and the limit s from the sizing of an electrical chain (maximum force and power). The results point out the effect of the prediction horizon or the mechanical energy into the objective function. Production profiles allow the study of the flicker constraint (due to grid voltage fluctuations) linked notably to the grid characteristics at the connection point. Other models have also been developed to quantify the aging of the most fragile and highly stressed components, namely the energy storage system used for power smoothing (with super capacitors or electrochemical batteries Li-ion) and power semiconductors.Finally, these aging models are used to optimize key design parameters using life-cycle analysis. Moreover, the sizing of the storage system is co-optimized with the smoothing management

In spite of the great variety of potential advantages, it is also necessary to illuminate the real effects of Standard Software in practice. Recent studies have revealed that 81% of companies interviewed using SAP, do not fully exploit the software’s ability to optimise business processes, though 61% stated that SAP offers very good process optimisation opportunities.[CS01] Therefore this paper evaluates popular life cycle models with respect to their suitability to implement Standard Software in a process driven way.

Abstract Advanced repair techniques intended for jet engine parts are continuously under development and improvement. Patching is a high-tech approach towards reduced scrap rates and an extended life of high pressure compressor blisks. In this work, we contribute to the structural design of patches for compressor blisks with improved high cycle fatigue behaviour. A fully parameterised patch model is developed, which allows the accurate description of the patch geometry. High cycle fatigue is assessed for welding seam positions specified by the patch model. On the basis of this automated process, a multi-objective optimisation is carried out. The fatigue strength and the length of the welding seam are defined as conflicting targets. Pareto-optimal solutions are calculated using a generalised pattern search algorithm. The engineer’s decision for a specific patch geometry can thus be made based on the optimisation results. The application of the new approach to a compressor blisk demonstrates the influence of vibration modes on fatigue strength. We identify sets of optimal suited patch geometries in accordance to the specified damage pattern.

This paper presents the fundamentals of an evolutionary, thermo-economic plant design methodology, which enables an improved and customer-focused optimization of the bottoming cycle of a large Combined Cycle Power Plant. The new methodology focuses on the conceptual design of the CCPP applicable to the product development and the pre-acquisition phase. After the definition of the overall plant configuration such as the number of gas turbines used, the type of main cooling system and the related fix investment cost, the CCPP is optimized towards any criteria available in the process model (e.g. lowest COE, maximum NPV/IRR, highest net efficiency). In view of the fact that the optimization is performed on a global plant level with a simultaneous hot- and cold- end optimization, the results clearly show the dependency of the HRSG steam parameters and the related steam turbine configuration on the definition of the cold end (Air Cooled Condenser instead of Direct Cooling). Furthermore, competing methods for feedwater preheating (HRSG recirculation, condensate preheating or pegging steam), different HRSG heat exchanger arrangements as well as applicable portfolio components are automatically evaluated and finally selected. The developed process model is based on a fixed superstructure and copes with the full complexity of today’s bottoming cycle configurations as well with any constraints and design rules existing in practice. It includes a variety of component modules that are prescribed with their performance characteristics, design limitations and individual cost. More than 100 parameters are used to directly calculate the overall plant performance and related investment cost. Further definitions on payment schedule, construction time, operation regime and consumable cost results in a full economic life cycle calculation of the CCPP. For the overall optimization the process model is coupled to an evolutionary optimizer, whereas around 60 design parameters are used within predefined bounds. Within a single optimization run more than 100’000 bottoming cycle configurations are calculated in order to find the targeted optimum and thanks to today’s massive parallel computing resources, the solution can be found over night. Due to the direct formulation of the process model, the best cycle configuration is a result provided by the optimizer and can be based on a single-, dual or triple pressure system using non-reheat, reheat or double reheat configuration. This methodology enables to analyze also existing limitations and characteristics of the key components in the process model and assists to initiate new developments in order to constantly increase the value for power plant customers.

Hub in the car faces high vibrations and centrifugal stresses. This calls for proper design and analysis. The life cycle of any formula car should be less which is adequate for racing purpose. This work is focused in analyzing low cycle fatigue and conducting vibratory analysis of a split hub of FSAE car. Stress concentration factor is significant in machine elements which give rise to localized stresses for any change in design of surface or abrupt change in cross section. This member acts as stress riser which leads to localized stresses in turn leading to peak stresses introducing cracks. These cracks may propagate and leads to catastrophic failure of machine elements and these conditions leads to fatigue analysis to calculate life. Two approaches are employed here. Based on linear elastic finite element analysis Neuber stresses are calculated from fictive elastic results. Strain Amplitude approach is followed by Coffin-Manson equation to determine Fatigue life. The failure induced by fretting fatigue due to two contact surfaces subjected to an oscillatory loading serves as premature crack nucleation which will gradually become a prominent issue during the running of car. In some cases it reduces the life due to micro slip at the edge of contact. The split pieces of hub talk to each other and create wear which is calculated by fretting. These rotary parts call for structurally rigid geometry. Modes with relatively high mode participation factor can be readily excited by the base vibrations. Vibratory stresses arise due to engine and rotating wheels acts like excitation frequencies which may lead to possible resonance. Campbell diagram is effectively used to modify the stiffness in turn design. Also an approach is done for design optimization of fillet stresses at Sharp edges caused due to bending strength of the split pieces. Optimization of diameter, contact region, root land dimensions is done to ensure stress distribution is uniformly spread along the fillet radius on both pieces of hub which otherwise may lead to crack initiation considering all peak stresses. Design of Experiments technique and optimization methods are used to improve structural integrity by finding the peak sectional stress. Design optimization is done using screening method to ensure strength of material.

Offshore wind farm managers are under increasing pressure to minimise life cycle costs whilst maintaining reliability or availability targets, and to operate within safety regulation. This paper presents a risk based decision-making methodology for undertaking run-repair-replace decisions with the ultimate aim of maximising the Net Present Value (NPV) of the investment in maintenance. The paper presents the methodology developed for the risk based life management of Offshore Wind farms under the remit of the CORLEX (Cost Reduction and Life Extension of Offshore Wind Farms) project funded by DTI (Department of Trade and Industry, UK) Technology Programme on Renewable Energy. Unlike traditional approaches to decision-making that consider either the probability of failure of a component or the consequence of failure in isolation, a risk-based approach considers both these aspects in combination to arrive at an optimal solution. The paper builds a basic Qualitative Risk Analysis methodology to highlight high-risk components that are then investigated further by a Quantitative Risk Analysis. The risk is now quantified in monetary terms and the time of action — replacement or maintenance — indicated by the model is such that the NPV of the action is maximized. The methodology is demonstrated by considering offshore wind turbine tower as the critical component and corrosion as the damage mechanism.

Abstract Designing commercial aircraft to use liquid hydrogen (LH2) is one way to substantially reduce their life-cycle CO2 emissions. The merits of hydrogen as an aviation fuel have long been recognized, however, the handling of a cryogenic fuel adds complexity to aircraft and engine systems, operations, maintenance and storage. The fuel tanks could account for 8–10% of an aircraft’s operating empty weight, so designing them for the least added weight is of high significance. This paper describes the heat transfer model developed in the EU Horizon 2020 project that is used to predict heat ingress to a cylindrical tank with hemispherical end caps with external foam insulation. It accounts for heat transfer according to the state of the tank contents, the insulation material properties, the environment, and the dimensions of the tank. The model also estimates the rate of pressure change according to the state of the fuel and the rate at which fuel is withdrawn from the tank. In addition, a methodology is presented, that allows for tank sizing taking into consideration the requirements of a design flight mission, the maximum pressure developed, and the fuel evaporated. Finally, the study demonstrates how to select optimal insulation material and thickness to provide the lightest design for the cases where no gaseous hydrogen is extracted, and where some hydrogen gas is extracted during cruise, the latter giving gravimetric efficiencies as high as 74%.

Gas turbines are designed to follow specific missions and the metal temperature is usually predicted with deterministic methods. However, in real life the mission is subjected to strong variations which can affect the thermal response of the components. This paper presents a stochastic analysis of the metal temperature variations during a gas turbine transient. A Monte Carlo Method (MCM) with Meta Model is used to evaluate the probability distribution of the stator disk temperature. The MCM is applied to a series of CFD simulations of a stator well, whose geometry is modified according to the deformations predicted during the engine cycle by a coupled thermo-mechanical analysis of the metal components. It is shown that even considering a narrow band for the stochastic output, +/− σ, the transient thermal gradients can be up to two orders of magnitude greater than those obtained with a standard deterministic analysis. Moreover, a small variation in the tail of the input probability density function, a rare event, can have serious consequences on the uncertainty level of the temperature. Rare events although inevitable they are not usually considered during the design phase. In this paper it is shown for the first time that is possible to mitigate their effect, minimizing the maximum standard deviation induced by the tail of the input PDF. The mission optimization reduces the maximum standard deviation by 15% and the mean standard deviation of about 12%. The maximum thermal gradients are also reduced by 10%, although this was not the parameter used as the goal in the optimisation study.

The use of road vehicles has always represented a major contribution to the growth of modern society: it facilitates goods and people mobility, meeting most of the daily needs and it represents a backbone for the development of world economy, (i.e. the industrial field). Nowadays, this mean of transportation, however, given the high number of vehicles on the roads, has a negative impact both on the environment and on the quality of human life. Moreover it leads to an increase in additional costs (i.e. the costs related to environment pollution, global warming and depletion of resources). Such a negative aspect is due to the fact that the drive systems are often characterized by high variability of the load, hence the propulsion system works in areas with low efficiencies and high pollutant emissions. In order to overcome these problems, and to allow the compliance of the road transport system with new European guidelines (i.e White paper, and Horizon 2020), it is necessary to develop innovative technologies able to: - increase the overall powertrain efficiency; - introduce a sustainable alternative fuels strategy including also the appropriate infrastructure; - reduce carbon emission through a decarbonisation approach; In this perspective, in recent years, the technology of electric and hybrid vehicles has been developed, and nowadays it has become a feasible solution in the context of means of transportation. Car/truck-makers and operators look at further developments and innovation in this field in order to optimise the existing solutions and reduce the production costs. The current solution for hybrid vehicles aims to couple a conventional engine with an electrical motor; these two propulsion system are coordinated by an opportune algorithm in order to let the conventional engine operate in its higher efficiency range. Hence the technology foresees the action of endothermic and electrical motors. It is then pivotal for the success of this transport the optimisation of the whole system (electrical and endothermic) in terms of efficiency, sizing and of the control algorithm that coordinate the two propulsion systems. For the modeling of the internal combustion engine conventional approaches, based on the numerical simulation of the combustion process, cannot be used because of their complexity in term of time needed for computing activity. For hybrid power train the general approach to simulated a drive cycle, that usually last at least a few minutes, is based on engine map approach [1–2]. The main burden to the described process is the identifications of maps of torque and consumption for the internal combustion engine, which are normally not predictable in detail, nor are provided by the manufacturers, but they can only be determined by means of experimental tests. Such a process can become extremely expensive and time consuming. Hence in this work the concept of virtual optimisation is introduced basing on the identification of torque and fuel consumption maps for internal combustion engines on analytical methods considering the similarities with engine of the same class. In this regard, a model of the system is developed based on the “Willans Line Method” approach, subsequently to a theoretical definition of the model, the identification of maps is carried out for two different engines (one diesel heavy-duty engine and one spark ignition engine) in order to consider the existing configurations of hybrid vehicles. Eventually the calculated maps are validated considering experimental data from existing experimental campaign. Providing the validity of the method and its usefulness in the hybrid vehicle design.

In the Northern areas the total thickness of structural layers in railway embankments is primarily governed by the design against harmful effect of seasonal frost. Because practically no frost heave can be allowed to take place on railway tracks with normal speed passenger traffic, the embankment must typically be built up to two or even two and a half meters thick. Meantime, the embankments have typically fairly steep slopes, for instance in Finland track embankments are normally built using a slope ratio of 1:1.5. Introduction of higher allowable axle loads and traffic speeds is, however, exposing the embankments structures to continuously increasing intensity of repeated loading which is also increasing the rate of permanent deformations accumulating into the embankment structure. In practical terms the embankment is widening as it deforms and the respective movements of the track must be compensated by more frequent maintenance actions. The most straightforward measures to increase the internal stability of a railway embankment are to make the embankment wider and/or to reduce the slope steepness of the embankment. Both of these actions mean, however, larger space requirement for the railway track and, above all, extensive increase in the use of high-quality non-frost-susceptible aggregate materials in connection with the embankment construction or renovation. Therefore, taking into account both the construction time costs on one hand and the maintenance costs of the track on the other hand, optimisation of the embankment dimensions and shape is an important issue regarding the life cycle costs of a railway line. In a research project going on at the Laboratory of Earth and Foundation Structures of the Tampere University of Technology the above mentioned problem is being studied by in-situ monitoring of a full scale railway track embankment having sections that are shaped in different embankment widths and slope angles. The long term deformations of the embankment have been monitored for about three years in addition to which also the short term responses of the embankment structure have been measured while trains are passing over the monitoring sections. In addition, model scale (1:4) test structures with different embankment widths and slope angles have been tested in laboratory using a loading system consisting of five hydraulic actuators operating consecutively so as to simulate the loading effect of a moving train. The results obtained so far indicate clearly that it is not only the embankment width and slope angle that are decisive concerning the permanent deformation behaviour of the railway embankment, but also the subgrade stiffness plays an important role in the overall performance of the embankment structure.