Comparison of design concepts in multi-criteria decision-making using level diagrams

[EN] In this work, we address the evaluation of design concepts and the analysis of multiple Pareto fronts in multi-criteria decision-making using level diagrams. Such analysis is relevant when two (or more) design concepts with different design alternatives lie in the same objective space, but describe different Pareto fronts. Therefore, the problem can be stated as a Pareto front comparison between two (or more) design concepts that only differ in their relative complexity, implementation issues, or the theory applied to solve the problem at hand. Such analysis will help the decision maker obtain a better insight of a conceptual solution and be able to decide if the use of a complex concept is justified instead of a simple concept. The approach is validated in a set of multi-criteria decision making benchmark problems. © 2012 Elsevier Inc. All rights reserved.This work was partially supported by the FPI-2010/19 Grant and Project PAID-06-11 from the Universitat Politecnica de Valencia and by Projects ENE2011-25900, TIN2011-28082 (Spanish Ministry of Science and Innovation) and GV/2012/073, PROMETEO/2012/028 (Generalitat Valenciana).Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Herrero Durá, JM. (2013). Comparison of design concepts in multi-criteria decision-making using level diagrams. INFORMATION SCIENCES. 221(1):124-141. https://doi.org/10.1016/j.ins.2012.09.049S124141221

Global plant characterisation and distribution with evolution and climate

Since Arrhenius published seminal work in 1921, research interest in the description of plant traits and grouped characteristics of plant species has grown, underpinning diversity in trophic levels. Geographic exploration and diversity studies prior to and after 1921 culminated in biological, chemical and computer-simulated approaches describing rudiments of growth patterns within dynamic conditions of Earth. This thesis has two parts:- classical theory and multidisciplinary fusion to give mathematical strength to characterising plant species in space and time.Individual plant species occurrences are used to obtain a Species-Area Relationship. The use of both Boolean and logic-based mathematics is then integrated to describe classical methods and propose fuzzy logic control to predict species ordination. Having demonstrated a lack of significance between species and area for data modelled in this thesis a logic based approach is taken. Mamdani and T-S-K fuzzy system stability is verified by application to individual plant occurrences, validated by a multiple interfaced data portal. Quantitative mathematical models are differentiated with a genetic programming approach, enabling visualisation of multi-objective dispersal of plant strategies, plant metabolism and life-forms within the water-energy dynamic of a fixed time-scale scenario. The distributions of plant characteristics are functionally enriched through the use of Gaussian process models. A generic framework of a Geographic Information System is used to visualise distributions and it is noted that such systems can be used to assist in design and implementation of policies. The study has made use of field based data and the application of mathematic methods is shown to be appropriate and generative in the description of characteristics of plant species, with the aim of application of plant strategies, life-forms and photosynthetic types to a global framework. Novel application of fuzzy logic and related mathematic method to plant distribution and characteristics has been shown on a global scale. Quantification of the uncertainty gives novel insight through consequent trophic levels of biological systems, with great relevance to mathematic and geographic subject development. Informative value of Z matrices of plant distribution is increased substantiating sustainability and conservation policy value to ecosystems and human populations dependent upon them for their needs.Key words: sustainability, conservation policy, Boolean and logic-based, fuzzy logic, genetic programming, multi-objective dispersal, strategies, metabolism, life-forms

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Bandwidth Based Methodology for Designing a Hybrid Energy Storage System for a Series Hybrid Electric Vehicle with Limited All Electric Mode

The cost and fuel economy of hybrid electrical vehicles (HEVs) are significantly dependent on the power-train energy storage system (ESS). A series HEV with a minimal all-electric mode (AEM) permits minimizing the size and cost of the ESS. This manuscript, pursuing the minimal size tactic, introduces a bandwidth based methodology for designing an efficient ESS. First, for a mid-size reference vehicle, a parametric study is carried out over various minimal-size ESSs, both hybrid (HESS) and non-hybrid (ESS), for finding the highest fuel economy. The results show that a specific type of high power battery with 4.5 kWh capacity can be selected as the winning candidate to study for further minimization. In a second study, following the twin goals of maximizing Fuel Economy (FE) and improving consumer acceptance, a sports car class Series-HEV (SHEV) was considered as a potential application which requires even more ESS minimization. The challenge with this vehicle is to reduce the ESS size compared to 4.5 kWh, because the available space allocation is only one fourth of the allowed battery size in the mid-size study by volume. Therefore, an advanced bandwidth-based controller is developed that allows a hybridized Subaru BRZ model to be realized with a light ESS. The result allows a SHEV to be realized with 1.13 kWh ESS capacity. In a third study, the objective is to find optimum SHEV designs with minimal AEM assumption which cover the design space between the fuel economies in the mid-size car study and the sports car study. Maximizing FE while minimizing ESS cost is more aligned with customer acceptance in the current state of market. The techniques applied to manage the power flow between energy sources of the power-train significantly affect the results of this optimization. A Pareto Frontier, including ESS cost and FE, for a SHEV with limited AEM, is introduced using an advanced bandwidth-based control strategy teamed up with duty ratio control. This controller allows the series hybrid’s advantage of tightly managing engine efficiency to be extended to lighter ESS, as compared to the size of the ESS in available products in the market

Application of Multidisciplinary Design Optimisation to Engine Calibration Optimisation.

Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively.Jaguar Land Rove

An Evolutionary Multi-Objective Optimization Framework for Bi-level Problems

Genetic algorithms (GA) are stochastic optimization methods inspired by the evolutionist theory on the origin of species and natural selection. They are able to achieve good exploration of the solution space and accurate convergence toward the global optimal solution. GAs are highly modular and easily adaptable to specific real-world problems which makes them one of the most efficient available numerical optimization methods. This work presents an optimization framework based on the Multi-Objective Genetic Algorithm for Structured Inputs (MOGASI) which combines modules and operators with specialized routines aimed at achieving enhanced performance on specific types of problems. MOGASI has dedicated methods for handling various types of data structures present in an optimization problem as well as a pre-processing phase aimed at restricting the problem domain and reducing problem complexity. It has been extensively tested against a set of benchmarks well-known in literature and compared to a selection of state-of-the-art GAs. Furthermore, the algorithm framework was extended and adapted to be applied to Bi-level Programming Problems (BPP). These are hierarchical optimization problems where the optimal solution of the bottom-level constitutes part of the top-level constraints. One of the most promising methods for handling BPPs with metaheuristics is the so-called "nested" approach. A framework extension is performed to support this kind of approach. This strategy and its effectiveness are shown on two real-world BPPs, both falling in the category of pricing problems. The first application is the Network Pricing Problem (NPP) that concerns the setting of road network tolls by an authority that tries to maximize its profit whereas users traveling on the network try to minimize their costs. A set of instances is generated to compare the optimization results of an exact solver with the MOGASI bi-level nested approach and identify the problem sizes where the latter performs best. The second application is the Peak-load Pricing (PLP) Problem. The PLP problem is aimed at investigating the possibilities for mitigating European air traffic congestion. The PLP problem is reformulated as a multi-objective BPP and solved with the MOGASI nested approach. The target is to modulate charges imposed on airspace users so as to redistribute air traffic at the European level. A large scale instance based on real air traffic data on the entire European airspace is solved. Results show that significant improvements in traffic distribution in terms of both schedule displacement and air space sector load can be achieved through this simple, en-route charge modulation scheme

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Modeling of Turbulence, Combustion and Knock for Performance Prediction, Calibration and Design of a Turbocharged Spark Ignition Engine

In this thesis work, a downsized VVA Spark Ignition engine is numerically and experimentally studied. In particular, the following topics are considered: •In-cylinder turbulence and combustion processes; •Knock and cycle by cycle variation (CCV) phenomena; •Techniques aiming to mitigate knock occurrence and improve fuel economy such as EGR and water injection methods; •Intake system redesign to reduce the emitted gas-dynamic noise; •Engine calibration. A deep experimental campaign is carried out to characterize the engine behaviour. Indeed, engine system is investigated both in terms of the overall performance (torque, power, fuel consumption, air flow rate, boost pressure etc.) and of the intake gas-dynamic noise at full load operation. In addition, proper experimental analyses are peformed on the engine to characterize the CCV phenomenon and the knock occurrence. Measured data are post-processed to derive experimental parameters which syntetize CCV and knock levels, according to the engine operating conditions. A 1D CFD model of the whole engine is realized in GT-PowerTM environment. Refined “in-house developed” sub-models capable to reproduce turbulence, combustion, CCVs and knock processes are introduced into 1D code through user routines. First of all, the whole engine model is validated against the experimental data both in terms of overall performance parameters and ensemble averaged pressure cycles and intake gas-dynamic noise at part and full load operation. Cycle by cycle variation is reproduced through a proper correlation and consequently a representative faster than average in-cylinder pressure cycle is obtained. Then, the knock model, with reference to the latter pressure cycle, allows to evaluate a proper knock index and to identify the knock limited spark advance (KLSA), basing on the same threshold level adopted in experimental knock analysis. In this way, the knock model taking into account the CCV is validated at full load operation. Once validated, the original engine architecture is modified by virtually installing a “Low pressure” EGR system. 1D simulations accounting for various EGR rates and mixture leaning are performed at full load points, showing improvements in the fuel economy with the same knock intensity of the base engine configuration. Water injection technique is also investigated by virtually mounting a water injector in the intake runners for each engine cylinder. In a similar way, 1D analyses are carried out for various water/fuel and air-to-fuel ratios, highlightinig BSFC improvements at full load operation. Since the engine under study is characterized by higher intake gas-dynamic noise levels, a partial redesign of the intake system is properly identified and subsequently tested with 1D and 3D CFD simulations to numerically quantify the gains in terms of reduction in the gas-dynamic noise emitted at the intake mouth. Finally, a numerical methodology aiming to calibrate the considered engine at high load knock-limited and at part load operations is developed. First, it shows the capability to identify with satisfactory accuracy the experimentally advised engine calibration. In addition, it allows the comparison of different intake valve strategies, underlining, in certain engine operating conditions, the fuel consumption benefits of an early intake valve closure (EIVC) strategy with respect to a Full Lift one, due to a better combustion phasing and a reduced mixture over-fuelling. The developed automatic procedure presents the capability to realize a “virtual” engine calibration on completely theoretical basis and proves to be very helpful in reducing time and costs related to experimental activities at the test bench

Crashworthiness analysis and design optimization of hybrid energy absorption devices: application to aircraft structures

Programa Oficial de Doutoramento en Enxeñaría Civil . 5011V01[Abstract]Amid the main research lines for the enhancement of aircraft and automotive designs, structural optimization and crashworthiness studies are at their pinnacle. Means of transport need to be robust and safe, albeit efficiency and lightness cannot be neglected. While active safety systems have avoided innumerable accidents, passive crashworthiness systems need to protect passengers when they do occur. In the event of a crash, modern structures are designed to collapse progressively, dissipating high amounts of kinetic energy and protecting the passengers against abrupt decelerations. Within this broad field of study, the aim of this thesis is that of bettering traditional crash structures by designing and optimizing thin-walled hybrid energy absorbers, and ultimately proving reduced occupant injury levels during representative impact scenarios. The collapsible energy absorbers studied throughout this research originated by combining square metallic tubes with inner cores made from glass-fiber reinforced polymer (GFRP) and foam structures. Honeycombs are studied in depth, showing their outstanding behavior as load bearing structures and identifying the effects of modifying their cell’s shape. Another composite structure investigated was that of an intertwined four-plate star core, slightly less stiff than honeycombs but promising crushing behavior. Foam extrusions are also used as standalone reinforcements and as filling of the inner core’s voids, always enhancing the energy absorption capabilities of specimens. Specimens are characterized according to different crashworthiness metrics, including their energy absorption value, peak force undergone during its collapse and the mass of the components. Moreover, each initial design is subjected to optimization techniques to achieve the utmost from the aforementioned metrics. For that, finite element simulations of axial dynamic loading are parametrized as to obtain variable core heights, material thicknesses and modifiable honeycomb’s cell size and shape. These are later coupled with sampling and metamodeling algorithms, constructing a surrogate model which performs accordingly with the simulation during any fluctuation in the design variables. Later on, the metamodels are single- and multi-objectively optimized with genetic algorithms, yielding various sets of designs that excel in one or more of the selected responses. As a second goals of this work, the previous energy absorber design and the methodology used are to be applied in a significant impact scenario of a passenger vehicle. A drop-test numerical simulation from a Boeing 737-200 fuselage section is developed and correlated with extensive experimental data, later analyzing the crushing behavior of isolated components and their energy absorption trends. The effect of adding hollow thin-walled tubes as vertical struts is studied, expecting a great enhancement of the conventional design response. Surrogate-based optimization methodologies are also applied to this simulation, monitoring various crashworthiness biometrics and the specimen’s mass. Results show that on a coupon basis, the usage of inner reinforcements can modify the tube’s collapse patterns and increase its specific energy absorption values by up to 30 %, mainly caused by the interaction between the core and the confining structure. Moreover, reducing the core’s height has also shown improved responses, offsetting the triggering loads of each component and yielding peak force values 33 % lower. Topographic optimization of honeycomb cells has revealed that the highest specific energy absorption values for dynamic loads are not achieved with a regular cell but with a pseudo-rectangular one. The usage of foam as cell-filling has also proved superb, increasing energy absorption by another 28 % with minor hindering on the specimen’s mass. As for the validation of the full size aircraft drop-test simulation, numerical and graphical results closely match those of the experimental procedure. It was found that removing the auxiliary fuel tank from the original section increased occupant injury levels due to high structural deformation and low energy absorption by the main structures. In a later phase, hybrid energy absorbers are added to the fuselage section with an empty cargo area, and a new surrogate model is built with 600 full-scale drop test simulation. The surrogate is then single- and multi-objectively optimized, reducing acceleration peak values by 50 % and injury levels from severe to moderate at different occupant locations

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management