To develop a computer tool that will give the best ship design using an optimization technique is one of the objects of the FIRST project. Choosing a containership design as a test case, the Design Optimization Tools (DOT) package is used as the optimization tool. The problem is tackled from the ship owner's point of view. The required freight rate is chosen as the objective function because the most important thing that concerns the ship owner is whether the ship will make a profit or not, and if so, how much profit it can make. DOT, as well as any other numerical optimization tool, only gives an approximation of the optimum design and uses numerical approximation during the optimization. It is very important for the users to formulate carefully the optimization problem so that it will give a stable and reasonable solution. Development of a geometric module and choosing suitable empirical formulas for performance evaluation are also major issues of the project. / Master of Science

Traditionally, the "Design Spiral" is used for the design of ships. The design spiral endorses the concept that the design process is sequential and iterative. Though this procedure was very effective over the years, the current trend of engineering demands that more stress be put on the exploration of optimum design. With the advancement of computing technologies, the onus has shifted from finding better calculation schemes to formulating an economically viable design scheme. One of the objects of the FIRST project funded by MARITECH was to develop a computer tool to give the best ship design using optimization techniques. This was entrusted to the Department of Aerospace and Ocean Engineering at Virginia Polytechnic Institute and State University in Blacksburg, Virginia. A container ship was chosen as the test case. The problem was tackled from an owner's point of view. Hence, the required freight rate was chosen as the objective. To achieve that goal, the team developed a package that consists of three modules: optimization, geometric and a performance evaluation module. Though these modules are essentially independent, the user has control over an overall manager. He can change the initial value of design parameters, set bounds and vary constraint bounds as per his needs. Though he does not know what goes on behind the user interface, he still feels secure with the design process because he has overall control. This sense of security breaks down when he has access to limited variables and constraints. A prototype MDO tool is developed based on Microsoft's COM framework using ATL. With this design, the modules can be modified with minimum programming effort. The user interface gives the user flexibility to manipulate relevant parameters that affect the design. A geometric shape manipulation scheme is developed in which the hull form was generated by blending two hull forms. This MDO tool is used to design a container ship with the required freight rate as the objective to be minimized. It is noticed that without a structural constraint, the design tends towards one with maximum length and beam. This led to unreasonably large ratios of B/D and L/D. A B/D constraint is applied to the design to get a better structural design. Results with this constraint enabled have pointed in the direction of adding two other design variables. This constraint increases the depth of the ship. With the increase in depth, the center of gravity of the ship also rises decreasing the GM of the ship. This lowering of GM adversely affects the GM constraint. The number of tiers on deck (NTd) is made a design variable to enable the optimizer to have the flexibility of manipulating the cargo carrying capacity. It was noticed that the ship is unable to have a high NTd because of the violation of the GM constraint. Hence, ballast has also been added as a design variable to reduce the center of gravity of the ship increasing the GM of the ship. This feature enables the optimizer to carry greater cargo on deck improving the objective function. An effort is made to analyze the efficacy of the MDO tool by varying various parameters that affect the design. Technology factors have been introduced which give an insight on effect of key parameters. They also reflect on future design trends. Three evaluation tools: sensitivity analysis, alpha plots and restart option have been incorporated in the design process to gauge the results of optimization. The effect of another structural constraint L/D was also investigated. This constraint tends to bring down the overall length and is inconclusive in its results. Further analysis of this constraint is needed to draw usable conclusions. The linear response surface approximation was eliminated and the original stepwise discontinuous TEU capacity function is employed in the later examples. It was found that the minimum of the required fright rate occurred at the lower limits of length and beam on each TEU capacity platform. A systematic search of TEU plateaus in the vicinity of the primary optimum was necessary to define the secondary optimum / Master of Science

No description available.

In this work several decomposition strategies for the synthesis / design optimization of highly coupled, highly dynamic energy systems are formally presented and their implementation illustrated. The methods are based on the autonomous optimization of the individual units (components, sub-systems or disciplines), while maintaining energy and cost links between all units, which make up the overall system. All of the approaches are designed to enhance current engineering synthesis / design practices in that: they support the analysis of systems and optimization in a modular way, the results at every step are feasible and constitute an improvement over the initial design state, the groups in charge of the different unit designs are allowed to work concurrently, and permit any level of complexity as to the modeling and optimization of the units. All of the decomposition methods use the Optimum Response Surface (ORS) of the problem as a basis for analysis. The ORS is a representation of the optimum objective function for various values of the functions that couple the system units1. The complete ORS or an approximation thereof can be used in ways, which lead to different methods. The first decomposition method called the Local Global Optimization (LGO) method requires the creation of the entire ORS by carrying out multiple unit optimizations for various combinations of values of the coupling functions. The creation of the ORS is followed by a system-level optimization in which the best combination of values for the coupling functions is sought The second decomposition method is called the Iterative Local Global Optimization (ILGO) scheme. In the ILGO method an initial point on the ORS is found, i.e. the unit optimizations are performed for initial arbitrary values of the coupling functions. A linear approximation of the ORS about that initial point is then used to guide the selection of new values for the coupling functions that guarantee an improvement upon the initial design. The process is repeated until no further improvement is achieved. The mathematical properties of the methods depend on the convexity of the ORS, which in turn is affected by the choice of thermodynamic properties used to charecterize the couplings. Examples in the aircraft industry are used to illustrate the application and properties of the methods. / Ph. D.

A method to efficiently introduce supersonic drag predictions from computational fluid dynamics (CFD) calculations in a combined aerodynamic-structural optimization of a High-Speed Civil Transport (HSCT) is presented. To achieve this goal, the method must alleviate the large computational burden associated with performing CFD analyses and reduce the numerical noise present in the analyses. This is accomplished through the use of response surface (RS) methodologies, a variation of the variable-complexity modeling (VCM) technique, and coarse grained parallel computing. Variable-complexity modeling allows one to take advantage of the information gained from inexpensive lower fidelity models while maintaining the accuracy of the more expensive high fidelity methods. The utility of the method is demonstrated on HSCT design problems of five, ten, fifteen, and twenty design variables. Motivation for including CFD predictions into the HSCT optimization comes from studies detailing the differences in supersonic aerodynamic predictions from linear theory, Euler, and parabolized Navier-Stokes (PNS) calculations for HSCT configurations. The effects of these differences in integrated forces and distributed loads on the aircraft performance and structural weight are investigated. These studies indicate that CFD drag solutions are required for accurate HSCT performance and weight estimates. Response surface models are also used to provide useful information to the designer with minimal computational effort. Investigations into design trade-offs and sensitivities to certain design variables, available at the cost of evaluating a simple quadratic polynomial, are presented. In addition, a novel and effective approach to visualizing high dimensional, highly constrained design spaces is enabled through the use of RS models. <i>NOTE: An updated copy of this ETD was added in July 2012 after there were patron reports of problems with the original file.</i> / Ph. D.

Research into the improvement of the Aircraft ConceptualDesign process by the application of MultidisciplinaryOptimization (MDO) is presented. Aircraft conceptual designanalysis codes were incorporated into a variety of optimizationmethods including Orthogonal Steepest Descent (full-factorialstepping search), Monte Carlo, a mutation-based EvolutionaryAlgorithm, and three variants of the Genetic Algorithm withnumerous options. These were compared in the optimization offour notional aircraft concepts, namely an advanced multiroleexport fighter, a commercial airliner, a flying-wing UAV, and ageneral aviation twin of novel asymmetric configuration. Tobetter stress the methods, the commercial airliner design wasdeliberately modified for certain case runs to reflect a verypoor initial choice of design parameters including wingloading, sweep, and aspect ratio. MDO methods were evaluated in terms of their ability to findthe optimal aircraft, as well as total execution time,convergence history, tendencies to get caught in a localoptimum, sensitivity to the actual problem posed, and overallease of programming and operation. In all, more than a millionparametric variations of these aircraft designs were definedand analyzed in the course of this research. Following this assessment of the optimization methods, theywere used to study the issue of how the computer optimizationroutine modifies the aircraft geometric inputs to the analysismodules as the design is parametrically changed. Since thiswill ultimately drive the final result obtained, this subjectdeserves serious attention. To investigate this subject,procedures for automated redesign which are suitable foraircraft conceptual design MDO were postulated, programmed, andevaluated as to their impact on optimization results for thesample aircraft and on the realism of the computer-defined"optimum" aircraft. (These are sometimes called vehicle scalinglaws, but should not be confused with aircraft sizing, alsocalled scaling in some circles.) This study produced several key results with application toboth Aircraft Conceptual Design and MultidisciplinaryOptimization, namely:     MDO techniques truly can improve the weight and cost ofan aircraft design concept in the conceptual design phase.This is accomplished by a relatively small "tweaking" of thekey design variables, and with no additional downstreamcosts.In effect, we get a better airplane for free.     For a smaller number of variables (&lt;6-8), adeterministic searching method (here represented by thefull-factorial Orthogonal Steepest Descent) provides aslightly better final result with about the same number ofcase evaluations     For more variables, evolutionary/genetic methods getclose to the best final result with far-fewer caseevaluations. The eight variables studied herein probablyrepresent the practical upper limit on deterministicsearching methods with today’s computer speeds.     Of the evolutionary methods studied herein, the BreederPool approach (which was devised during this research andappears to be new) seems to provide convergence in the fewestnumber ofcase evaluations, and yields results very close tothe deterministic best result. However, all of the methodsstudied produced similar results and any of them is asuitable candidate for use.     Hybrid methods, with a stochastic initial optimizationfollowed by a deterministic final "fine tuning", proved lessdesirable than anticipated.     Not a single case was observed, in over a hundred caseruns totaling over a million parametric design evaluations,of a method returning a local rather than global optimum.Even the modified commercial airliner, with poorly selectedinitial design variables far away from the global solution,was easily "fixed" by all the MDO methods studied.     The postulated set of automated redesign procedures andgeometric constraints provide a more-realistic final result,preventing attainment of an unrealistic "better" finalresult. Especially useful is a new approach defined herein,Net Design Volume, which can prevent unrealisticallyhigh design densities with relatively little setup andcomputational overhead. Further work in this area issuggested, especially in the unexplored area of automatedredesign procedures for discrete variables.

<p>Research into the improvement of the Aircraft ConceptualDesign process by the application of MultidisciplinaryOptimization (MDO) is presented. Aircraft conceptual designanalysis codes were incorporated into a variety of optimizationmethods including Orthogonal Steepest Descent (full-factorialstepping search), Monte Carlo, a mutation-based EvolutionaryAlgorithm, and three variants of the Genetic Algorithm withnumerous options. These were compared in the optimization offour notional aircraft concepts, namely an advanced multiroleexport fighter, a commercial airliner, a flying-wing UAV, and ageneral aviation twin of novel asymmetric configuration. Tobetter stress the methods, the commercial airliner design wasdeliberately modified for certain case runs to reflect a verypoor initial choice of design parameters including wingloading, sweep, and aspect ratio.</p><p>MDO methods were evaluated in terms of their ability to findthe optimal aircraft, as well as total execution time,convergence history, tendencies to get caught in a localoptimum, sensitivity to the actual problem posed, and overallease of programming and operation. In all, more than a millionparametric variations of these aircraft designs were definedand analyzed in the course of this research.</p><p>Following this assessment of the optimization methods, theywere used to study the issue of how the computer optimizationroutine modifies the aircraft geometric inputs to the analysismodules as the design is parametrically changed. Since thiswill ultimately drive the final result obtained, this subjectdeserves serious attention. To investigate this subject,procedures for automated redesign which are suitable foraircraft conceptual design MDO were postulated, programmed, andevaluated as to their impact on optimization results for thesample aircraft and on the realism of the computer-defined"optimum" aircraft. (These are sometimes called vehicle scalinglaws, but should not be confused with aircraft sizing, alsocalled scaling in some circles.)</p><p>This study produced several key results with application toboth Aircraft Conceptual Design and MultidisciplinaryOptimization, namely:</p><p>    MDO techniques truly can improve the weight and cost ofan aircraft design concept in the conceptual design phase.This is accomplished by a relatively small "tweaking" of thekey design variables, and with no additional downstreamcosts.<i>In effect, we get a better airplane for free.</i></p><p>    For a smaller number of variables (<6-8), adeterministic searching method (here represented by thefull-factorial Orthogonal Steepest Descent) provides aslightly better final result with about the same number ofcase evaluations</p><p>    For more variables, evolutionary/genetic methods getclose to the best final result with far-fewer caseevaluations. The eight variables studied herein probablyrepresent the practical upper limit on deterministicsearching methods with today’s computer speeds.</p><p>    Of the evolutionary methods studied herein, the BreederPool approach (which was devised during this research andappears to be new) seems to provide convergence in the fewestnumber ofcase evaluations, and yields results very close tothe deterministic best result. However, all of the methodsstudied produced similar results and any of them is asuitable candidate for use.</p><p>    Hybrid methods, with a stochastic initial optimizationfollowed by a deterministic final "fine tuning", proved lessdesirable than anticipated.</p><p>    Not a single case was observed, in over a hundred caseruns totaling over a million parametric design evaluations,of a method returning a local rather than global optimum.Even the modified commercial airliner, with poorly selectedinitial design variables far away from the global solution,was easily "fixed" by all the MDO methods studied.</p><p>    The postulated set of automated redesign procedures andgeometric constraints provide a more-realistic final result,preventing attainment of an unrealistic "better" finalresult. Especially useful is a new approach defined herein,<i>Net Design Volume</i>, which can prevent unrealisticallyhigh design densities with relatively little setup andcomputational overhead. Further work in this area issuggested, especially in the unexplored area of automatedredesign procedures for discrete variables.</p>

Der Vortrag zeigt die einzelnen notwendigen Schritte auf, die zur Lösung des Resonanzproblems an der Vorderradaufhängung eines E-Rollstuhls der Firma MEYRA-ORTOPEDIA notwendig waren. Alle Lösungsschritte wurden mit Creo Elements/Pro und seinen Modulen Mechanism Design Option (MDO) und Advanced Mechanica erarbeitet.

La réduction de la traînée aérodynamique des véhicules dans un objectif de diminution de la consommation énergétique est en plein essor aussi bien pour les véhicules électriques que thermiques. Cette étude porte sur un tricycle à motorisation hybride dont la forme et le comportement aérodynamique sont à la frontière entre une motocyclette et une automobile. L'étude s'inspire des avancées scientifiques sur ces deux types de véhicules en matière d'aérodynamique. L'objectif principal est de réduire la traînée aérodynamique du véhicule par des modifications de l'enveloppe externe tout en assurant le refroidissement du moteur thermique et des composants de la chaîne électrique. On développe une optimisation topologique de la position des échangeurs sur le tricycle, on conçoit et fabrique un prototype en fonction des résultats d'optimisation. Ensuite, on valide le prototype par des essais en soufflerie et on caractérise son aérodynamique ainsi que la sensibilité de la traînée du véhicule suivant des paramètres comme la vitesse, l'angle de lacet, etc. En n, l'étude s'oriente vers une approche d'optimisation globale multidisciplinaire permettant d'atteindre l'objectif principal en fonction des contraintes ayant trait au projet.

Master of Engineering (Research) / The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.