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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.

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.

Goal of this dissertation's thesis is to familiarize the reader with the issues connected with automatic optimization and its implementation in desired company, as a substitute to its current solution. The main objective of this thesis is therefore considered to be consideration and evaluation of benefits software solution can bring to HOPI, with full meeting of its requirements using its resources. In theoretical part of thesis will look at all the necessary information the reader will need to fully understand the theory, as well as the issueo of creating and optimizing great number of routes with a large fleet. Company HOPI will be introduced, as well as all software solutions, which were used in this work, or are mentioned with references. Practical part will describe the process of planning in both version, actual and software one. Form gained information and consultations with management of HOPI, conclusions will be drawn about benefits and difficulties of each solution. Ideal procedure for consideration will be drawn and verdict about software solution will be reached. In conclusion, thesis will summarize all gained knowledge, which will lead to final assessment and it will establish the best options of solutions for HOPI.

Student Number : 9910899R - MSc dissertation - School of Mining - Faculty of Engineering and the Built Environment / The field of risk management has been growing in popularity over the last few years. Risk management is not a new concept but is becoming more important since the release of the Turnbull report. This research reviews all the risk management systems currently available in the mining industry. The focus of this research is from a Mining Economics as well as a Minerals Resource Management perspective. It is the Mineral Resource Managers primary task to ensure that the orebody is extracted in the most optimum method to ensure the maximum return for the shareholder. In order to do that, the Resource Manager needs a good understanding of the ore body as well as the extraction methods and the cost of mining. Recently it has become important to understand the risks around the mining process as well. It was found that the principal risk associated with mining is extracting the orebody sub economically and hence the research focus was on optimisation. Three tools have been designed to facilitate the determination of optimisation. The above three tools have been tested in practice. The first section of research focuses on how risk is defined in the industry. There is also an analysis what a Mining Economist and A Mineral Resource Manager will encounter in terms of risk. The second section covers the Basic Mining Equation (BME) and its uses. The research looks at using stochastic methods to improve optimisation and identifying risk. The @Risk software was used to analyse 5 years of historical data from an existing mine and predicting the future, using the distributions identified in the history. The third section is based on the use of the Cigarette Box Optimiser (CBO), where the cost volume curve and the orebody signature are used to determine optimality in returns. It also looks at various forms of the BME and how it can be used to identify risk. The section also covers quantification of risk from a probability perspective using systems reliability logic. The fourth section centres on the Macro Grid Optimiser (MGO), which considers the spatial differentiation of the orebody and determining the optimality through, an iterative process. The last section analyses risk from a Mining Economics perspective. It considers the use of the ‘S-curve’ to determine risk. The section also includes a high-level shaft infrastructure optimisation exercise. As an overall conclusion, it was found that the biggest risk associated with mining could be to extract the orebody sub economically. Some ore bodies could yield double the return that they intend to extract. In order for that to happen, the extraction program should undergo some form of optimisation. This will ensure that the optimal volume, cut-off, selectivity and efficiencies are met. There is no greater risk than to mine an ore body out without making an optimal profit. We are in mining to make money! Cash is king!

Thesis (MSc)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: This work focussed on a local multiple chamber falling film evaporator (FFE). The FFE is currently under operator control and experiencing large amounts of lost production time due to excessive fouling. Furthermore, the product milk dry mass fraction (WP) is constantly off specification, negatively influencing product quality, while the first effect temperature (TE1) runs higher than the recommended 70°C (this is a main cause of fouling). A two month period of historical data were received with the aim to develop a controller that could outperform the operators by keeping both control variables, WP and TE1, at desired set points while also increasing throughput and maintaining product quality. Access to the local plant was not possible and as such available process data were cleaned and used to identify two data based models, transfer function and autoregressive with exogenous inputs (ARX) models, as well as a semi-empirical-model. The ARX model proved inadequate to predict TE1 trends, with an average TE1 correlation to historical data of 0.36, compared to 0.59 and 0.74 for the transfer function and semi-empirical-models respectively. Product dry mass correlations were similar between the models with the average correlations of 0.47, 0.53 and 0.51 for the semi-empirical, transfer function and ARX models respectively. Although the semi-empirical showed the lowest WP correlation, it was offset by the TE1 prediction advantage. Therefore, the semi-empirical model was selected for controller development and comparisons. The success of the semi-empirical model was in accordance with previous research [1] [2] [3], yet other studies have concluded that ARX modelling was more suited to FFE modelling [4]. Three controllers were developed, namely: a proportional and integral (PI) controller as base case, a linear quadratic regulator (LQR) as an optimal state space alternative and finally, to make full use of process knowledge, a predictive fuzzy logic controller (PFC). The PI controller was able to offer zero offset set point tracking, but could not adequately reject a feed dry mass (WF) disturbance (as proposed and reported by Winchester [5]). The LQR was combined with a Kalman estimator and used pre-delay states. In order to offer increased disturbance rejection, the feedback gains of the disturbance states were tuned individually. The altered LQR and PFC solutions proved to adequately reject all modelled disturbances and outperform a cascade controller designed by Bakker [6]. The maximum deviation in WP was a fractional increase of 0.007 for LQR and 0.005 for FPC, compared to 0.012 for PI and 0.0075 for the cascade controller [6] (WF disturbance fractional increase of 0.01). All the designed controllers managed to reduce the standard deviation of operator controlled WP and TE1 by at least 700% and 450%, respectively. The same level of reduction was seen for maximum control variable deviations (370%), the integral of the absolute error (300%) and the mean squared error (900%). All these performance metrics point to the controllers performing better than the operator based control. In order to prevent manipulated variable saturation and optimise the feed flow rate (F1), a fuzzy feed optimiser (FFO) was developed. The FFO focussed on maximising the available evaporative capacity of the FFE by optimising the motive steam pressure (PS), which supplied heat to the effects. By using the FFO for each controller the average feed flow rate was increased by 4.8% (±500kg/h) compared to the operator control. In addition to flow rate gain, the controllers kept TE1 below 70°C and WP on specification. As such, the overall product quality also increased as well as decreasing the down time due to less fouling. / AFRIKAANSE OPSOMMING: Hierdie projek het op ‘n vallende film verdamper (VFV) gefokus. Die VFV word tans beheer deur operateurs en ondervind groot hoeveelhede verlore produksie tyd a.g.v oormatige aangroeisels. Die vorming van aangroeisels is grootliks te danke aan die eerste effek temperatuur (TE1) wat gereeld 70°C oorskrei. Die produk droë massa fraksie (WP) is ook telkens nie op die gewenste vlak nie, wat produk kwaliteit negatief beinvloed. Data, wat oor ‘n twee maand periode strek, was verkry met die doelstelling om ‘n beheerder te ontwerp wat beter sou vaar as die operateurs, deur beide WP en TE2 om ‘n nou stelpunt te beheer. Ter selfde tyd moet die ontwerpte beheerder die produksie tempo en produk kwaliteit verhoog. Geen toegang tot die plaaslikke VFV was moontlik nie, dus was die data skoongemaak en gebruik om twee data gebasseerde modelle te identifiseer, nl. oordragsfunksie en outoregressiwe met eksogene insette (ORX) modelle, asook ‘n semi-empiriese model. Die ORX model kon nie TE1 goed voorspel nie, met ‘n korrelasie faktor (tot die historiese data) van 0.36, vergeleke met die 0.59 en 0.74 van die oordragsfunksie en semi-empiriese modelle onderskeidelik. WP korrelasie faktore was meer konstant tussen die modelle, met waardes van 0.47, 0.53 en 0.51 vir die semi-empiriese, oordragsfunskie en ORX modelle onderskeidelik. Alhoewel die semi-empiriese model die laagste WP korrelasie vertoon het, was die tekortkoming vergoed deur die beter TE1 voorspelling. Gevolglik was die semi-empiriese model gebruik vir beheerder ontwerp en vergelyking. Die sukses van die semiempiriese model stem ooreen met vorige studies [1] [2] [3], tog het ander studies al bevind dat die ORX model beter gepas is vir die VFV proses [4]. Drie beheerders was ontwikkel, nl. ‘n proporsionele en integreerder (PI) beheerder as basis geval, ‘n liniêre kwadratiese reguleerder (LKR) as optimale toestands beheer alternatief en laastens ‘n voorspellende wasige logika beheerder (VWB) om volle gebruik van proseskennis te maak. Die PI beheerder kon foutlose volging van die stelpunte lewer, maar kon nie ‘n inset voer droë massa fraksie (WF) versteuring (soos voorgestel en weergegee deur Winchester [5]) na wense verwerp nie. Die LKR was saamgevoeg met ‘n Kalman afskatter en het gebruik gemaak van onvertraagde toestande. Die versteuringstoestande was individueel verstel om beter versteurings verweping te weeg te bring. Die aangepaste LKR en VWB kon beide die WF versteuring verwerp en het beter gevaar as ‘n kaskade beheer oplossing wat deur Bakker [6] ontwerp was. Die WP afwyking is beperk tot ‘n fraksie droë masse verandering van 0.007 vir LKR en 0.005 vir VWB, vergeleke met die afwykings van 0.012 vir die PI beheerder asook die 0.0075 van die kaskade beheerder [6]. Die ontwerpte beheerder kon ook die standaard afwyking van beide WP en TE1 met ten minste 700% en 450% onderskeidelik verminder. Soortgelyke verbeterings was gesien vir die maksimum beheer veranderlikke afwyking (370%), die integraal van die absolute fout (300%) en die gemiddelde fout (900%). Dus het die ontwerpte beheerders wesenlik verbeter op die operateur beheer. Ten einde om gemanipuleerde veranderlikke versadiging te voorkom, asook die voer vloei (V1) te optimiseer, was ‘n wasige logika optimiseerder (WVO) ontwerp. Die WVO het die beskikbare verdampingskapasiteit ten volle benut deur te sorg dat die stoom druk (PS), wat energie verskaf vir verdamping, ge-optimiseerd bly. ‘n Gemiddelde V1 stygging van 4.8% (±500kg/uur), vergeleke met operateur beheer, is waargeneem. Al die beheerders kon steeds die WP en TE1 stelpunte volg en dus TE1 onder 70°C hou (wat verminderde vormasie van aangroeisels tot gevolg gehad het). Daarom het die produk kwailiteit verhoog en die verlore produksie tyd verminder.

RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule structures. RNA is a nucleic acid found in living organisms which fulfils a number of important roles in living cells. Knowledge of its structure is crucial in the understanding of its function. Determining RNA secondary structure computationally, rather than by physical means, has the advantage of being a quicker and cheaper method. This dissertation introduces a new Set-based Particle Swarm Optimisation algorithm, known as SetPSO for short, to optimise the structure of an RNA molecule, using an advanced thermodynamic model. Structure prediction is modelled as an energy minimisation problem. Particle swarm optimisation is a simple but effective stochastic optimisation technique developed by Kennedy and Eberhart. This simple technique was adapted to work with variable length particles which consist of a set of elements rather than a vector of real numbers. The effectiveness of this structure prediction approach was compared to that of a dynamic programming algorithm called mfold. It was found that SetPSO can be used as a combinatorial optimisation technique which can be applied to the problem of RNA secondary structure prediction. This research also included an investigation into the behaviour of the new SetPSO optimisation algorithm. Further study needs to be conducted to evaluate the performance of SetPSO on different combinatorial and set-based optimisation problems. / Dissertation (MS)--University of Pretoria, 2009. / Computer Science / unrestricted

After the historic chess match between Deep Blue and Garry Kasparov, many researchers considered the game of chess solved and moved on to the more complex game of soccer. Artificial intelligence research has shifted focus to creating artificial players capable of mimicking the task of playing soccer. A new training algorithm is presented in this thesis for training teams of players from zero knowledge, evaluated on a simplified version of the game of soccer. The new algorithm makes use of the charged particle swarm optimiser as a neural network trainer in a coevolutionary training environment. To counter the lack of domain information a new relative fitness measure based on the FIFA league-ranking system was developed. The function provides a granular relative performance measure for competitive training. Gameplay strategies that resulted from the trained players are evaluated. It was found that the algorithm successfully trains teams of agents to play in a cooperative manner. Techniques developed in this study may also be widely applied to various other artificial intelligence fields. / Dissertation (MSc)--University of Pretoria, 2013. / Computer Science / unrestricted