139 research outputs found
Computational design for micro fluidic devices using lattice boltzmann and heuristic optimisation algorithms
Get PDFKipouros, Timoleon - Associate SupervisorThe study on micro devices is gaining importance in various fields from
biological to engineering. The dimensions of these devices range from
millimetres (mm) to micrometres (μm) and they work within the laminar flow
regime due to their low Reynolds number. Although diffusivity dictates the
mixing in such conditions, this work is based on the simulation of two non
reacting iso-thermal and incompressible fluids for both streams, so the mixing is
governed only by turbulence. A numerical study using the Lattice Boltzmann
method (LBM) is carried out in order to examine the mixing in one configuration.
In the second part of the work an interface is developed between the LBM code
and multi objective optimisation software in order to investigate new
configurations which enhance the mixing. The objectives are to maximise the
vorticity and minimise the pressure drop, which are conflicting between
themselves. The tool that integrates the optimiser and the LBM code for
simulating microreactor, can run on a multi level parallelisation, hence the time
required for the whole simulation has been drastically reduced.
A preliminary optimisation is performed on a microreactor with multi holed baffle
plate. Despite small number of iterations, three totally different configurations
have been found: greatest vorticity, smallest pressure drop and a compromise.
The first two configurations satisfy the expectations, whereas the compromise
solution presents an innovative configuration. In fact, it has a low pressure drop
and a high vorticity, which is achievable by having high Reynolds number and
big diameters of the holes. Hence, this tool proves to be robust and efficient in
the multi objective optimisation of microreactor.MSc in Thermal Powe
Application of probabilistic set-based design exploration on the energy management of a hybrid-electric aircraft
Get PDFThe energy management strategy of a hybrid-electric aircraft is coupled with the design of the propulsion system itself. A new design space exploration methodology based on Set-Based Design is introduced to analyse the effects of different strategies on the fuel consumption, NOx and take-off mass. Probabilities are used to evaluate and discard areas of the design space not capable of satisfying the constraints and requirements, saving computational time corresponding to an average of 75%. The study is carried on a 50-seater regional turboprop with a parallel hybrid-electric architecture. The strategies are modelled as piecewise linear functions of the degree of hybridisation and are applied to different mission phases to explore how the strategy complexity and the number of hybridised segments can influence the behaviour of the system. The results indicate that the complexity of the parametrisation does not affect the trade-off between fuel consumption and NOx emissions. On the contrary, a significant trade-off is identified on which phases are hybridised. That is, the least fuel consumption is obtained only by hybridising the longest mission phase, while less NOx emissions are generated if more phases are hybridised. Finally, the maximum take-off mass was investigated as a parameter, and the impact to the trade-off between the objectives was analysed. Three energy management strategies were suggested from these findings, which achieved a reduction to the fuel consumption of up to 10% and a reduction to NOx emissions of up to 15%.European Union funding: 87555
Effects of turbulence modelling on the analysis and optimisation of high-lift configurations
Get PDFDue to the significant effects on the performance and competitiveness of aircraft, high lift devices are of extreme importance in aircraft design. The flow physics of high lift devices is so complex, that traditional one pass and multi-pass design approaches can’t reach the most optimised concept and multi-objective design optimisation (MDO) methods are increasingly explored in relation to this design task.
The accuracy of the optimisation, however, depends on the accuracy of the underlying Computational Fluid Dynamics (CFD) solver. The complexity of the flow around high-lift configuration, namely transition and separation effects leads to a substantial uncertainty associated with CFD results. Particularly, the uncertainty related to the turbulence modelling aspect of the CFD becomes important. Furthermore, employing full viscous flow solvers within MDO puts severe limitations on the density of computational meshes in order to achieve a computationally feasible solution, thereby adding to the uncertainty of the outcome. This thesis explores the effect of uncertainties in CFD modelling when detailed aerodynamic analysis is required in computational design of aircraft configurations. For the purposes of this work, we select the benchmark NLR7301 multi-element airfoil (main wing and flap). This flow around this airfoil features all challenges typical for the high-lift configurations, while at the same time there is a wealth of experimental and computational data available in the literature for this case.
A benchmark shape bi-objective optimization problem is formed, by trying to reveal the trade-off between lift and drag coefficients at near stall conditions. Following a detailed validation and grid convergence study, three widely used turbulence models are applied within Reynolds-Averaged Navier-Stokes (RANS) approach. K- Realizable, K- SST and Spalart-Allmaras. The results show that different turbulent models behave differently in the optimisation environment, and yield substantially different optimised shapes, while maintaining the overall optimisation trends (e.g. tendency to maximise camber for the increased lift). The differences between the models however exhibit systemic trends irrespective of the criteria for the selection of the target configuration in the Pareto front. A-posteriori error analysis is also conducted for a wide range of configurations of interest resulting from the optimisation process. Whereas Spalart-Allmaras exhibits best accuracy for the datum airfoil, the overall arrangement of the results obtained with different models in the (Lift, Drag) plane is consistent for all optimisation scenarios leading to increased confidence in the MDO/RANS CFD coupling
Multi-objective Tabu Search 2: first technical report
Get PDFThe purpose of this document is to describe Multi-Objective Tabu
Search 2 (MOTS2), which is a native mutli-objective optimiser. It
has been developed to tackle a variety of real-world problems of engineering
interest. The design and implementation are presented, followed
by verification, validation and user instructions. At a glance,
it involves introduction to the algorithm, explains configuration settings
and structure, and results interpretation. Then, the optimiser
is tested against a series of mathematical test functions in order to
verify its functionality. The main goal is to demonstrate and assess
the performance and applicability of the optimiser. The next step is to
use MOTS2 on a real-world case, where the performance of optimising
a 2D airfoil is validated and illustrated
Multi-objective shape optimisation of a Transonic Fan Rotor downstream of an S-duct
Get PDFThis master's degree thesis aims to optimize a transonic fan following an S-duct through the use of all the tools necessary for an optimization process. In order to perform the required analysis, an automatic CFD based optimization circuit built around GA was built. Specifically, a dedicated parameterization framework was created for 3D blades. In the results it can be seen how the optimization of NASA Rotor 67 gives better results than expected, despite the flow problems created by the S-ductopenEmbargo temporaneo per motivi di priorità nella ricerca previo accordo con terze part
Modelling and aerodynamic design of optimisation of the twin-boom aegis UAV.
Get PDFThe aircraft industry gives considerable attention to computational optimisation tools in order to enhance the design process and product quality in terms of efficiency and performance, respectively. In reality, most real-world applications contain many complicating factors and constraints that affect system behaviour. Consequently, finding optimal solutions, or even only those viable for a given design problem, in an economical computational time is a difficult task, even with the availability of superfast computers. Thus, it is important to optimise the use of available computational resources.
This research project presents a method for using stochastic multi-objective optimisation approaches combined with Artificial Intelligence and Interactive Design techniques to support the decision-making process. The improved ability of the developed methods to accelerate the search while retaining all the useful information in the design space was the main area of work. Both the efficiency and reliability of the proposed methodology have been demonstrated through the aerodynamic design of the Aegis-UAV.
Initially, the optimisation platform Nimrod/O was deployed to enable the designer to manipulate and better understand different design scenarios. This happened before any commitment to a specific design architecture to allow for a wider exploration of the design space before a decision was made for a more detailed study of the problem. This had the potential to improve the quality of the product and reduce the design cycle time. The optimisation was performed using the Multi-Objective Tabu Search (MOTS) algorithm, chosen for its suitability for this type of complex aerodynamic design problem.
Prior to the optimisation process, a parametric study was performed using the Sweep Method (SM) to explore the design space and identify design limitations. Analysis and investigation of the SM results were used to help determine the formulation of the design problem. SM was chosen because it has been proven to be reliable, effective, and able to provide a large amount of structured information about the design problem to the decision maker (DM) at this stage.
Next, since most decisions of a DM in practical applications concern regions of the Pareto front, an interactive optimisation framework was proposed where the DM was involved with the optimisation process in real time. The framework used the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm for its suitability to this type of design problem. The results obtained confirmed the ability of the DM to use its preferences effectively, to steer the search to the Region of Interest (ROI) without degrading the aerodynamic performance of the optimised configurations. Even using only half the evaluations, the DM was able to obtain results similar to, or better than those obtained by the non-interactive use of MOTS and MOPSO. Furthermore, it was possible for the DM to stop the search at any iteration, which is not possible in non-interactive approaches even though the solutions do not converge or may be infeasible.
Finally an Artificial Neural Network (ANN) was introduced to guide the MOPSO algorithm in deciding whether the trial solution was worthy of full evaluation, or not. The results obtained showed the success of the ANN in recognising non-valid particles. Consequently, the solver avoided wasting computational efforts on non-worthwhile particles. The optimisation process provides particles that are more valid for almost the same computational time. Demonstrating the algorithm’s effectiveness was done by comparing results of the ANN-MOPSO solutions with those obtained by the other approaches for the same design problems.
In conclusion, future avenues of research have been identified and presented in the final chapter of the thesis.PhD in Aerospac
Robust Design Optimisation of S-Ducts under Uncertainties
Get PDFRobust optimisations have become very popular. The aim of this new type of optimisation is to consider the sensitivity of the output results to small variations in the operating conditions or in the manufacturing constraints. in this thesis, the main objective has been to extend the robust design optimisation for S-ducts with more uncertainties input and output. For the uncertainties quantification two different non-intrusive Polynomial Chaos techniques have been chosen: NIPC and NISP.ope
A set-based design space exploration framework for hybrid-electric aicraft design
No full textEngineering design is characterised by uncertainty caused by a lack of experience and
information. The traditional approach focuses on iterating and refining an initial conceptual
design, which often is similar to the final one. Although this method serves well in
the case of evolutionary design, it is unsuitable for innovation. In fact, without a suitable
initial starting point, many rework iterations may be required to correct early inadequate
design decisions. In addition, it may be challenging to map the requirements directly onto
the design space.
This dissertation aims at developing a methodology to address this problem. The
developed framework starts from the hypothesis, and the knowledge to carry out the mapping
of requirements onto the input parameters is embedded in the simulation model, and
hence no additional rules are required. Instead, a probabilistic surrogate model based on
Gaussian processes is used in conjunction with Bayesian statistics to find and eliminate
unfeasible areas of the design space. This selection criterion is used in a set-based design
approach to explore pockets of the entire continuous design space. Finally, sets with a
sufficient likelihood of satisfying the requirements are searched with a local multidisciplinary
optimisation algorithm to recover the individual design points.
This process reduced the computational cost of the design space exploration by 80%
without sacrificing the number of alternative solutions. Thanks to the large amount of
data obtained, it was possible to produce new knowledge on hybrid-electric aircraft design.
Specifically, it was found that linear segments are sufficient for defining energy
management strategies, and the reduction of NOx emissions and fuel consumption are associated
with climb and cruise, respectively. Furthermore, when studying regional aircraft
operating missions, it was found that partial recharge is necessary to maintain the design
performance. However, this could reduce the duration of the battery. The battery ageing
rate correlates with the EMS’s demand for electrical energy. Finally, it was found that
the battery’s energy density is a determinant of the pack’s durability and the feasibility of
HE aircraft. The rate of improvement in emissions and fuel consumption is non-linear,
suggesting that investing in considerable technological improvements has better returns.
Indeed, the required technological level will not be available until the 2040s without an
exponential increment of the cell energy density.PhD in Aerospac
Three-objective optimization studies of an S-duct
Get PDFIn this work the CFD analysis and the MOO are combined. In particular the MOO is focused on the optimisation of an S-duct intake while the CFD analysis is a study of the interaction between dierent s-duct, obtained by the MOO, with the rotor 67. The S-duct intake create inlet distorsions to the fan and many studies have been done to replicate these inlet distorsions. In literature total pressure and swirl distorsion are analised separatly but in reality the two distorsions coesist toge.ope
Support an S-duct optimization design study using state-of-the-art Machine Learning techniques
Get PDFManage the state-of-the-art method and tools in computational engineering design area, including stochastic optimisation, machine learning, computational fluid dynamics, and flexible geometry management algorithmsope
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