37 research outputs found
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On the effect of fluid-structure interactions and choice of algorithm in multi-physics topology optimisation
This article presents an optimisation framework for the compliance minimisation of structures subjected to design-dependent pressure loads. A finite element solver coupled to a Lattice Boltzmann method is employed, such that the effect of the fluid-structure interactions on the optimised design can be considered. It is noted that the main computational expense of the algorithm
is the Lattice Boltzmann method. Therefore, to improve the computational
efficiency and to assess the effect of the fluid-structure interactions on the fi nal optimised design, the degree of coupling is changed.
Several successful topology optimisation algorithms exist with thousands
of associated publications in the literature. However, only a small portion of these are applied to real-world problems, with even fewer offering a comparison of methodologies. This is especially important for problems involving fluid-structure interactions, where discrete and continuous methods can provide different advantages.
The goal of this research is to couple two key disciplines, fluids and structures, into a topology optimisation framework, which shows fast convergence for multi-physics optimisation problems. This is achieved by offering a comparison of three popular, but competing, optimisation methodologies. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms less efficient for this task. A coupled analysis, where the fluid and structural mechanics are updated, provides superior results compared with an uncoupled analysis approach, however at some computational expense. The results in this article show that the method is sensitive to whether fluid-structure coupling is included, i.e. if the fluid mechanics are updated with design changes, but not to the degree of the coupling, i.e. how regularly the fluid mechanics are updated, up to a certain limit. Therefore, the computational efficiency of the algorithm can be considerably increased with small penalties in the quality of the objective by relaxing the coupling
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Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm
Recently, the study of micro fluidic devices has gained much interest in various fi elds from biology to engineering. In the constant development cycle, the need to optimise the topology of the interior of these devices, where there are two or more optimality criteria, is always present. In this work, twin physical situations, whereby optimal fluid mixing in the form of vorticity maximisation is accompanied by the requirement that the casing in which the mixing takes place has the best structural performance in terms of the greatest speci c stiffness, are considered. In the steady state of mixing this also means that the stresses in the casing are as uniform as possible, thus giving a desired operating life with minimum weight.
The ultimate aim of this research is to couple two key disciplines, fluids
and structures, into a topology optimisation framework, which shows fast convergence for multidisciplinary optimisation problems. This is achieved by developing a bi-directional evolutionary structural optimisation algorithm that is directly coupled to the Lattice Boltzmann method, used for simulating the flow in the micro fluidic device, for the objectives of minimum compliance and maximum vorticity. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms, such as genetic algorithms, particle swarms and Tabu Searches, less efficient for this task.
The multidisciplinary topology optimisation framework presented in this article is shown to increase the stiffness of the structure from the datum case and produce physically acceptable designs. Furthermore, the topology optimisation method outperforms a Tabu Search algorithm in designing the baffle to maximise the mixing of the two fluids
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Multi-Physics Bi-directional Evolutionary Topology Optimization on GPU-architecture
Topology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs.D. J. Munk thanks the Australian government for their financial support through the Endeavour Fellowship scheme. The authors would like to acknowledge the UK Consortium on Mesoscale Engineering
Sciences (UKCOMES) EPSRC grant No EP/L00030X/1 for providing the HPC capabilities used in this article
Multi‑physics bi‑directional evolutionary topology optimization on GPU‑architecture
Topology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs
Flow-Based Optimization of Products or Devices
Flow-based optimization of products and devices is an immature field compared to the corresponding topology optimization based on solid mechanics. However, it is an essential part of component development with both internal and/or external flow. The aim of this book is two-fold: (i) to provide state-of-the-art examples of flow-based optimization and (ii) to present a review of topology optimization for fluid-based problems
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Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method
To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.D.J. Munk thanks the Australian government for their financial support through the Endeavour Fellowship scheme
From connected pathway flow to ganglion dynamics : understanding the effect of pore-scale properties on dynamic fluid connectivity and average flow functions
Since the turn of the industrial revolution in the early 1900s, the global economy has relied on fossil
fuels for energy, transport, and other day to day industrial, commercial, and domestic activities.
The combustion of fossil fuels (coal, petroleum (oil) and natural gas) is the primary cause of
atmospheric carbon dioxide (CO2) emissions which result in climate change and global warming.
Until we fully transition to cleaner alternative energy sources, the global economy will continue to
rely on fossil fuels. The injection and storage of CO2 in subsurface geological formations such as
saline aquifers and depleted oil and gas reservoirs, has been identified as a promising solution for
mitigating climate change and global warming.
Changes in reservoir rock and/or fluid properties at the pore-scale (scale of several microns) have
been known to have an impact on flow and transport properties at the Darcy-scale (scale of several
centimetres to metres). As such, successful implementation of CO2 storage technology at the large
scale, relies heavily on our ability to understand and predict changes that occur in the subsurface
at the pore scale and their subsequent effect on average flow functions. One of the major,
unresolved challenges in upscaling multiphase flow from the pore scale to the Darcy scale lies in
addressing the effects of connected and disconnected fluid fractions.
Direct numerical simulations (DNS) were coupled with flow through experiments in miniature
replicas of porous rocks fabricated on glass substrates (micromodels) to investigate the effects of
pore-scale flow and transport properties on dynamic fluid connectivity and average flow functions
such as displacement efficiency and the saturation function. Flow and transport properties
investigated include surface roughness, wettability, as well as fluid velocity.
Three pore-scale flow regimes were identified from the investigations conducted: two disconnected
pore scale flow regimes namely, the ganglion dynamics (GD) regime and the droplet traffic flow
(DTF) regime and a regime in which fluid displacement occurred by connected flow paths (the
connected pathway flow (CPF) regime). It was established that there is a relationship between the
dominant pore-scale mechanism and the kinetics of fluid displacement processes. Disconnected
flow regimes were found to accelerate the fluid displacement process. The impact of disconnected
and connected flow regimes was studied and it was determined that the GD regime can have a
negative impact on the efficiency of subsurface fluid displacement processes and would adversely
impact CO2 storage operations. In contrast, the DTF regime was found to enhance fluid
displacement efficiency. Transitions between connected and disconnected flow regimes were also
investigated and it was found that the shape of the saturation function is strongly influenced by
transitions between pore-scale flow regimes. This work shows that the impact of pore-scale
dynamic fluid connectivity on flow transport kinetics and the saturation function is highly significant
and should not be ignored. Pore-scale property induced changes in the rate of change of saturation
and the shape of the saturation function and could potentially have a knock-on effect on saturation dependent Darcy-scale functions such as relative permeability-saturation curves. Further work
should be done to ascertain the relationship between dynamic fluid connectivity and relative
permeability-saturation curves
Numerical and experimental analysis on microbubble generation and multiphase mixing in novel microfluidic devices
In this study, a novel K-junction microfluidic junction and a conventional cross-junction were investigated numerically and experimentally for microbubble generation and multiple fluids mixing. In the K-junction, liquid solutions were injected into the junction via three liquid inlet channels, along with inert nitrogen gas supplied via the gas inlet channel, to periodically generate microbubbles in a controlled manner at the outlet channel. Numerical simulations based on Finite Volume method and Volume of Fluid (VOF) technique and experiments of both the K-junction and the cross-junction were conducted. The effect of parameters such as contact angle, surface tension, viscosity, gas pressure and gas-liquid flow ratios on the microbubble size distribution was investigated. The process of microbubble generation, obtained through high speed camera imaging and the numerical simulation, has shown good agreement in both junctions as well as the influence of viscosity and gas-liquid flow ratios for the K-junction and cross-junction. It was indicated that parameters like solution viscosities, gas-to-liquid flow ratios, gas inlet pressure, and their combination have a significant influence on the microbubble diameter, which was found to be in the range of 70-240 µm when using micro capillaries of 100 µm inner diameter. The multiple fluids mixing study was investigated by using two or three different polymer solutions for the cross-junction and the K-junction respectively in simulations and experiments. It can be seen that the mixing process obtained from simulations agrees well with experimental results and chaotic mixing was found in the mixing area of the K-junction, with higher mixing efficiency than the cross junction. Fluorescent images of microbubbles generated by using polymer solutions with dyes inside have shown the devices’ potential of encapsulating fluorescent dyes and polymers on the shell of bubbles and could be adopted as a method to encapsulate active pharmaceutical ingredients for potential applications in drug delivery
Detection of Pathogens in Water Using Micro and Nano-Technology
Detection of Pathogens in Water Using Micro and Nano-Technology aims to promote the uptake of innovative micro and nano-technological approaches towards the development of an integrated, cost-effective nano-biological sensor useful for security and environmental assays.Â
The book describes the concerted efforts of a large European research project and the achievements of additional leading research groups. The reported knowledge and expertise should support in the innovation and integration of often separated unitary processes. Sampling, cell lysis and DNA/RNA extraction, DNA hybridisation detection micro- and nanosensors, microfluidics, together also with computational modelling and risk assessment can be integrated in the framework of the current and evolving European regulations and needs. The development and uptake of molecular methods is revolutionizing the field of waterborne pathogens detection, commonly performed with time-consuming cultural methods. The molecular detection methods are enabling the development of integrated instruments based on biosensor that will ultimately automate the full pathway of the microbiological analysis of water
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Topology optimization for thermal-fluid applications using an unstructured finite volume scheme
Topology optimization is a method for developing optimized geometric designs that maximize a quantity of interest (QoI) subject to constraints. Unlike shape optimization, which optimizes the dimensions of a template shape, topology optimization does not start with a pre-conceived shape. Instead, the algorithm builds the geometry iteratively by placing material pixels in a specified background domain, aiming to maximize the QoI subject to a constraint on the volume of material or other constraints. The power of topology optimization lies in its ability to realize design solutions that are not initially apparent to the engineer. Topology optimization, though well established in structural applications, has not percolated to the thermal-fluids community to any great degree, and most published papers have not addressed sufficiently realistic engineering problems. However, the methodology has immense application potential in the area of fluid flow, heat and mass transfer and other transport phenomena at all length scales. In the literature, the solution methodology used for topology optimization is based mostly on finite element methods. However, unstructured finite volume methods are frequently the numerical method of choice in the industry for those addressing thermal-fluid or other transport problems. It is essential that methods for topology optimization work well in the finite volume framework if they are to find traction in industry. Regardless of the numerical method employed for forward solution, the most popular methodology employed for topology optimization is the solid isotropic material with penalization (SIMP) approach in conjunction with a gradient-based optimization algorithm. This optimization approach requires the calculation of sensitivity derivatives of the QoI with respect to design variables through a discrete adjoint method. The Method of Moving Asymptotes (MMA) is a widely-used algorithm for topology optimization. Thus the objective of this dissertation is to build a robust framework for topology optimization for thermal-fluid problems, employing SIMP and MMA, within the framework of industry-standard finite volume schemes.Towards realizing this goal, we first develop and demonstrate topology optimization for multidimensional steady heat conduction problems in a cell-centered unstructured finite volume framework. The fundamental methodologies for SIMP/RAMP interpolation of thermal conductivity and the basic optimization infrastructure using MMA are developed and tested in this chapter. The effect of including secondary gradients in sensitivity computations is evaluated for typical heat conduction problems. Topologies that maximize or minimize relevant quantities of interest in heat conduction applications with and without volumetric heat generation are presented. Industry standard finite volume codes for fluid flow are built on unstructured cell-centered formulations employing co-located pressure-velocity storage, and a sequential solution algorithm. This type of algorithm is very widely used, but poses a number of difficulties when used as the solution kernel for performing efficient gradient-based topology optimization. The complete Jacobian required for discrete adjoint sensitivity computation is never available in a sequential technique. Also, the complexities of co-located algorithms must be correctly reflected in the Jacobian and sensitivity computations if correct optimal structures are to evolve. We build an Automatic Differentiation library, christened 'Rapid', to compute accurate Jacobians and other necessary derivatives for the discrete adjoint method in the context of an unstructured co-located sequential pressure based algorithm. The library is designed to provide a problem-agnostic pathway to automatically computing all required derivatives to machine accuracy. With sensitivities obtained from the Rapid library, we next develop and demonstrate topology optimization for multidimensional laminar flow applications. We present a variety of test cases involving internal channel flows as well as external flows, for a range of Reynolds numbers. An essential feature of Rapid is that it is not necessary to write new code to find sensitivities when new physics, such as turbulence models, are added, or when new cost functions are considered. The next step is therefore to extend the topology optimization for flow problems to the turbulent regime. Based on the Spalart-Allmaras RANS turbulence model, the topology optimization methodology for steady state turbulent flow problems is developed and demonstrated for channel flow problems. Finally we develop topology optimization methodology for forced convection applications which requires the coupling of the Navier-Stokes and energy equations and which are typically solved sequentially in finite volume schemes. The coupled nature of the problem introduces the concept of multi-objective opposing cost functions from the two physical models, for example, minimizing pressure drop and simultaneously maximizing heat transfer. Techniques to obtain sensitivities for forced convection with laminar and turbulent flow with Rapid are presented. Challenges for topology optimization resulting from multi-objective cost functions are discussed. We believe this is the first time that a complete topology optimization framework using an unstructured finite volume method and the discrete adjoint method, fully generalizable to practical use in commercial solvers and for industrial applications, has been demonstrated in the open literature. The methodologies developed here provide a basis for performing topology optimization involving other transport phenomena, more complex cost functions and more realistic constraints.Mechanical Engineerin