Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

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A challenge in petroleum geostatistics is the application of modeling algorithms such as Gaussian simulation to unstructured grids that are being used for flow simulation. Geostatistical modeling is typically applied on a fine scale regular grid and then upscaled to the unstructured grid. This work proposes a fine scale unstructured grid. The grid is designed so that its elements align with the flow simulation grid elements, eliminating the occurrence of intersections between the two grids. Triangular and tetrahedral grids are used for the fine scale grid; however, they introduce a variety of element scales. The approach developed in this work populates the fine scale grid based on the scale of conditioning data. The resulting error due to scale discrepancy is quantified and mitigated though the upscaling process. A methodology to assess the error in upscaled properties is developed and used to control the design of the fine scale grid. Populating the fine scale grid with reservoir properties requires modification of existing geostatistical algorithms. The set of spatial locations for modeling is irregular and three differences that result from this are addressed: random path generation; spatial search; and the covariance lookup table. Results are compiled into an algorithm for sequential indicator and sequential Gaussian simulation on irregular point sets. Checking variogram reproduction on large irregular point sets is a challenge. An algorithm that efficiently computes the experimental variogram for these cases is developed. A flow based upscaling method based on the multipoint flux approximation is developed to upscale permeability models from the fine scale unstructured grid to the flow simulation grid. Triangular grids are assumed for the fine scale. Flow simulation results using the upscaled transmissibilities are very similar to results obtained using traditional flow simulation on high resolution regular grids. / Mining Engineering

A numerical method for the simulation of compressible turbulent flows is presented. This method includes a novel hybrid grid generation for airfoil cascades and an unstructured mesh flow solver. The mesh tool incorporates a mapping technique and a grid smoothing method. The mapping technique is used to build an initial volume mesh and the grid smoothing method is used to improve the quality of the initial mesh. The grid smoothing is based on the optimization of mesh-quality parameters. The further improvement of the smoothed mesh is achieved by an edge-swapping and node-insertion technique. The unstructured flow solver is developed for a hybrid grid. This flow solver uses a rotational frame of reference. The convective and viscous fluxes are numerically solved by an upwind scheme and an averaged nodal gradient. A higher-order spatial accuracy is achieved by a piece-wise linear reconstruction. An explicit multi-stage method is employed for integration in time. The Menter’s k −τ model is implemented to simulate the turbulence effects. The flow solver is validated against the analytical and experimental results. A parametric study is performed for a high speed centrifugal compressor.

A numerical method for the simulation of compressible turbulent flows is presented. This method includes a novel hybrid grid generation for airfoil cascades and an unstructured mesh flow solver. The mesh tool incorporates a mapping technique and a grid smoothing method. The mapping technique is used to build an initial volume mesh and the grid smoothing method is used to improve the quality of the initial mesh. The grid smoothing is based on the optimization of mesh-quality parameters. The further improvement of the smoothed mesh is achieved by an edge-swapping and node-insertion technique. The unstructured flow solver is developed for a hybrid grid. This flow solver uses a rotational frame of reference. The convective and viscous fluxes are numerically solved by an upwind scheme and an averaged nodal gradient. A higher-order spatial accuracy is achieved by a piece-wise linear reconstruction. An explicit multi-stage method is employed for integration in time. The Menter’s k −τ model is implemented to simulate the turbulence effects. The flow solver is validated against the analytical and experimental results. A parametric study is performed for a high speed centrifugal compressor.

A development of high resolution NFT model for simulation of incompressible flows is presented. The model uses finite volume spatial discretisation with edge based data structure and operates on unstructured meshes with arbitrary shaped cells. The key features of the model include non-oscillatory advection scheme Multidimensional Positive Definite Advection Transport Algorithm (MPDATA) and non-symmetric Krylov-subspace elliptic solver. The NFT MPDATA model integrates the Reynolds Average Navier Stokes (RANS) equations. The implementation of the Spalart-Allmaras one equations turbulence model extends the development further to turbulent flows. An efficient non-staggered mesh arrangement for pressure and velocity is employed and provides smooth solutions without a need of artificial dissipation. In contrast to commonly used schemes, a collocated arrangement for flow variables is possible as the stabilisation of the NFT MPDATA scheme arises naturally from the design of MPDATA. Other benefits of MPDATA include: second order accuracy, strict sign-preserving and full multidimensionality. The flexibility and robustness of the new approach is studied and validated for laminar and turbulent flows. Theoretical developments are supported by numerical testing. Successful quantitative and qualitative comparisons with the numerical and experimental results available from literature confirm the validity and accuracy of the NFT MPDATA scheme and open the avenue for its exploitation for engineering problems with complex geometries requiring flexible representation using unstructured meshes.

Although having conscious experiences is a fundamental feature of our everyday life, our understanding of what consciousness is is very limited. According to one of the main conclusions of contemporary philosophy of mind, the qualitative aspect of consciousness seems to resist functionalisation, i.e. it cannot be adequately defined solely in terms of functional or causal roles, which leads to an epistemic gap between phenomenal and scientific knowledge. Phenomenal qualities, then, seem to be, in principle, unexplainable in scientific terms. As a reaction to this pessimistic conclusion it is a major trend in contemporary science of consciousness to turn away from subjective experiences and re-define the subject of investigations in neurological and behavioural terms. This move, however, creates a gap between scientific theories of consciousness, and the original phenomenon, which we are so intimately connected with. The thesis focuses on this gap. It is argued that it is possible to explain features of consciousness in scientific terms. The thesis argues for this claim from two directions. On the one hand, a specific identity theory is formulated connecting phenomenal qualities to certain intermediate level perceptual representations which are unstructured for central processes of the embedding cognitive system. This identity theory is hypothesised on the basis of certain similarities recognised between the phenomenal and the cognitive-representational domains, and then utilised in order to uncover further similarities between these two domains. The identity theory and the further similarities uncovered are then deployed in formulating explanations of the philosophically most important characteristics of the phenomenal domain—i.e. why phenomenal qualities resist functionalisation, and why the epistemic gap occurs. On the other hand, the thesis investigates and criticises existing models of reductive explanation. On the basis of a detailed analysis of how successful scientific explanations proceed a novel account of reductive explanation is proposed, which utilises so-called prior identities. Prior identities are prerequisites rather than outcomes of reductive explanations. They themselves are unexplained but are nevertheless necessary for mapping the features to be explained onto the features the explanation relies on. Prior identities are hypothesised in order to foster the formulation of explanatory claims accounting for target level phenomena in terms of base level processes—and they are justified if they help projecting base level explanations to new territories of the target level. The thesis concludes that the identity theory proposed is a prior identity, and the explanations of features of the phenomenal domain formulated with the aid of this identity are reductive explanations proper. In this sense, the thesis introduces the problem of phenomenal consciousness into scientific discourse, and therefore offers a bridge between the philosophy and the science of consciousness: it offers an approach to conscious experience which, on the one hand, tries to account for the philosophically most important features of consciousness, whereas, on the other hand, does it in a way which smoothly fits into the everyday practice of scientific research.

This work is part of an effort to develop an unstructured, three-dimensional, direct simulation Monte Carlo/particle-in-cell (DSMC/PIC) code for the simulation of non-ionized, fully ionized and partially-ionized flows in micropropulsion devices. Flows in microthrusters are often in the transitional to rarefied regimes, requiring numerical techniques based on the kinetic description of the gaseous or plasma propellants. The code is implemented on unstructured tetrahedral grids to allow discretization of arbitrary surface geometries and includes an adaptation capability. In this study, an existing 3D DSMC code for rarefied gasdynamics is improved with the addition of the variable hard sphere model for elastic collisions and a vibrational relaxation model based on discrete harmonic oscillators. In addition the existing unstructured grid generation module of the code is enhanced with grid-quality algorithms. The unstructured DSMC code is validated with simulation of several gaseous micronozzles and comparisons with previous experimental and numerical results. Rothe s 5-mm diameter micronozzle operating at 80 Pa is simulated and results are compared favorably with the experiments. The Gravity Probe-B micronozzle is simulated in a domain that includes the injection chamber and plume region. Stagnation conditions include a pressure of 7 Pa and mass flow rate of 0.012 mg/s. The simulation examines the role of injection conditions in micronozzle simulations and results are compared with previous Monte Carlo simulations. The code is also applied to the simulation of a parabolic planar micronozzle with a 15.4-micron throat and results are compared with previous 2D Monte Carlo simulations. Finally, the code is applied to the simulation of a 34-micron throat MEMS-fabricated micronozzle. The micronozzle is planar in profile with sidewalls binding the upper and lower surfaces. The stagnation pressure is set at 3.447 kPa and represents an order of magnitude lower pressure than used in previous experiments. The simulation demonstrates the formation of large viscous boundary layers in the sidewalls. A particle-in-cell model for the simulation of electrostatic plasmas is added to the DSMC code. Solution to Poisson's equation on unstructured grids is obtained with a finite volume implementation. The Poisson solver is validated by comparing results with analytic solutions. The integration of the ionized particle equations of motion is performed via the leapfrog method. Particle gather and scatter operations use volume weighting with linear Lagrange polynomial to obtain an acceptable level of accuracy. Several methods are investigated and implemented to calculate the electric field on unstructured meshes. Boundary conditions are discussed and include a formulation of plasma in bounded domains with external circuits. The unstructured PIC code is validated with the simulation of a high voltage sheath formation.

Replication is a popular technique for increasing data availability and improving perfor- mance in peer-to-peer systems. Maintaining freshness of replicated data is challenging due to the high cost of update management. While updates have been studied in structured networks, they have been neglected in unstructured networks. We therefore confront the problem of maintaining fresh replicas of data in unstructured peer-to-peer networks. We propose techniques that leverage path replication to support efficient lazy updates and provide freshness for cached data in these systems using only local knowledge. In addition, we show that locally available information may be used to provide additional guarantees of freshness at an acceptable cost to performance. Through performance simulations based on both synthetic and real-world workloads from big data environments, we demonstrate the effectiveness of our approach.