Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II

Genetic Programming For Cellular Automata Urban Inundation Modelling

Recent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods than using standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local state-transition rules that determine the progression of the flow from one cell in the grid to another cell, in many publications the Manning’s Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be processed, although these methods are limited by the approximation represented by the Manning’s formula. Literature demonstrates that the viability of the Manning’s formula will break down with too large a time step, flow rates, too small a cell size, or too smooth roughness factor; Therefore further increases in computational efficiency could be gained with a better approximation, or rather one capable of producing the required simulation with enough accuracy at larger time steps, smaller cells sizes, smoother roughness factors. Genetic programming has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Mannings formula and findings indicate that the GP rules can improve on these approaches

Interactive 3D Visualization Of Optimization For Water Distribution Systems

This project investigates the use of modern 3D visualisation techniques to enable the interactive analysis of water distribution systems with the aim of providing the engineer with a clear picture of the problem and thus aid the overall design process. Water distribution systems are complex entities that are difficult to model and optimise as they consist of many interacting components each with a set of considerations to address, hence it is important for the engineer to understand and assess the behaviour of the system to enable its effective design and optimisation. This paper presents a new three-dimensional representation of pipe based water systems and demonstrates a range of innovative methods to convey information to the user resulting in the ability to simultaneously display more useful information than traditional two-dimensional plan view network representations. The interactive visualisation system presented not only allows the engineer to visualise the various parameters of a network but also allows the user to observe the behaviour and progress of an iterative optimisation method. This paper contains examples of the combination of the interactive visualisation system and an evolutionary algorithm enabling the user to track and visualise the actions of the algorithm down to an individual pipe diameter change. The visualisation will aggregate changes to the network over an evolutionary algorithm run and ‘lift the lid’ on the operations of an EA as it is optimising a network. In addition, the method allows the engineer to view other important optimisation-related information such as the extent to which constraints have been violated in the current design. It is proposed that this interactive visualisation system will provide engineers an unprecedented view of the way in which optimisation algorithms interact with a network model and may pave the way for greater interaction between engineer, network and optimiser in the future

Fast approximation of centrality and distances in hyperbolic graphs

We show that the eccentricities (and thus the centrality indices) of all vertices of a $\delta$-hyperbolic graph $G=(V,E)$ can be computed in linear time with an additive one-sided error of at most $c\delta$, i.e., after a linear time preprocessing, for every vertex $v$ of $G$ one can compute in $O(1)$ time an estimate $\hat{e}(v)$ of its eccentricity $ecc_G(v)$ such that $ecc_G(v)\leq \hat{e}(v)\leq ecc_G(v)+ c\delta$ for a small constant $c$. We prove that every $\delta$-hyperbolic graph $G$ has a shortest path tree, constructible in linear time, such that for every vertex $v$ of $G$, $ecc_G(v)\leq ecc_T(v)\leq ecc_G(v)+ c\delta$. These results are based on an interesting monotonicity property of the eccentricity function of hyperbolic graphs: the closer a vertex is to the center of $G$, the smaller its eccentricity is. We also show that the distance matrix of $G$ with an additive one-sided error of at most $c'\delta$ can be computed in $O(|V|^2\log^2|V|)$ time, where $c'< c$ is a small constant. Recent empirical studies show that many real-world graphs (including Internet application networks, web networks, collaboration networks, social networks, biological networks, and others) have small hyperbolicity. So, we analyze the performance of our algorithms for approximating centrality and distance matrix on a number of real-world networks. Our experimental results show that the obtained estimates are even better than the theoretical bounds.Comment: arXiv admin note: text overlap with arXiv:1506.01799 by other author

The imprints of primordial non-gaussianities on large-scale structure: scale dependent bias and abundance of virialized objects

We study the effect of primordial nongaussianity on large-scale structure, focusing upon the most massive virialized objects. Using analytic arguments and N-body simulations, we calculate the mass function and clustering of dark matter halos across a range of redshifts and levels of nongaussianity. We propose a simple fitting function for the mass function valid across the entire range of our simulations. We find pronounced effects of nongaussianity on the clustering of dark matter halos, leading to strongly scale-dependent bias. This suggests that the large-scale clustering of rare objects may provide a sensitive probe of primordial nongaussianity. We very roughly estimate that upcoming surveys can constrain nongaussianity at the level |fNL| <~ 10, competitive with forecasted constraints from the microwave background.Comment: 16 pages, color figures, revtex4. v2: added references and an equation. submitted to PRD. v3: simplified derivation, additional reference

Dark energy, antimatter gravity and geometry of the Universe

This article is based on two hypotheses. The first one is the existence of the gravitational repulsion between particles and antiparticles. Consequently, virtual particle-antiparticle pairs in the quantum vacuum may be considered as gravitational dipoles. The second hypothesis is that the Universe has geometry of a four-dimensional hyper-spherical shell with thickness equal to the Compton wavelength of a pion, which is a simple generalization of the usual geometry of a 3-hypersphere. It is striking that these two hypotheses lead to a simple relation for the gravitational mass density of the vacuum, which is in very good agreement with the observed dark energy density

Strong lensing constraints on the velocity dispersion and density profile of elliptical galaxies

We use the statistics of strong gravitational lensing from the CLASS survey to impose constraints on the velocity dispersion and density profile of elliptical galaxies. This approach differs from much recent work, where the luminosity function, velocity dispersion and density profile were typically {\it assumed} in order to constrain cosmological parameters. It is indeed remarkable that observational cosmology has reached the point where we can consider using cosmology to constrain astrophysics, rather than vice versa. We use two different observables to obtain our constraints (total optical depth and angular distributions of lensing events). In spite of the relatively poor statistics and the uncertain identification of lenses in the survey, we obtain interesting constraints on the velocity dispersion and density profiles of elliptical galaxies. For example, assuming the SIS density profile and marginalizing over other relevant parameters, we find 168 km/s < sigma_* < 200 km/s (68% CL), and 158 km/s < sigma_* < 220 km/s (95% CL). Furthermore, if we instead assume a generalized NFW density profile and marginalize over other parameters, the slope of the profile is constrained to be 1.50 < beta < 2.00 (95% CL). We also constrain the concentration parameter as a function of the density profile slope in these models. These results are essentially independent of the exact knowledge of cosmology. We briefly discuss the possible impact on these constraints of allowing the galaxy luminosity function to evolve with redshift, and also possible useful future directions for exploration.Comment: Uses the final JVAS/CLASS sample, more careful choice of ellipticals, added discussion of possible biases. Final results essentially unchanged. Matches the MNRAS versio

Machine Learning-Based Early Warning System for Urban Flood Management

Characterisation of predictive limits of data-driven models (e.g. ANN) for urban flooding based on actual rainfall.With the growth in urban population and other pressures, such as climate change, the impact and severity of urban flood events are likely to continue to increase. “Intelligent water networks” are viewed as the way forward to ensure that infrastructure services are flexible, safe, reliable and economical. Reduction of flood-risk from urban drainage and sewerage infrastructure is likely to require increasingly sophisticated computational techniques to keep pace with the level of data that is collected both from meteorological and online water monitoring systems in the field. This paper describes and characterises an example of an Early Warning System (EWS), designated "RAPIDS" (RAdar Pluvial flooding Identification for Drainage System) that deals with urban drainage systems and the utilisation of rainfall data concurrently to predict flooding of multiple urban areas in near real-time using a single multi-output Artificial Neural Network (ANN). The system has the potential to provide early warning for decision makers within reasonable time, this being a key requirement determining the operational usefulness of such systems. Computational methods that require hours or days to run will not be able to keep pace with fast-changing situations such as manhole flooding or Combined Sewer Overflow (CSO) spills and thus the system developed is able to react in close to real time. This paper includes a sensitivity analysis and demonstrates that the - predictive capability of such a system based on actual rainfall is limited to a maximum of the Time of Concentration (ToC) of each node being modelled. To achieve operationally useful prediction times, predictions of rainfall as input signals are likely to be needed for most urban drainage networks.UKWIR RTM project (2011-12

Lead-free high-temperature dielectrics with wide operational range

The dielectric, electrical and structural properties of (1-x) (0.94Bi(1/2)Na(1/2)TiO(3)-0.06BaTiO(3))-xK(0.5)Na(0.5)NbO(3) (BNT-BT-xKNN) with x=0.09, 0.12, 0.15, and 0.18 were investigated as potential candidates for high-temperature capacitors with a working temperature far beyond 200 degrees C. Temperature dependent dielectric permittivity (epsilon) showed two local broad maxima that at the optimal composition of KNN (x=0.18) are combined to form a plateau. This then results in a highly temperature-insensitive permittivity up to similar to 300 degrees C at the expense of a small reduction in absolute permittivity values. High-temperature in situ x-ray diffraction study showed pseudocubic symmetry without obvious structural changes, which implies that the dielectric anomalies observed could only be a consequence of a slight change in space group. BNT-BT-0.18KNN showed a permittivity of similar to 2150 at the frequency of 1 kHz at 150 degrees C with a normalized permittivity epsilon/epsilon(150 degrees C) varying no more than +/- 10% from 43 to 319 degrees C. With very good electrical properties persisting up to 300 degrees C, i.e., a resistivity on the order of magnitude of 10(8) Omega m and the RC constant of about 1 s, the examined BNT-BT-xKNN compositions present a good starting point for the development of high-temperature capacitor materials.open343