Pressure-Driven Demand Extension for EPANET (EPANETpdd)

addresses: Mark Morley, University of Exeter, Centre for Water Systems, North Park Road, Exeter, Devon, EX4 4QF, United Kingdom; Carla Tricarico, Università degli Studi di Cassino e del Lazio Meridionale, Dipartimento di Ingegneria Civile e Meccanica, via di Biasio, 43, Cassino, Lazio, ItalyIntroduction Predominantly, Demand-Driven hydraulic simulators such as EPANET used in optimization processes are configured to deliver water even when there is insufficient pressure to do so – Demand-Driven network solver (as EPANET – Rossman, 2000). In the analysis of structurally inadequate systems, however, recent studies [Germanopoulos, 1985, Hayuti & Burrows, 2004, Soares et al., 2003], have highlighted limitations related to the use of such demand-driven solvers. Initially, the sole requirement for the PDD extension was for it to be able to determine more accurately the non-revenue water unsupplied in a pressure-deficient network in order to better estimate the network’s Economic Level of Reliability [Tricarico et al., 2006]. A logical extension of that work required that the PDD simulator should also be able to operate in an EPS mode. As well as EPS, the application of the simulator to the Neptune project introduced two further requirements. PD demand nodes need to be able to exist in parallel with EPANET’s conventional emitters and the ability to specify emitter exponents on an individual rather than global basis. This functionality is required to simulate bursts in networks: PDD nodes will be used to observe the effects on demand nodes whilst EPANET’s standard emitters will be used to simulate unconstrained bursts, which will be represented by different emitter characteristics.University of Exeter, Centre for Water System

Hybrid Evolutionary Optimization/Heuristic Technique for Water System Expansion and Operation

This paper presents a methodological solution to The Battle of Background Leakage Assessment for Water Networks (BBLAWN) competition. The methodology employs two constrained multiple-objective optimization problems and is implemented in the context of a software application for the generic hydraulic optimization and benchmarking of Water Distribution System (WDS) problems. The objectives are the combined infrastructure and operational costs and system-wide leakage, both to be minimized. In order to accelerate the evaluation of potential solutions, a distributed computing approach permits multiple EPANET solutions to be evaluated in parallel. A pressure-driven demand extension to EPANET assists the optimization in accurately ranking near-feasible solutions and to dynamically allocate leakage demand to nodes. Pressure Reducing Valves (PRVs) have been located in two ways: a priori, with respect to the optimization analysis and a posteriori after the infrastructure optimization to reduce excess pressure and pipe leakage. The latter demonstrates better overall fitness, leading to optimal configurations dominating those obtained with the former. Several temporal resolutions for PRV settings have been evaluated to contrast the optimal solutions with the computational effort required

Spectral Variability from the Patchy Atmospheres of T and Y Dwarfs

Brown dwarfs of a variety of spectral types have been observed to be photometrically variable. Previous studies have focused on objects at the L/T transition, where the iron and silicate clouds in L dwarfs break up or dissipate. However, objects outside of this transitional effective temperature regime also exhibit variability. Here, we present models for mid-late T dwarfs and Y dwarfs. We present models that include patchy salt and sulfide clouds as well as water clouds for the Y dwarfs. We find that for objects over 375 K, patchy cloud opacity would generate the largest amplitude variability within near-infrared spectral windows. For objects under 375 K, water clouds also become important and generate larger amplitude variability in the mid-infrared. We also present models in which we perturb the temperature structure at different pressure levels of the atmosphere to simulate hot spots. These models show the most variability in the absorption features between spectral windows. The variability is strongest at wavelengths that probe pressure levels at which the heating is the strongest. The most illustrative types of observations for understanding the physical processes underlying brown dwarf variability are simultaneous, multi-wavelength observations that probe both inside and outside of molecular absorption features.Comment: 6 pages, 5 figures, Accepted for publication in ApJ Letter

Photolytic Hazes in the Atmosphere of 51 Eri b

We use a 1D model to address photochemistry and possible haze formation in the irradiated warm Jupiter, 51 Eridani b. The intended focus was to be carbon, but sulfur photochemistry turns out to be important. The case for organic photochemical hazes is intriguing but falls short of being compelling. If organic hazes form, they are likeliest to do so if vertical mixing in 51 Eri b is weaker than in Jupiter, and they would be found below the altitudes where methane and water are photolyzed. The more novel result is that photochemistry turns H$_2$S into elemental sulfur, here treated as S$_8$. In the cooler models, S$_8$ is predicted to condense in optically thick clouds of solid sulfur particles, whilst in the warmer models S$_8$ remains a vapor along with several other sulfur allotropes that are both visually striking and potentially observable. For 51 Eri b, the division between models with and without condensed sulfur is at an effective temperature of 700 K, which is within error its actual effective temperature; the local temperature where sulfur condenses is between 280 and 320 K. The sulfur photochemistry we have discussed is quite general and ought to be found in a wide variety of worlds over a broad temperature range, both colder and hotter than the 650-750 K range studied here, and we show that products of sulfur photochemistry will be nearly as abundant on planets where the UV irradiation is orders of magnitude weaker than it is on 51 Eri b.Comment: 24 pages including 11 figures and a tabl

Integrated Decision Support Framework

© TRUST 2012An Integrated Framework for the development of a Decision Support System (DSS) is described, facilitating strategic decision-making for the long-term city metabolism planning problem. A novel methodology for comparing and selecting alternative solutions is presented employing Multi-Criteria Decision Analysis and multiple scenarios handling resulting in a system able to deal with uncertain futures, complementing the modules developed in WP54 and using the same platform (AWARE-P) as the other software applications delivered in WA5. The DSS assists in the decision making process by managing problem definition, structuring/analysis and solving. This document outlines the Integrated Decision Support Framework upon which the DSS will ultimately be delivered. Succinctly, this is the software environment in which the DSS will be constructed, describing the concepts, data structures, data flows and associations that will be required to bring it to fruition.European Union Seventh Framework Programme (FP7/2007-2013

WP54: TRUST DSS Memo

© TRUST 2015Introduction This document is written for the benefit of internal TRUST reviewers with the aim to help them install and assess the DSS software tool (Deliverable D54.2). A more comprehensive description of the DSS methodology, software tool and demonstration on a real-life example will be provided in the final report (Deliverable 54.3) which is due by the end of the project […

A DSS generator for multiobjective optimisation of spreadsheet-based models

Copyright © 2011 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software Vol. 26 (2011), DOI: 10.1016/j.envsoft.2010.11.004Water management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support applications. GANetXL’s utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.Engineering and Physical Sciences Research Council (EPSRC

A Framework for Evolutionary Optimization Applications in Water Distribution Systems

The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.Engineering and Physical Sciences Research Council, UK

Water Clouds in Y Dwarfs and Exoplanets

The formation of clouds affects brown dwarf and planetary atmospheres of nearly all effective temperatures. Iron and silicate condense in L dwarf atmospheres and dissipate at the L/T transition. Minor species such as sulfides and salts condense in mid-late T dwarfs. For brown dwarfs below Teff=450 K, water condenses in the upper atmosphere to form ice clouds. Currently over a dozen objects in this temperature range have been discovered, and few previous theoretical studies have addressed the effect of water clouds on brown dwarf or exoplanetary spectra. Here we present a new grid of models that include the effect of water cloud opacity. We find that they become optically thick in objects below Teff=350-375 K. Unlike refractory cloud materials, water ice particles are significantly non-gray absorbers; they predominantly scatter at optical wavelengths through J band and absorb in the infrared with prominent features, the strongest of which is at 2.8 microns. H2O, NH3, CH4, and H2 CIA are dominant opacity sources; less abundant species such as may also be detectable, including the alkalis, H2S, and PH3. PH3, which has been detected in Jupiter, is expected to have a strong signature in the mid-infrared at 4.3 microns in Y dwarfs around Teff=450 K; if disequilibrium chemistry increases the abundance of PH3, it may be detectable over a wider effective temperature range than models predict. We show results incorporating disequilibrium nitrogen and carbon chemistry and predict signatures of low gravity in planetary- mass objects. Lastly, we make predictions for the observability of Y dwarfs and planets with existing and future instruments including the James Webb Space Telescope and Gemini Planet Imager.Comment: 23 pages, 20 figures, Revised for Ap