scholarly journals A balanced calibration of water quantity and quality by multi-objective optimization for integrated water system model

2016 ◽  
Vol 538 ◽  
pp. 802-816 ◽  
Author(s):  
Yongyong Zhang ◽  
Quanxi Shao ◽  
John A. Taylor
2021 ◽  
Vol 5 (1) ◽  
pp. 43-62
Author(s):  
Ming Chang Ong ◽  
Yik Teeng Leong ◽  
Yoke Kin Wan ◽  
Irene Mei Leng Chew

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
J. M. Hamel ◽  
Devin Allphin ◽  
Joshua Elroy

A system-level computational model of a recently patented and prototyped novel steam engine technology was developed from first principles for the express purpose of performing design optimization studies for the engine's inventors. The developed system model consists of numerous submodels including a flow model of the intake process, a dynamic model of the intake valve response, a pressure model of the engine cylinder, a kinematic model of the engine piston, and an output model that determines engine performance parameters. A crank-angle discretization strategy was employed to capture the performance of engine throughout a full cycle of operation, thus requiring all engine design submodels to be evaluated at each crank angle of interest. To produce a system model with sufficient computational speed to be useful within optimization algorithms, which must exercise the system level model repeatedly, various simplifying assumptions and modeling approximations were utilized. The model was tested by performing a series of multi-objective design optimization case studies using the geometry and operating conditions of the prototype engine as a baseline. The results produced were determined to properly capture the fundamental behavior of the engine as observed in the operation of the prototype and demonstrated that the design of engine technology could be improved over the baseline using the developed computational model. Furthermore, the results of this study demonstrate the applicability of using a multi-objective optimization-driven approach to conduct conceptual design efforts for various engine system technologies.


Author(s):  
Tabony Aiman ◽  
Llabres-Valls Enriqueta

Current conditions related to food security lead to study alternative forms of food production in cities such as vertical urban farming in high dense urban environments. This paper discusses the development of the Innovate UK award-winning project consisting of a dynamic system model that generates a large dataset of artificial environments linked to a multi-objective optimization model of urban massing for one square kilometer of development along the coastline of Singapore. The scope of the model is to reach the highest level of self-sufficiency in relation to food consumption. The model operates, as a dynamic system constituted of different subsystems including transport, water, agriculture and energy. These systems dynamically interact among each other and with their environment, which is considered the primary source of energy and the main provider of hydrological resources. A large dataset of artificial environments is created employing a Dynamic System Modelling Software; this includes different scenarios of environmental stress such as sea level rise, population growth or changes on the demand side. Such dataset of artificial environments serves as an input for the multi-objective optimization model that employs genetic algorithms to produce a large data set of urban massing including the distribution of a range of food production technologies in relation to pre-established conditions for vertical urban agriculture and compatibility with other urban programs. Connectivity, solar radiation and visual cones are the fitness criteria against which the model has been tested. This paper assesses whether artificial environments further away from the pareto front produce populations of urban design solutions that respond to extreme environmental conditions and environmental shocks.


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