Multi-objective optimisation of a floating LNG terminal

The paper presents a comprehensive multi-objective hydrodynamic optimisation procedure and its application to the early design of a floating liquefied natural gas (LNG) terminal for improved seakeeping and wave attenuation characteristics on its lee side. Genetic algorithms are used to find the Pareto optima designs followed by multi-objective decision making procedures to select the optimum design among them. The paper addresses the definition of the relevant optimisation problem and the set-up and interface of relevant software tools, presents results of systematic studies with respect to the minimisation of the motion responses and wave elevation on the leeward side of free-floating terminals and concludes with analysis and critical review of the obtained results and their impact on terminal design.     

Development and parameterisation of a complex hydrogeological model based on high-resolution direct-push data

Ongoing developments in geological and hydrogeological investigation techniques, especially direct-push methods, have led to an increase in the quality, density and spatial resolution of data available from such investigations. This has created new challenges in the development of numerical models in terms of accurately and efficiently translating detailed and complex conceptual models into effective numerical models. Suitable geometrical and numerical modelling tools are essential in order to meet these challenges. This paper describes the development of a three-dimensional hydrogeological flow model for a contaminated site near Berlin, Germany, based on high-resolution geological data obtained principally using direct-push methods. The available data were first interpreted to construct a detailed GIS-based geological model, which formed the basis of the conceptual site model. The conceptual model was then translated into a geometrical model, which was used to create a finite element numerical model. An innovative geometry object-based approach enabled the complex structural details of the conceptual model to be accurately reproduced in the numerical model domain. The resulting three-dimensional steady-state unconfined flow model was successfully calibrated using external automated calibration software, whereby parameter values for groundwater recharge and hydraulic conductivity were determined.     

Interpretation of borehole magnetometer data for the detection and characterisation of unexploded bombs

Unexploded war-time bombs (UXB) can be found at the ground surface or buried at depths of up to 20 m, where surface-based detection methods become ineffective due to signal weakness and interference. A total-field borehole magnetometer can penetrate to such depths and collect relatively quiet data. However, conventional interpretation techniques suffer from the inherent non-uniqueness in the borehole dimension. In this paper, a constrained optimisation method is utilised for the interpretation of total-field borehole magnetometer data for the detection of deeply buried unexploded bombs. The major advantage over conventional techniques comes from the analytically-derived constraints imposed on the parameter vector by excluding non-geosensible results from consideration and hence reducing the non-uniqueness to a minimal level. A depth dependant objective function is defined to minimise the interference and measurement errors. A test site has been developed for evaluation using real world data. The interpretation results demonstrate its superior capability in handling real-world problems with high non-uniqueness. Furthermore, this method provides a way to estimate the moment strength without knowing the exact position. Together with the modelled signature data for different objects, characterisation of a particular bomb is possible from the inversion of a single total-field borehole profile.     

基于半分析模型的高光谱遥感水深探测方法

海岸带浅水和底质的成分复杂且分布不均,使遥感水深探测面临很大挑战.尝试通过利用Hydrolight 仿真建立的遥感反射率半分析模型来解决这个问题.采用英属维尔京群岛彼得岛的高光谱影像,应用非线性优化的方法提取了水下地形.从反演水深和海图标注水深拟合的趋势线斜率为0.939 2,决定系数为0.825 8.试验结果证明,使...     

BEM analysis of the interaction factor for vertically loaded dissimilar piles in saturated poroelastic soil

This paper focuses on an analysis by the boundary element method (BEM) of the pile-to-pile interaction for pile groups with dissimilar piles of different pile lengths embedded in saturated poroelastic soil. The behaviour of the poroelastic homogeneous soil is governed by Biot’s consolidation equations. The pile–soil system is decomposed into extended soil and fictitious piles. Considering the compatibility of vertical strain between fictitious piles and soil, the second kind of Fredholm integral equations were obtained to predict the axial force and settlement along pile shafts numerically. For the analysis of the interaction factor, two loading conditions for a two-dissimilar-pile system were proposed: (a) only one pile is loaded and (b) each pile is subjected to a load proportional to the pile length. Furthermore, the two-pile system was extended to pile groups with a rigid cap to capture the optimum design where each pile shares the same loading at the pile heads. The optimum results require shortening the peripheral piles and elongating internal piles, and the consolidation effect needs to be considered due to the adjustment of loading distribution among piles.     

An automated approach for conditioning discrete fracture network modelling to in situ measurements

Creation of a geological fracture network model conditioned to in situ geometric measurements is of great importance to geoprofessionals, as fractures dominate pathways for fluid flow, a major concern for many engineering applications. This paper introduces and applies the Stochastic Nelder Mead simplex method to automatically calibrate stochastic parameters of geometric characterisations of a discrete fracture network model. This method can overcome the non-convergence of a previous exploratory approach on the classic Nelder Mead method by others, and is an effective substitution to the manual trial-and-error method and is complementary to existing conditional simulation approaches. The procedure to integrate the Stochastic Nelder Mead with a discrete fracture network is presented in detail, and a case study was conducted. Results show that the improved model can better handle the stochastic nature of the underlying system and effectively simulates the observed number and mean trace length of these fractures, although the model results underestimate its standard deviation. Simulated distributions of trace lengths and spacings are within acceptable ranges except for some small offsets, which can be adjusted during model runs.     

Data-driven estimation of building interior plans

This work investigates constructing plans of building interiors using learned building measurements. In particular, we address the problem of accurately estimating dimensions of rooms when measurements of the interior space have not been captured. Our approach focuses on learning the geometry, orientation and occurrence of rooms from a corpus of real-world building plan data to form a predictive model. The trained predictive model may then be queried to generate estimates of room dimensions and orientations. These estimates are then integrated with the overall building footprint and iteratively improved using a two-stage optimisation process to form complete interior plans.

The approach is presented as a semi-automatic method for constructing plans which can cope with a limited set of known information and constructs likely representations of building plans through modelling of soft and hard constraints. We evaluate the method in the context of estimating residential house plans and demonstrate that predictions can effectively be used for constructing plans given limited prior knowledge about the types of rooms and their topology.     

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

The optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.     

Measuring segregation as a spatial optimisation problem,revisited: a case study of London, 1991–2011

There has been long and wide-ranging debate in the social science literature about how best to conceptualise and to measure segregation. A popular measure is the dissimilarity index, usually attributed to Duncan and Duncan who were aware of its geographical limitations – that it, like most indices, is invariant to the precise spatial patterning of the segregation measured. Whilst one response to this shortcoming has been to develop a spatial adjustment, a number of papers from the 1980s and 1990s took the approach of treating the measurement as a (spatial) optimisation problem. This paper revisits that optimisation literature, arguing that what was computationally prohibitive in the past is now possible in the open-source software, R, and emblematic of the sorts of problem that might be more routinely solved in a cyberinfrastructure tailored to geographical analysis. Applying this method to UK Census data for London, and comparing the optimisation measure with the standard and adjusted dissimilarity indices, claims of ethnic desegregation are considered.     

Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm / Modélisation d’incertitudes dans la gestion de barrage à court terme grâce à des ensembles flous et à un algorithme génétique

Abstract

Abstract Various uncertainties are inherent in modelling any reservoir operation problem. Two of these are addressed in this study: uncertainty involved in the expression of reservoir penalty functions, and uncertainty in determining the target release value. Fuzzy set theory was used to model these uncertainties where the preferences of the decision maker for the fuzzified parameters are expressed as membership functions. Nonlinear penalty functions are used to determine the penalties due to deviations from targets. The optimization was performed using a genetic algorithm with the objectives to minimize the total penalty and to maximize the level of satisfaction of the decision maker with fuzzified input parameters. The proposed formulation was applied to the problem of finding the optimal release and storage values, taking Green reservoir in Kentucky, USA as a case study. The approach offers more flexibility to reservoir decision-making by demonstrating an efficient way to represent subjective uncertainties, and to deal with non-commensurate objectives under a fuzzy multi-objective environment.