Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter a...
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Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters;some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca2+ ions with various types of solvents. This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'.
Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonabl...
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Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonable level of performance. The major difficulty in applying computational tools to support decision making lies in a large number of pavement sections as a result of a high length of road networks. This paper addresses maintenance scheduling for pavements by consolidating this task into two stages. In each stage, multiobjective optimization is used to optimize maintenance schedules. The main motivation is to obtain a computationally treatable model for large road networks. The first stage is defined by a collection of pavement sections composing the road network. In this stage, the performance and maintenance models are addressed. These models account for uncertainties in the future performance and effects of maintenance by defining model parameters as random variables. The second stage refers to combining maintenance schedules for individual sections to determine the optimal maintenance plan at the network level. The results obtained for the real road network demonstrate the validity and usefulness of the proposed framework. Moreover, this framework is general and can be extended to different types of infrastructure assets.
During the last decades, standards on building construction have risen sharply to integrate new, ambitious demands regarding energy efficiency, as well as thermal and optical comfort in the design procedure. Building ...
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During the last decades, standards on building construction have risen sharply to integrate new, ambitious demands regarding energy efficiency, as well as thermal and optical comfort in the design procedure. Building simulation software assists in the accurate calculation of a hypothetical or existing building's performance on several aspects;but they are, in their vast majority, assessment-oriented, rather than focused on dynamically supporting the decision-making procedure. During the last two decades, a clear shift of design professionals and academia towards addressing performance issues from the conceptual stages of a building's design is observed. In this review, the methodology of performance-driven design optimization using computational/parametric design and optimization is presented, and the core literature available on the topic is reviewed in order to identify the current status, different approaches, obstacles, and areas of future research on the subject. The review findings confirm that there is enormous potential for the design of better-performing buildings using this technique, but there are still many obstacles to overcome and areas for future research.
In this paper we present a scatter search (SS) heuristic for the optimal location, sizing and contract pricing of distributed generation (DG) in electric distribution systems. The proposed optimization approach consid...
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In this paper we present a scatter search (SS) heuristic for the optimal location, sizing and contract pricing of distributed generation (DG) in electric distribution systems. The proposed optimization approach considers the interaction of two agents: (i) the potential investor and owner of the DG, and (ii) the Distribution Company (DisCo) in charge of the operation of the network. The DG owner seeks to maximize his profits from selling energy to the DisCo, while the DisCo aims at minimizing the cost of serving the network demand, while meeting network constraints. To serve the expected demand the DisCo is able to purchase energy, through long-term bilateral contracts, from the wholesale electricity market and from the DG units within the network. The interaction of both agents leads to a bilevel programming problem that we solve through a SS heuristic. Computational experiments show that SS outperforms a genetic algorithm hybridized with local search both in terms of solution quality and computational time.
Using the evolutionary crystal structure prediction algorithm USPEX, we identify the compositions and crystal structures of thermodynamically stable compounds in the Fe-S system at pressures in the range of 100-400 GP...
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Using the evolutionary crystal structure prediction algorithm USPEX, we identify the compositions and crystal structures of thermodynamically stable compounds in the Fe-S system at pressures in the range of 100-400 GPa. We find that at pressures in the Earth's solid inner core (330-364 GPa) two compounds are stable-Fe2S and FeS. In equilibrium with iron, only Fe2S can exist in the inner core. Using the equation of state of Fe2S, we find that, in order to reproduce the density of the inner core by adding sulfur alone, 10.6-13.7 mol.% (6.4-8.4 wt.%) sulfur is needed. An analogous calculation for silicon (where the only stable compound at inner core pressures is FeSi) reproduces the density of the inner core with 9.0-11.8 mol. % (4.8-6.3 wt.%) silicon. In both cases, a virtually identical mean atomic mass (M) over bar in the range of 52.6-53.3 results for the inner core, which is much higher than (M) over bar = 49.3 inferred for the inner core from Birch's law. In the case of oxygen (allowing for the equilibrium coexistence of suboxide Fe2O with iron under core conditions), the inner core density can be explained by the oxygen content of 13.2-17.2 mol.% (4.2-5.6 wt.%), which corresponds to (M) over bar between 49.0 and 50.6. Combining our results and previous work, we arrive at four preferred compositional models of the Earth's inner core (in mol.%): (i) 86 % (Fe + Ni) + 14 % C;(ii) 84 % (Fe + Ni) + 16 % 0;(iii) 84% (Fe + Ni) + 7 % S + 9 % H;(iv) 85 % (Fe + Ni) + 6 % Si + 9 % H.
In most real-world optimization problems, it is very difficult to obtain accurate analytical objective functions derived from process mechanisms. Instead, only approximate objective functions can be built based on spa...
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ISBN:
(纸本)9781509042401
In most real-world optimization problems, it is very difficult to obtain accurate analytical objective functions derived from process mechanisms. Instead, only approximate objective functions can be built based on sparse historical data. Performance optimization of fused magnesium furnaces is a typical small data-driven optimization problem, where only very limited and noisy data is available. A surrogate-assisted data-driven evolutionary algorithm is proposed in this paper for off-line data-driven optimization of furnaces performance in magnesia production. The multiobjective evolutionary algorithm is assisted by Gaussian process models to search for Pareto optimal solutions. To generate new data samples in surrogate management, a low-order polynomial model is constructed as an approximate mechanism model that can be treated as the real fitness function. To verify the effectiveness of the proposed Gaussian process assisted evolutionary algorithm, it is first tested on nine benchmark problems in comparison with a popular multi-objective evolutionary algorithm and a surrogate-assisted evolutionary algorithm. The algorithm is then applied to a real-world fused magnesium furnaces optimization problem.
The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity...
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The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity sources. However, only the optimization of pump scheduling (where pumps are controlled based on times) and the optimization of simple controls (where pumps are controlled based on one condition only, e.g. the level of one tank) were previously able to be used in the EPANET2 toolkit. This paper uses a modified version of the hydraulic solver EPANET2 that enables rule-based controls (i.e. controls based on more than one condition) to be automatically changed by an optimization algorithm. This modification is particularly useful in cases where the pump operations need to be decided based on multiple conditions: typical examples are the cases where the pumps are controlled according to the water levels of multiple tanks or when both tank levels and time of day are taken into account to reduce the pumping in the peak tariff period. The new toolkit, called ETTAR (EPANET2 Toolkit to Alter Rules), is applied to a large case study, where different types of pump operations will be tested. Results show that the optimization of rule-based controls can decrease operational costs while guaranteeing robust pump controls.
Additive Metal Deposition (AMD) is an additive manufacturing process building parts based on a nozzle-fed powder by laser assisted solidification. The AMD technology offers unique advantages for the production of near...
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Additive Metal Deposition (AMD) is an additive manufacturing process building parts based on a nozzle-fed powder by laser assisted solidification. The AMD technology offers unique advantages for the production of near net-shape parts. In contrast to the powder bed-based technologies it provides a high productivity grade. Today AMD lacks reproducible process strategies manufacturing large parts in narrow tolerances. The building height of a single layer and the geometrical shape of a whole part alter progressively with increasing part dimensions - consecutively leading to a higher effort in the manufacturing-process development for such parts. To reduce this effort, in this paper first an iterative identification of optimal process parameters is performed by following an evolutionary algorithm under varied BC. Based on the geometry-related parameter sets, tolerances are defined. The process strategies and tolerances are validated for a prototype application considering the defined quality aims. Finally the results are discussed and summarized in an a-priori process design guideline for AMD Ti6Al4V-parts. (C) 2016 The Authors. Published by Elsevier B.V.
The assessment of a building's energy performance as a design factor in the early design stages is a very demanding and complex procedure. Over the last decades, a number of tools and methods have been developed t...
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The assessment of a building's energy performance as a design factor in the early design stages is a very demanding and complex procedure. Over the last decades, a number of tools and methods have been developed to address performance-related design questions, mostly using Multi-Objective Optimization algorithms. Parametric modelling offers dynamic control over geometry and components, allowing the designer to assess multiple variants at the same time. In this paper, a new design workflow methodology is proposed, integrating evolutionary algorithms and energy simulation through Grasshopper for Rhinoceros 3d, for a comprehensive exploration of performance-based design alternatives in the building scale.
Optimization plays a key role in MEMS design. However, most MEMS design optimization (exploration) methods either depend on ad-hoc analytical / behavioural models or time consuming numerical simulations. Surrogate mod...
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ISBN:
(纸本)9783981537079
Optimization plays a key role in MEMS design. However, most MEMS design optimization (exploration) methods either depend on ad-hoc analytical / behavioural models or time consuming numerical simulations. Surrogate modeling techniques have been introduced to integrate generality and efficiency, but the number of design variables which can be handled by most existing efficient MEMS design optimization methods is often less than 5. To address the above challenges, a new method, called Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) is proposed. The key idea is the proposed ON-LINE adaptive surrogate model assisted optimization framework. In particular, AGDEMO performs global optimization of MEMS using numerical simulation and the differential evolution (DE) algorithm, and a Gaussian process surrogate model is constructed ONLINE to predict the results of expensive numerical simulations. AGDEMO is tested by two actuators (both with 9 design variables). Comparisons with state-of-the-art methods verify advantages of AGDEMO in terms of efficiency, optimization capacity and scalability.
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