Interactive design exploration for constrained meshes

In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm.We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately.Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces.     

Synergistic effect of co-alloying elements on site preferences and elastic properties of Ni3Al: A first-principles study

The site preferences of co-alloying elements (Mo–Ta, Mo–Re, Mo–Cr) in Ni₃Al are studied using first-principles calculations, and the effects of these alloying elements on the elastic properties of Ni₃Al are evaluated by elastic property calculations. The results show that the Mo–Ta, Mo–Re and Mo–Cr atom pairs all prefer Al–Al sites and the spatial neighbor relation of substitution sites almost has no influence on the site preference results. Furthermore, the Young's modulus of Ni₃Al increases much higher by substituting Al–Al sites with co-alloying atoms, among which Mo–Re has the best strengthening effect. The enhanced chemical bondings between alloying atoms and their neighbor host atoms are considered to be the main strengthening mechanism of the alloying elements in Ni₃Al.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

A conceptualization of intended learning outcomes supporting self‐regulated learners in indicating learning paths

Intended learning outcomes (ILOs) indicate what learners will be able to achieve after they are taught. Traditionally, ILOs are expressed as plain text or unstructured documents. What if all ILOs of a specific course of study can be conceptualized through a structured diagrammatic technique? It was hypothesized that learners can benefit from this conceptualization in learning, especially in self‐regulated learning. The aim of this study was to investigate whether the ILOs represented in unstructured or structured formats can facilitate learners to identify learning paths. The results revealed that the mean ratings of all learning paths were statistically significantly higher with structured ILOs.     

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments.     

A design of experiments investigation into dry separation using a Knelson Concentrator

Enhanced gravity, or centrifugal, separators have revolutionised gold processing over the past decades, significantly increasing the recovery of fine (−100 μm) free gold. One of the main drawbacks of centrifugal gravity concentrators is the large volume of water required (even if it is all recycled). With water becoming an ever increasingly important “commodity”, reducing this is of importance both from an environmental and a monetary point of view. This work investigated operating a laboratory scale Knelson Concentrator with a dry feed and using air as the fluidising medium. The feed used was a synthetic mixture of tungsten and quartz, used to mimic a gold ore. The response surface method and central composite design techniques were used to design the experiments and to model the results, with the experimental variables being the bowl speed (G-Level), air fluidising pressure and the feed rate. The models corresponded well to the experimental results, indicating that for this experimental setup, the optimal conditions were a bowl G-Level of 40 G, a feed rate of 220 g/min and an air fluidising pressure of 8 psi.     

Material design and development: From classical thermodynamics to CALPHAD and ICME approaches

This paper presents an overview and examples of material design and development using (1) classical thermodynamics; (2) CALPHAD (calculation of phase diagrams) modeling; and (3) Integrated Computational Materials Engineering (ICME) approaches. Although the examples are given in lightweight aluminum and magnesium alloys for structural applications, the fundamental methodology and modeling principles are applicable to all materials and engineering applications. The examples in this paper have demonstrated the effectiveness and limitations of classical thermodynamics in solving specific problems (such as nucleation during solidification and solid-state precipitation in aluminum alloys). Computational thermodynamics and CALPHAD modeling, when combined with critical experimental validation, have been used to guide the selection and design of new magnesium alloys for elevated-temperature applications. The future of material design and development will be based on a holistic ICME approach. However, key challenges exist in many aspects of ICME framework, such as the lack of diffusion/mobility databases for many materials systems, limitation of current microstructural modeling capability and integration tools for simulation codes of different length scales.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

Improved method for roadside barrier length of need modeling using real-world trajectories

The 2011 AASHTO Roadside Design Guide (RDG) contains perhaps the most widely used procedure for choosing an appropriate length of need (LON) for roadside barriers. However, this procedure has several limitations. The procedure uses a highly simplified model of vehicle departure, and the procedure does not allow designers to specify an explicit level of protection. A new procedure for choosing LON that addresses these limitations is presented in this paper. This new procedure is based on recent, real-world road departure trajectories and uses this departure data in a more realistic way. The new procedure also allows LON to be specified for a precisely known level of protection – a level which can be based on number of crashes, injury outcomes or even estimated crash cost – while still remaining straightforward and quick to use like the 2011 RDG procedure.