Numerical study of the 2D lid-driven triangular cavities based on the Lattice Boltzmann method

Numerical study of two dimensional lid driven triangular cavity flow is performed via using lattice Boltzmann method on low Reynolds numbers. The equilateral triangular cavity is the first geometry to be studied, the simulation is performed at Reynolds number 500 and the numerical prediction is compared with previous work done by other scholars. Several isosceles triangular cavities are studied at different initial conditions, Reynolds numbers ranging from 100 to 3000, regardless of the geometry studied, the top lid is always moving from left to right and the driven velocity remains constant. Results are also compared with previous work performed by other scholars, the agreement is very good. According to the authors’ knowledge, this is the first time that MRT-LBM model is used to simulate the flow inside the triangular cavities. One of the advantages of this method is that it is capable of producing at low and high Reynolds numbers.Peer ReviewedPostprint (published version

Review of research in feature-based design

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization

Physicality in Australian patent law

It is generally understood that the patent system exists to encourage the conception and disclosure of new and useful inventions embodied in machines and other physical devices, along with new methods that physically transform matter from one state to another. What is not well understood is whether, and to what extent, the patent system is to encourage and protect the conception and disclosure of inventions that are non-physical methods – namely those that do not result in a physical transformation of matter. This issue was considered in Grant v Commissioner of Patents. In that case the Full Court of the Federal Court of Australia held that an invention must involve a physical effect or transformation to be patentable subject matter. In doing so, it introduced a physicality requirement into Australian law. What this article seeks to establish is whether the court’s decision is consistent with the case law on point. It does so by examining the key common law cases that followed the High Court’s watershed decision in National Research Development Corporation v Commissioner of Patents, the undisputed authoritative statement of principle in regard to the patentable subject matter standard in Australia. This is done with a view to determining whether there is anything in those cases that supports the view that the Australian patentable subject matter test contains a physicality requirement

Yang-Baxter Equations, Computational Methods and Applications

Computational methods are an important tool for solving the Yang-Baxter equations(in small dimensions), for classifying (unifying) structures, and for solving related problems. This paper is an account of some of the latest developments on the Yang-Baxter equation, its set-theoretical version, and its applications. We construct new set-theoretical solutions for the Yang-Baxter equation. Unification theories and other results are proposed or proved.Comment: 12 page

The Dawn of Fully Automated Contract Drafting: Machine Learning Breathes New Life Into a Decades-Old Promise

Technological advances within contract drafting software have seemingly plateaued. Despite the decades-long hopes and promises of many commentators, critics doubt this technology will ever fully automate the drafting process. But, while there has been a lack of innovation in contract drafting software, technological advances have continued to improve contract review and analysis programs. “Machine learning,” the leading innovative force in these areas, has proven incredibly efficient, performing in mere minutes tasks that would otherwise take a team of lawyers tens of hours. Some contract drafting programs have already experimented with machine learning capabilities, and this technology may pave the way for the full automation of contract drafting. Although intellectual property, data access, and ethical obstacles may delay complete integration of machine learning into contract drafting, full automation is likely still viable