In this paper, a novel technique for drag reduction in turbulent flows is presented. The technique involves the modification of the large scales of turbulent flows and is a passive approach. The lateral transport of momentum, which is a dominant mechanism in turbulence, is attenuated by the introduction of moving shearfree surfaces (SFSes). This brings about a reduction in the drag. 2D simulations have been carried out for aturbulent channel flow using shear stress transport (SST) Reynolds-averaged Navier–Stokes (RANS) model and validated with the available experimental results. The interaction between the plates and the fluid is two way,and is enforced either by the use of a rigid body solver with moving mesh, or by considering the SFSes to befixed at particular locations and then updating the velocities of the plates at those locations. The latter is equivalent to solving a fully developed flow in the moving mesh case. The number, shape, size and placement of the SFSes strongly influence the amount of drag reduction. The phenomenon is confirmed to be governed by a

‘slow’ turbulent time scale. Further, the efficacy of the method is seen to depend on the ratio of two time scales – an advection time scale indicating the ‘resident time’ near an SFS, and the turbulent time scale. In addition, the effectiveness of the approach is improved by judicious placement of multiple SFSes in the flow.

To overcome the limitations of context-free and context-sensitive grammars, regulated grammars have been proposed. In this paper, an algorithm is proposed for the recognition of faulty strings in regulated grammar. Furthermore, depending on the errors and certainty, it is decided whether the string belongs to thelanguage or not based on string membership value. The time complexity of the proposed algorithm is O(|G_R ² |·|w|), where |G_R| represents the number of production rules and |w| is the length of the input string, w. The reader isprovided with numerical examples by applying the algorithm to regularly controlled and matrix grammar. Finally, the proposed algorithm is applied in the Hindi language for the recognition of faulty strings in regulated grammar as a real-life application.

Incremental sheet forming process has been proved to be quiet suitable and economical for job and batch type production, which exempts expensive and complex tooling for sheet forming. Investigation of forming forces becomes important for selecting the appropriate hardware and optimal process parameters in order to assure perfection and precision of process. Moreover, lack of available knowledge regarding the process parameters makes the process limited for industrial applications. This research paper aims at finding out effectsof different input factors on forming forces in single-point incremental forming (SPIF) process. For operation sustainability and hardware safety, it becomes critical to optimize forming forces for a given set of factors to form a particular shape. In this study, optimization of input factors has been performed to produce conical frustums with helical tool path using Taguchi analysis as design of experiment (DOE) and analysis of variance (ANOVA). The optimal experimental conditions for forming forces have been calculated as sheet thickness(0.8 mm), step size (0.2 mm), tool diameter (7.52 mm), tool shape (hemispherical), spindle speed (1000 rpm), feed rate (1000 mm/min) and wall angle (50^o). Effects of tool shape and viscosity of lubricants have also been investigated. An intensive understanding of the mechanism of forming forces has been presented, which shows that force trend after peak values depends upon instant input factors that can be categorized as a safe, severe and crucial set of parameters.

In this paper, optimization-based approach has been adopted to localize the odor source in an unknown environment. Two scenarios taken into consideration, first single odor source (SOS) with a point source emission at a constant rate and four multiple odor sources (MOS) with point source emissions and different release rates constant in time. In context to SOS, four environments that have distinct dimensional layout have been generated with slight variation in wind velocity and diffusion constant. In case of MOS, there are five environments with same layout but different contributing factors such as wind velocity, placement ofodor sources and emission rates which are considered to demonstrate its impact on success rate of algorithms. A recent optimization technique called hybrid teaching learning particle swarm optimization (HTLPSO) has been adopted and implemented in all the arenas, namely SOS and MOS, where mobile robots AKA virtual agents (VAs) are working in collaboration. There are group of VAs deployed in this operation ranging from {3–15}. To investigate the effectiveness of the algorithm, results of HTLPSO are compared with classical particle swarmoptimization (PSO) and teaching learning-based optimization (TLBO). It is observed that HTLPSO outperforms TLBO and PSO in arenas with larger dimensions while utilizing few iterations in comparison with other algorithms in case of SOS. HTLPSO also performs best in case of MOS, surviving the effect of wind velocity and change in emission rates. Only when odor sources are placed differently and scattered, TLBO gives the best result. Another highlight of HTLPSO is convergence with high accuracy even with less number of VAs.