Articles written in Sadhana

    • Drag reduction by the introduction of shear-free surfaces in a turbulent channel flow


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      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.

    • Error tolerance for the recognition of faulty strings in a regulated grammar using fuzzy sets


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      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(|GR 2 |·|w|), where |GR| 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.

    • Experimental investigations and optimization of forming force in incremental sheet forming


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      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 (50o). 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.

    • Single and multiple odor source localization using hybrid nature-inspired algorithm


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      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.

    • Trait of subsidence under high rate of coal extraction by longwall mining: some inferences


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      Transformation in surface topography is a common phenomenon caused due to underground mining. With a view to focus outward, underground mining at a depth of 410 m although earlier considered as mothball is indispensable as on date to meet the production target. Subsidence investigation has been carried out over longwal panel no. 1 in Adriyala mine of Singareni Collieries Company Limited (SCCL) located in Godavari Valley Coalfield. The rate of face advance varied between 2.7 and 4.8 m/day. The present study envelops cementing relation of subsidence due to underground mining by longwall method with the active and old dumps, partially covered over the panel. Symmetric subsidence profile has been observed across the panel with higher angle of draw in dip side. Resettlement of dump led to higher vertical displacement and found to bean indispensable investigation for stability viewpoint. The angle of draw has been analyzed to be a controlling parameter with respect to the rate of face advance. The impact of subsidence on surface has been evaluated by constructing walls at maximum possible tensile zones and development of cracks after subsidence has been observed. Hydrogeological study has also been conducted, from seepage viewpoint, to evaluate the extent of damage in the strata for safe underground working. The investigation has been conducted during and aftermining, with and without release of canal water, to assess the influence of seepage in ground. The assorted subsidence and hydrogeological investigations can be applied to interpret the extent of damage and for comprehensiveunderstanding of the trait of cracks on the surface, in the strata and their continuity thereof.

    • Dynamic modeling of dielectric elastomer-based minimum energy structures with membrane entanglements and finite extensibility


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      The shape morphing behavior of dielectric elastomer-based minimum energy structures (DEMES) generated by combining an inextensible frame and a pre-stretched dielectric elastomer membrane is unique. The geometrical and material properties of the DE membrane and compliant frame are responsible for the DEMES actuator’s shape morphing capabilities. The internal polymer networks of the dielectric elastomer have strong perplexed entanglements and finite extensibility that alter significantly the dynamic behavior of the DE membrane. In this paper, we present a theoretical framework for investigating the impact of intrinsic entanglements and finite extensibility of the dielectric elastomer polymer networks on the DEMES actuator’s nonlinear dynamic performance. The nonlinear equation that governs the dynamic motion of the DEMES actuator is obtained by employing the least action principle-based Euler-Lagrange’s equation. The main results of the present work prove that the attained initial (equilibrium) and final configurations of the DEMES actuator arealtered appreciably by the entanglements and finite extensibility of the polymer chains of the DE membrane. A parametric investigation reveals that the initial pre-stretch associated with the DE membrane and bending stiffness of the compliant frame governs the acquired equilibrium configuration of the DEMES actuator. The framework established provides a constructive platform for integrating the microcosmic features of the DE membrane polymer chains with the macroscopic dynamic behavior of the DEMES actuator.

    • Microstrip line fed dielectric resonator antenna optimization using machine learning algorithms


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      In this communication, a microstrip line fed dielectric resonator antenna is optimized using various Machine learning-based models. Different ML algorithms such as ANN (artificial neural network), KNN (KNearest Neighbors), XG Boost (extreme gradient boosting), Random Forest, and Decision Tree are used tooptimize the proposed antenna design within the frequency band 3.3–3.65 GHz. |S11| of the proposed antenna is predicted by using various ML algorithms. Dataset for the same is created through HFSS EM (Electromagnetic) simulator by varying the radius, height of DRA (Dielectric Resonator Antenna) as well as the width of microstrip line and conformal strip. Predicted results from all these models are quite close to the actual one except ANN. To overcome the problem of ANN, Knowledge-Based Neural Network techniques (KBNN) are implemented.All these ML algorithms are authenticated by practically constructing and measuring the proposed antenna. Fabricated antenna results are in good agreement with the values predicted by ML algorithms.

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