Numerical investigation on the aerodynamic characteristics of an optimised NACA0012 aerofoil in-ground effect (IGE) has been performed. Gradient-based shape optimisation was carried out using the ANSYS® 19.0 Adjoint Solver to augment lift over drag ratio (L/D) by at least 10%, at various heights and angles of attack. SST k-ω turbulence model was chosen for the simulations, after its validation for out-of-ground effect (OGE) and performing wind tunnel tests for IGE. While the desired target of 10% increase in the performance parameter was easily achieved through optimisation at low angles of attack (α < 6°), the frozen turbulence assumption in Adjoint Solver limited large shape alterations at higher angles of attack. Upper surface of the aerofoil had larger changes from original camber when compared to the lower surface. Also, the optimised profiles had significant modifications towards x/c ≥ 0.8. This signifies the suitability of trailing edge morphing for such applications.
Diya AP, Harsha A, Jaison J. Image Encryption Using Chaotic Map And Related Analysis. International Conference on Advances in Computing and Communications (ICACC), doi: 10.1109/ICACC-202152719.2021.9708189 , pp. 1-5, . 2021.
Aishwarya V, Afaf M, Ann J, Chinju MR, Jaison J. LoRa Based Wireless Network for Disaster Rescue Operations. International Conference on Advances in Computing and Communications (ICACC), doi: 10.1109/ICACC-202152719.2021.9708218. 2021:pp. 1- 7 .
In the field of transportation planning and management, passenger flow analysis is a significant problem with a wide range of applications. The prediction performance of forecast models is hence cardinal to any software analytic system. A predominant source of metro data is the automated fare card (AFC) system from which it is possible to gather a tremendous amount of information connected to passenger flow. Passenger flow represents a process whose dynamics are highly stochastic and dependent on a number of extrinsic and intrinsic parameters. This paper presents a restricted and simple model to study the intrinsic statistical influences governing the dynamics. These influences are either spatial or temporal. The feature space in which analysis algorithms run will be more effective if there is a collation of information from both spatial and temporal dimensions. The passenger flow parameter is fed into the layers of the deep neural network using the ST-LSTM (Spatio-Temporal Long Short-Term Memory) architecture. The architecture is evaluated with passenger movement data collected from the AFC information from the Kochi metro rail. To reduce the impact of irregular flow, the design uses the SVM-based outlier detection and elimination algorithm. A higher precision has been reached by the approach in comparison with SVR,ANN, LSTM algorithms.
