Lightweight Material Optimization of Aquatic Vehicles’ Propeller Based on Fatigue Life Using Hydro Structural Interaction Simulation

Generally, inward and outward effects are huge and prime in the rotating components. Based on the working environments of a rotor, the complexity is increased furthermore. Similarly, this work also deals the complicated problem, which is fatigue life estimation of Marine Vehicles’ propeller for different lightweight materials under given Ocean environments by using Ansys Fluent 16.2. The conceptual design of the ship propeller is modeled with the help of CATIA. Fatigue life estimation on the rotor is a key and complex output of this work, so advanced methodology is mandatory for computation. For that purpose, the following advanced methodology has been implemented for this work, which is Hydro Structural Interaction (HSI) and Moving Reference Frame (MRF) techniques are associated in Computational Fluid Dynamics (CFD). Hydro-Fluid properties such as density and operating pressure are used as per the working vehicles’ environment, which has been easily, defined in Ansys Fluent 17.2. Thus this computational platform is perfect to handle hydrodynamic simulations, even though the gird convergence study is conducted for the better outcomes. In the case of structural simulation, the existing materials such as Aluminium alloy and Stainless Steel are used for fatigue life estimation under HSI loading conditions. Finally, the fatigue life estimation of Marine Vehicles’ propeller is extended for composite materials to compare the life of a rotor. Both the Hydrostatic and Hydrodynamic loading conditions are tested on Aquatic Vehicle’s rotor and thereby the suitable material is chosen and given to the future input for real-time applications.

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structural Interaction Methodology

A tightly coupled fluid-structural interaction (FSI) methodology is developed for aeroelasticity analysis in the time domain. The preconditioned Navier–Stokes equations for all Mach numbers are employed and the structural equations are tightly coupled with the fluid equations by discretizing their time derivative term in the same pseudo time-stepping method. A modified mesh deformation method based on reduced control points radial basis functions (RBF) is utilized, and a RBF based mapping algorithm is introduced for data exchange on the interaction interface. To evaluate the methodology, the flutter boundary and the limit cycle oscillation of Isogai wing and the flutter boundary of AGARD 445.6 wing are analyzed and validated.

Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.