Model reduction for nonlinear aerodynamics and aeroelasticity using a Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of Computational Fluid Dynamics (CFD) solvers, for fast nonlinear aerodynamic and aeroelastic modeling. A nonlinear function is identified on selected interpolation points by
a discrete empirical interpolation method (DEIM). The flow field is then reconstructed using a least square approximation of the flow modes extracted
by proper orthogonal decomposition (POD). The aeroelastic reduce order
model (ROM) is completed by introducing a nonlinear mapping function
between displacements and the DEIM points. The proposed model is investigated to predict the aerodynamic forces due to forced motions using
a N ACA 0012 airfoil undergoing a prescribed pitching oscillation. To investigate aeroelastic problems at transonic conditions, a pitch/plunge airfoil
and a cropped delta wing aeroelastic models are built using linear structural models. The presence of shock-waves triggers the appearance of limit
cycle oscillations (LCO), which the model is able to predict. For all cases
tested, the new ROM shows the ability to replicate the nonlinear aerodynamic forces, structural displacements and reconstruct the complete flow
field with sufficient accuracy at a fraction of the cost of full order CFD
model.

D4.3 Prototype Software (Documentation)

The agent-based social simulation component of the TELL ME project (WP4) developed prototype software to assist communications planners to understand the complex relationships between communication, personal protective behaviour and epidemic spread. Using the simulation, planners can enter different potential communications plans, and see their simulated effect on attitudes, behaviour and the consequent effect on an influenza epidemic.

The model and the software to run the model are both freely available (see section 2.2.1 for instructions on how to obtain the relevant files). This report provides the documentation for the prototype software. The major component is the user guide (Section 2). This provides instructions on how to set up the software, some training scenarios to become familiar with the model operation and use, and details about the model controls and output.

The model contains many parameters. Default values and their source are described at Section 3. These are unlikely to be suitable for all countries, and may also need to be changed as new research is conducted. Instructions for how to customise these values are also included (see section 3.5).

The final technical reference contains two parts. The first is a guide for advanced users who wish to run multiple simulations and analyse the results (section 4.1). The second is to orient programmers who wish to adapt or extend the simulation model (section 4.2). This material is not suitable for general users.