Karl-Eerik Unt | Searcharxiv

arXiv2026

Advancing Threshold-Inception Modeling for Predictive Simulation of Ionic Wind Fan Performance

This study investigates the predictive capability of a threshold inception-based multiphysics modeling approach for ionic wind fans by direct comparison with experimental measurements. A wire-to-cylinder electroaerodynamic (EAD) fan with variable electrode spacing is used as a reference system to assess the model's ability to reproduce airflow characteristics, discharge current, and performance trends under atmospheric conditions. Numerical simulations show good qualitative agreement with experimental results across all tested configurations; however, systematic deviations emerge at higher voltages and larger electrode gaps. Analysis of these discrepancies indicates that the commonly adopted assumption of perfectly smooth emitter surfaces can limit model accuracy. Experimental characterization of the emitter wire reveals micro-scale surface protrusions, which locally enhance the electric field and alter corona inception behavior. Incorporating representative surface roughness into the numerical model improves quantitative agreement with measured airflow velocities. The results demonstrate that while the threshold inception model provides a robust foundation for EAD fan simulations, electrode surface morphology is a critical factor for reliable prediction. This work advances the validation and refinement of ionic wind fan modeling methodologies and identifies key considerations for the development of more accurate engineering-oriented simulation tools.

physics.comp-ph↗

arXiv2025

$π$-MPPI: A Projection-based Model Predictive Path Integral Scheme for Smooth Optimal Control of Fixed-Wing Aerial Vehicles

Model Predictive Path Integral (MPPI) is a popular sampling-based Model Predictive Control (MPC) algorithm for nonlinear systems. It optimizes trajectories by sampling control sequences and averaging them. However, a key issue with MPPI is the non-smoothness of the optimal control sequence, leading to oscillations in systems like fixed-wing aerial vehicles (FWVs). Existing solutions use post-hoc smoothing, which fails to bound control derivatives. This paper introduces a new approach: we add a projection filter $π$ to minimally correct control samples, ensuring bounds on control magnitude and higher-order derivatives. The filtered samples are then averaged using MPPI, leading to our $π$-MPPI approach. We minimize computational overhead by using a neural accelerated custom optimizer for the projection filter. $π$-MPPI offers a simple way to achieve arbitrary smoothness in control sequences. While we focus on FWVs, this projection filter can be integrated into any MPPI pipeline. Applied to FWVs, $π$-MPPI is easier to tune than the baseline, resulting in smoother, more robust performance.

cs.RO↗