Miguel Alfonso Mendez

The extraordinary aerial agility of hummingbirds and insects continues to inspire the design of flapping-wing drones. To replicate them, computational fluid dynamics (CFD) simulations must be coupled with rigid body dynamics solvers. While OpenFOAM® provides a general articulated-body algorithm (ABA) for these multiphysics simulations, its current implementation is restricted to passive joints and lacks thorough verification for free-flight applications. This work addresses these limitations by introducing an extension of the ABA to support active joints with prescribed kinematics, allowing the body dynamics and the imposed motions to be solved simultaneously. The imposed motion is implemented in a new imposedMotion library that directly prescribes joint positions, velocities, and accelerations. The proposed approach is verified using a hierarchical strategy, including analytical benchmarks, comparison with quasi-steady aerodynamic models, and grid- and time-integration sensitivity analyses. Results demonstrate the robustness of the proposed framework and its ability to capture body–wing inertial coupling effects, contributing to the OpenFOAM® community with an extensible open-source platform for simulating partially actuated multibody systems in unsteady flows.

Ejectors are passive devices used in refrigeration, propulsion and process industries to compress a flow without moving parts by exploiting the energy released during the expansion of another stream. The engineering modelling of choking in these devices remains an open question, with two mechanisms – Fabri and compound choking – proposed in the literature. This work develops a quasi-one-dimensional framework based on common governing equations, enabling a direct comparison between the two using axisymmetric Reynolds–averaged Navier–Stokes (RANS) simulations as reference. The compound formulation incorporates wall and inter-stream friction and a local pressure-equalisation procedure that enables stable integration through the sonic point, together with a normal-shock reconstruction. The Fabri formulation is assessed by imposing the dividing streamline extracted from RANS, isolating the sonic condition while avoiding additional modelling assumptions. The calibrated compound model predicts on-design secondary mass flow typically within 2 % of the RANS simulations, rising to 5 % for a strongly under-expanded primary jet due to the equal-pressure constraint. The Fabri analysis attains less than 1 % error in on-design entrainment, but exhibits high sensitivity to the dividing streamline and closure, which limits predictive use beyond on-design. Overall, the results show that Fabri and compound mechanisms can coexist within the same device and operating map, each capturing distinct aspects of the physics and offering complementary modelling value. Nevertheless, compound choking emerges as the more general mechanism governing flow rate blockage, as evidenced by choked flows with a subsonic secondary stream.

Decarbonizing energy-intensive and other hard-to-abate sectors remains essential for meeting climate targets, yet these sectors continue to depend on fuels for high-temperature heat, chemical feedstocks, and energy-dense transport applications. This roadmap assesses the potential role of renewable synthetic fuels (RSFs) in supporting this transition, with a primary focus on the European context and on sectors including steel, cement, glass, aviation, shipping, and power generation. It reviews the current status of RSF combustion science, the high-fidelity experimental and numerical data needed to understand fuel behavior under relevant conditions, and the emerging hybrid physics-based and data-driven modeling approaches that can accelerate progress. Particular attention is given to digital twins and related cyber-physical tools as enabling technologies for process optimization, monitoring, scale-bridging, and industrial implementation. The roadmap also examines practical deployment barriers, including fuel availability, infrastructure, safety, regulatory frameworks, policy barriers, and data integration challenges. Developed in the framework of the CYPHER COST Action, it highlights that large-scale RSF deployment will require coordinated action across research, industry, and policy, together with stronger business cases, demand-stimulating policies, and workforce development.

Constrained radial basis function (RBF) regression has recently emerged as a powerful meshless tool for reconstructing continuous velocity fields from scattered flow measurements, particularly in image-based velocimetry. However, existing formulations based on isotropic kernels often suffer from spurious oscillations in regions with sharp gradients or strong flow anisotropy. This work introduces an anisotropic, gradient-informed, and adaptively sampled extension of the constrained RBF framework for regression of scattered data. Gradient information is estimated via local polynomial regression at collocation points, smoothed, and used to (1) re-sample data, maximizing sampling density near steep gradients while downsampling in smooth regions, and (2) construct a local anisotropic metric that shapes each basis function according to the flow directionality. In addition, a gradient-informed regularization is introduced by embedding observed gradients into the least-squares system as weighted soft constraints. The resulting formulation is fully meshless, linear, and computationally efficient, while significantly improving reconstruction quality in challenging regions. The method is evaluated on both synthetic and experimental datasets, including direct numerical simulation (DNS) data of a turbulent channel and time-resolved particle tracking velocimetry of a turbulent jet. Results show that the proposed approach outperforms isotropic and gradient-free RBF formulations in accuracy, smoothness, and physical consistency---particularly near shear layers and boundaries---while reducing the number of bases by an order of magnitude. To support the application, we have created a repository (https://github.com/mendezVKI/SPICY_VKI) that provides access to the investigated datasets.

This chapter opens with a review of classic tools for regression, a subset of machine learning that seeks to find relationships between variables. With the advent of scientific machine learning this field has moved from a purely data-driven (statistical) formalism to a constrained or ``physics-informed'' formalism, which integrates physical knowledge and methods from traditional computational engineering. In the first part, we introduce the general concepts and the statistical flavor of regression versus other forms of curve fitting. We then move to an overview of traditional methods from machine learning and their classification and ways to link these to traditional computational science. Finally, we close with a note on methods to combine machine learning and numerical methods for physics