Purpose Impinging jets have been widely studied, and the addition of swirl has been found to be beneficial to heat transfer. As there is no literature on Reynolds-averaged Navier Stokes equations (RANS) nor experimental data of swirling jet flows generated by a rotating pipe, the purpose of this study is to fill such gap by providing results on the performance of this type of design. Design/methodology/approach As the flow has a different behaviour at different parts of the design, the same turbulent model cannot be used for the full domain. To overcome this complexity, the simulation is split into two coupled stages. This is an alternative to use the costly Reynold stress model (RSM) for the rotating pipe simulation and the SST k-ω model for the impingement. Findings The addition of swirl by means of a rotating pipe with a swirl intensity ranging from 0 up to 0.5 affects the velocity profiles, but has no remarkable effect on the spreading angle. The heat transfer is increased with respect to a non-swirling flow only at short nozzle-to-plate distances H/D < 6, where H is the distance and D is the diameter of the pipe. For the impinging zone, the highest average heat transfer is achieved at H/D = 5 with swirl intensity S = 0.5. This is the highest swirl studied in this work. Research limitations/implications High-fidelity simulations or experimental analysis may provide reliable data for higher swirl intensities, which are not covered in this work. Practical implications This two-step approach and the data provided is of interest to other related investigations (e.g. using arrays of jets or other surfaces than flat plates). Originality/value This paper is the first of its kind RANS simulation of the heat transfer from a flat plate to a swirling impinging jet flow issuing from a rotating pipe. An extensive study of these computational fluid dynamics (CFD) simulations has been carried out with the emphasis of splitting the large domain into two parts to facilitate the use of different turbulent models and periodic boundary conditions for the flow confined in the pipe.

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Swirling turbulent flows created by the rotation of pipes and applications for heat transfer can have interesting industrial purposes. As such physical phenomenon is under some physical uncertainties, it is interesting to understand their impact on the relevant parameters. In the present paper, the Stochastic Collocation method with Sparse Grids is developed, in order to study how the uncertainties are propagated from the Computational Fluid Dynamics (CFD) simulations of a Swirling jet created by the rotation of a pipe to the CFD simulations of the heat transfer from a flat plate by the impingement of the generated swirling jet. In addition, some mathematical models for the velocity profile and turbulent parameters are given, and their uncertainties studied, in order to facilitate this two-step process for industrial applications.

A classic approach to computational fluid dynamics is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study. To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.

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In real-life mechanical engineering applications, it is often complex to achieve an optimal multi-objective design, because of the costs related to fabrication and test of different prototypes. For this reason, the use of computational tools is a recommended practice. In this work, the design of an efficient mixing mechanical device composed of a rectangular pillar confined in a microchannel is aided by machine learning techniques (addressed as machine learning-aided design optimization, MLADO, proposed in this work). A random forest classifier is trained to predict which geometric configuration may lead to vortex shedding. Later, a multi-objective optimization problem is investigated, which consists of minimizing the required pumping power and maximizing the mixing efficiency under some design constrains. If extra training data are desired for surrogates, the random forest classifier can be used to predict whether it is worthy or not to simulate the new configuration, avoiding to run irrelevant computational intensive cases and accelerating the data-driven process. The resulting optimal designs from using the NSGA-II genetic algorithm on the surrogates are simulated, and their performance is shown. The optimal geometric configurations, even for very unfavorable mixing conditions and a medium-low Reynolds number of 200, give a maximum mixing efficiency of around 50% at very low pumping power cost in a short channel, outperforming existing devices in the literature. The MLADO framework followed in this work can be easily extendable and automated for other similar design processes in mechanical engineering at any scale, by including shape parameterization strategies.

Heat exchangers are widely used in many mechanical, electronic, and bioengineering applications at macro and microscale. Among these, the use of heat exchangers consisting of a single fluid passing through a set of geometries at different temperatures and two flows in T-shape channels have been extensively studied. However, the application of heat exchangers for thermal mixing over a geometry leading to vortex shedding has not been investigated. This numerical work aims to analyse and characterise a heat exchanger for microscale application, which consists of two laminar fluids at different temperature that impinge orthogonally onto a rectangular structure and generate vortex shedding mechanics that enhance thermal mixing. This work is novel in various aspects. This is the first work of its kind on heat transfer between two fluids (same fluid, different temperature) enhanced by vortex shedding mechanics. Additionally, this research fully characterise the underlying vortex mechanics by accounting all geometry and flow regime parameters (longitudinal aspect ratio, blockage ratio and Reynolds number), opposite to the existing works in the literature, which usually vary and analyse blockage ratio or longitudinal aspect ratio only. A relevant advantage of this heat exchanger is that represents a low-Reynolds passive device, not requiring additional energy nor moving elements to enhance thermal mixing. This allows its use especially at microscale, for instance in biomedical/biomechanical and microelectronic applications.