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Purpose This paper aims to introduce physics-informed neural networks (PINN) applied to the two-dimensional steady-state laminar Navier–Stokes equations over a flat plate with roughness elements and specified local heating. The method bridges the gap between asymptotics theory and three-dimensional turbulent flow analyses, characterized by high costs in analysis setups and prohibitive computing times. The results indicate the possibility of using surface heating or wavy surface to control the incoming flow field. Design/methodology/approach The understanding of the flow control mechanism is normally caused by the unsteady interactions between the aircraft structure and the turbulent flows as well as some studies have shown, surface roughness can significantly influence the fluid dynamics by inducing perturbations in the velocity profile. Findings The description of the boundary-layer flow, based upon a triple-deck structure, shows how a wavy surface and a local surface heating generate an interaction between the inviscid region and the viscous region near the flat plate. Originality/value To the best of the authors’ knowledge, the presented approach is especially original in relation to the innovative concept of PINN as a solver of the asymptotic triple-deck method applied to the viscous–inviscid boundary layer interaction.

The study presents a numerical investigation of aerodynamic drag reduction by implementing a counterflow plasma jet, emanating from the stagnation point of an aerodynamic surface in a supersonic regime with a constant pressure ratio PR = 3 , and compares findings with a conventional opposing jet. The computational study is carried out by solving three-dimensional and axisymmetric Navier–Stokes equations for counterflow plasma-jet interaction. The calculations are performed at free-stream Mach ( M ∞ = 1.4) with sea level stagnation conditions. The weakly ionized argon plasma jet generated by a plasma torch has constant stagnation pressure and temperature of 303,975 Pa and 3000 K . The effect of the Mach number and the angle of attack variation on plasma-jet effectiveness is also analyzed. The results indicate that the counterflow plasma jet reduces more drag (in twice) compared to the conventional jet (nonplasma). The gravitational, magnetic field effect and chemical processes in the plasma formation are considered negligible. It is inferred that the effectiveness of the counterflow plasma jet strongly depends upon the jet stagnation temperature.

Infrastructure selection, design and planning play a pivotal role in creating a safe travel environment for road users, especially the vulnerable road user. In this work, it is aimed to develop a predictive intelligent safety model for the riskiest cyclist infrastructure, based upon the prevalent environment, traffic flow conditions, and specific users using the infrastructure; and also develop an understanding of how these factors affect safety alone and in combination with each other. The study area of Northumbria in the northeast of England is selected for investigation. A hybrid methodology is proposed: a) Crash data collection, b) Predictive model (deep learning), and c) Variable interaction model (deep learning variable importance and principal component analysis). A complex deep learning model with a neural network classifier, and backpropagation error function is used to model this complex and nonlinear relationship. An accurate model is developed with an average accuracy of 86. Through variable interaction, it is found that critical variables affecting safety are the riders age, gender, environmental conditions, sudden change in the road hierarchy, and the traffic flow regime. It is found that the adverse environmental conditions and different traffic flow regimes complicate the cyclist interactions, having varied safety implications for different infrastructure types. The traffic flow regime poses a varying level of risk to the cyclist to which riders belonging to different genders react differently. The traffic flow conditions and the infrastructure variables alone are critical variables affecting the safety of cyclists. The study results help develop a better understanding of risk variation for different infrastructure types and predict the riskiest infrastructure type. It will contribute towards better planning of the cyclist infrastructure and thus contribute towards the development of a sustainable transportation system

The paper discusses the effect of compressor characteristic on surge phenomena in axial flow compressors. Specifically, the effect of nonlinearities on the compressor dynamics is analyzed. For this purpose, generalized multiple time scales method is used to parameterize equations in amplitude and frequency explicitly. The pure surge case of the famous Moore-Greitzer model is used as the basis of the study. The compressor characteristic used in the Moore-Greitzer model is generalized to evaluate the effect of the parameters involved. Subsequently, bifurcation theory is used to study the effect of nonlinear dynamics on surge behavior. It has been found that the system exhibits supercritical Hopf bifurcation under specific conditions in which surge manifests as limit cycle oscillations. Key parameters have been identified in the analytical solution which govern the nonlinear dynamic behavior and are responsible for the existence of limit cycle oscillations. Numerical simulations of the Moore-Greitzer model are carried out and found to be in good agreement with the analytical solution.

The use of second- and third-order classical piston theory [1] (CPT) is commonplace, with the role of the higher-order terms being well understood [2]. The advantages of local piston theory (LPT) relative to CPT have been demonstrated previously [3]. Typically, LPT has been used to perturb a mean-steady solution obtained from the Euler equations, and recently, from the Navier-Stokes equations [4]. The applications of LPT in the literature have been limited to first-order LPT [5–7]. The reasoning behind this has been that the dynamic linearization used assumes small perturbations. The present note clarifies the role of higher-order terms in LPT. It is shown that second-order LPT makes a non-zero contribution to the normal-force prediction, in contrast to second-order CPT.

A near real-time optimal trajectory planning framework for UAVs is presented and tested in a series of low altitude obstacle avoidance planning scenarios. The framework uses the Inverse Dynamics Trajectory Optimisation approach with a quaternion point-mass aircraft dynamic model and a hybrid Differential Evolution and Sequential Quadratic Programming based Interior-Point optimisation strategy.\ud \ud It was found that the new framework was able to successfully find a feasible (if not optimal) trajectory and to do so as efficiently as possible. However, it was also concluded that at this stage the framework is not yet fit to be used on a UAV, as the framework tends to take longer to plan a trajectory than it takes the UAV to fly it.\ud \ud Ultimately it was concluded that with some further work, the Hybrid framework could be a viable near real-time trajectory planner for UAVs.