Many thermal applications involve fluid flow across the sharp bend. The fluid flow across the sharp bend causes entire modification due to back pressure and flow separation, etc. Quantifying pressure across the sharp turn due to the modification of flow behaviour is always a complex challenge. One of the difficulties associated with the intrusive techniques of pressure measurements is to disturb the flow and the lack of pressure field estimation in the entire domain. This article presents a complete methodology for pressure estimation before and at the sharp bend using stereo-particle image velocimetry (PIV) data. The method relies on knowledge of the PIV velocity field, which is used to calculate the pressure field analytically and numerically. The outcomes of the study show that how the sharp bend in the fluid path results in a sharp pressure drop. Pressure at the entry of the bend is found to be ~5 Pa (absolute), and at the exit side, it drops to ~3 Pa for a turbulent in-flow condition (Reynolds number = 5500). The time-averaged streamlines are also superimposed over velocity magnitude and pressure data for a better understanding of flow and pressure simultaneously.

The present study deals with the fundamentals of vortex evolution and merging across a 180° sharp bend for laminar inflow (Re = 800). A three-dimensional flow and pressure distribution across the bend are analyzed by using particle image velocimetry measurements. The results show that the sudden flow redirection and adverse pressure gradient create a recirculation region in the vicinity of the divider wall. The vorticity and turbulent kinetic energy distribution highlight the presence of a shear layer between the recirculation and mainstream turning flow. A Rayleigh instability analysis shows the existence of centrifugal instabilities outside the shear layer in the mainstream turning flow. The imbalance of centrifugal and pressure forces act in unison to produce a net force that creates Dean instability across the mainstream turning flow. Consequently, two pairs of counter-rotating Dean vortices develop after the first turn, possibly merging into a single pair after the subsequent 90° turn. Finally, the evolution and merging of Dean vortices are explained with the help of a conceptual diagram illustrating the force interaction and the formation of counter-rotating currents.

Efficient life-cycle bridge asset management delineates a planning optimization problem of paramount importance for the operational reliability of transportation infrastructure. It necessitates adept inspection and maintenance policies able to reduce risks and costs while incorporating long-term stochastic deterioration models, inference under uncertain structural health data, and various probabilistic and deterministic constraints. Structural integrity management policies for individual bridges, which are mere constituents of broader complex networks, cannot be devised in isolation of the policies of other system components, such as other bridges and pavement sections, and without considering system functions and traffic considerations. Such network effects render the optimization problem even harder to solve. Currently, age- or condition-based maintenance techniques, as well as risk-based or periodic inspection plans, have been used to address this class of challenging optimization problems. However, the efficacy of these techniques is often limited by optimality-, scalability-, and uncertainty-induced complexities. In practice, infrastructure management agencies often treat interconnected systems using disjoint plans for different component types, which in general do not ensure system-level optimality. To tackle the above, the optimization problem is herein cast within constrained Partially Observable Markov Decision Processes (POMDPs), which provide a comprehensive mathematical framework for stochastic sequential decision settings under observation/monitoring data uncertainty and limited resources. For the problem solution, the DDMAC algorithm (Deep Decentralized Multi-agent Actor-Critic) is successfully used, a deep reinforcement learning algorithm well-suited for management of large multi-state multi-component systems, as illustrated in an example application of an existing transportation network in Virginia, USA. The studied network comprises several bridge and pavement components exhibiting nonstationary deterioration, and various agency-imposed constraints, and traffic delay and risk factors are considered. Comparisons against conventional management policies showcase that the DDMAC solution significantly outperforms its counterparts.