A new solution to the coupled problems of aero-propulsion system deceleration under supersonic state

Under supersonic state, the aero-propulsion system exhibits different coupled characters in deceleration from that in acceleration. However, the deceleration control has not been fully studied. In order to solve the coupled problems, an integrated component-level model including inlet and turbofan engine was established. Based on the integrated model, the particularity of inlet adjustment during deceleration was analyzed. And the analyzed results showed that the inlet regulation is not necessary to keep the inlet and engine working in well-matched at any time under supersonic state. Due to the coupled relationship between inlet and turbofan engine, a new optimal integrated control scheme is proposed in this paper. The inlet ramp angle is taken as an optimal control variable as the same as main fuel mass flow and nozzle throat area. The simulation results indicate that inlet ramp angle regulation showed a more effective control quality in the rapid drop of aero-propulsion–installed thrust. Furthermore, the deceleration could be completed in a shorter control time.

Exposure to Non-exhaust Emission in Central Seoul using an Agent-based Framework

Non-exhaust emission (NEE) from brake and tyre wear cause deleterious effects on human health, but the relationship with mobility has not been thoroughly examined. We construct an in silico agent-based traffic simulator for Central Seoul to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 (particles less than 10 microns in size) for groups of drivers and subway commuters. The results show that significant extra particulates relative to the background exist along roadways where NEEs contributed some 40% of the roadside PM10. In terms of health risk, 88% of resident drivers had an acute health effect in late March but that kind of emergence rarely happened. By contrast, subway commuters’ health risk peaked at a maximum of 30% with frequent oscillations whenever the air pollution episodes occurred. A 90% vehicle restriction scenario reduced PM10 by 18-24%, and reduced the resident driver's risk by a factor of 2, but not effective for subway commuters as the group generally walked through background areas rather than along major roadways. Using an agent-based traffic simulator in a health context can give insights into how exposure and health outcomes can depend on the time of exposure and the mode of transport.

A Data-Driven Space-Time-Parameter Reduced-Order Model with Manifold Learning for Coupled Problems: Application to Deformable Capsules Flowing in Microchannels

An innovative data-driven model-order reduction technique is proposed to model dilute micrometric or nanometric suspensions of microcapsules, i.e., microdrops protected in a thin hyperelastic membrane, which are used in Healthcare as innovative drug vehicles. We consider a microcapsule flowing in a similar-size microfluidic channel and vary systematically the governing parameter, namely the capillary number, ratio of the viscous to elastic forces, and the confinement ratio, ratio of the capsule to tube size. The resulting space-time-parameter problem is solved using two global POD reduced bases, determined in the offline stage for the space and parameter variables, respectively. A suitable low-order spatial reduced basis is then computed in the online stage for any new parameter instance. The time evolution of the capsule dynamics is achieved by identifying the nonlinear low-order manifold of the reduced variables; for that, a point cloud of reduced data is computed and a diffuse approximation method is used. Numerical comparisons between the full-order fluid-structure interaction model and the reduced-order one confirm both accuracy and stability of the reduction technique over the whole admissible parameter domain. We believe that such an approach can be applied to a broad range of coupled problems especially involving quasistatic models of structural mechanics.

An extended Hamilton principle as unifying theory for coupled problems and dissipative microstructure evolution

An established strategy for material modeling is provided by energy-based principles such that evolution equations in terms of ordinary differential equations can be derived. However, there exist a variety of material models that also need to take into account non-local effects to capture microstructure evolution. In this case, the evolution of microstructure is described by a partial differential equation. In this contribution, we present how Hamilton’s principle provides a physically sound strategy for the derivation of transient field equations for all state variables. Therefore, we begin with a demonstration how Hamilton’s principle generalizes the principle of stationary action for rigid bodies. Furthermore, we show that the basic idea behind Hamilton’s principle is not restricted to isothermal mechanical processes. In contrast, we propose an extended Hamilton principle which is applicable to coupled problems and dissipative microstructure evolution. As example, we demonstrate how the field equations for all state variables for thermo-mechanically coupled problems, i.e., displacements, temperature, and internal variables, result from the stationarity of the extended Hamilton functional. The relation to other principles, as the principle of virtual work and Onsager’s principle, is given. Finally, exemplary material models demonstrate how to use the extended Hamilton principle for thermo-mechanically coupled elastic, gradient-enhanced, rate-dependent, and rate-independent materials.