![]() The recent improvement of technology readiness level in aeronautics and the renewed demand for faster transportation are driving the rebirth of supersonic flight for commercial aviation. While all necessary parts for a potential realization are available, challenges such as low data quality and computational burden have to be overcome first. At the same time, the performed measurements would serve as input for the solution of the differential equations. Not only would this allow a high resolution with a lower number of network nodes but it would also result in a simple method for recalibration, given the traceability of simulations. This theoretical paper makes considerations that air quality monitoring networks and air pollution models could be combined into one single measurement instrument. ![]() Air pollution models based on partial differential equations offer an arbitrary spatial and temporal resolution, but they require some experimental data as initial and boundary conditions in order to be solved. These systems, however, need frequent recalibration to maintain metrological traceability. In the context of smart cities, high-resolution air quality monitoring with low-cost sensor systems has seen remarkable interest. When applying these tools in developing a practical flow solver, a host of practical issues relating to the physical model, the grid, the choice of implicit or explicit marching methods, and convergence acceleration, must be considered. Major tools in the development of CFD solvers are the Von Neumann analysis, a linear Fourier analysis used to determine numerical stability as well as analyzing the dissipative and dispersive errors in the numerical approximation, and the nonlinear monotonicity analysis, which is used to develop limiters needed to prevent numerical oscillations that may overrun the numerical simulation. CFD is primarily concerned with the approximation of partial differential equations (PDEs) on computational grids to make these approximations suitable for the simulation of fluid flow, they have to adhere to principles of consistency, stability, convergence, monotonicity, conservation, and irreversibility. The development of CFD for compressible flow was dominated by weapons research and astrophysics until 1980, when aeronautical CFD caught up, took the lead, and has been in the forefront since. Computational fluid dynamics (CFD) has its roots in weapons research since World War II, it has been used to replace experiments that are expensive, difficult, dangerous, or even impossible to conduct.
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