Under steady-state conditions when fluid temperature is constant, temperature measurement can be accomplished with high degree of accuracy owing to the absence of damping and time lag. However, when fluid temperature varies rapidly, for example, during start-up, appreciable differences occur between the actual and measured fluid temperature. These differences occur because it takes time for heat to transfer through the heavy thermometer pocket to the thermocouple. In this paper, a method for determinig transient fluid temperature based on the first-order thermometer model is presented. Fluid temperature is determined using a thermometer, which is suddenly immersed into boiling water. Next, the time constant is defined as a function of fluid velocity for four sheated thermocouples with different diameters. To demonstrate the applicability of the presented method to actual data where air velocity varies, the temperature of air is estimated based on measurements carried out by three thermocouples with different outer diameters. Lastly, the time constant is presented as a function of fluid velocity and outer diameter of thermocouple.

The analysis of subsonic stall flutter in turbomachinery blade cascade is carried out using a medium-fidelity reduced-order aeroelastic numerical model. The model is a type of field mesh-free approach and based on a hybrid boundary element method. The medium-fidelity flow solver is developed on the principle of viscous-inviscid two-way weak-coupling approach. The hybrid flow solver is employed to model separated flow and stall flutter in the 3D blade cascade at subsonic speed. The aerodynamic damping coefficient w.r.t. inter blade phase angle in traveling-wave mode is estimated along with other parameters. The same stability parameter is used to analyze the cascade flutter resistance regime. The estimated results are validated against experimental measurements as well as Navier-Stokes based high fidelity CFD model. The simulated results show good agreement with experimental data. Furthermore, the hybrid flow solver has managed to bring down the computational cost significantly as compared to mesh-based CFD models. Therefore, all the prime objectives of the research have been successfully achieved.

The study examines various approaches oriented towards conceptual and numerical reduction of first-principle models, data-driven methodologies for surrogate (black box) and hybrid (grey box) modeling, and addresses the prospect of using digital twins in chemical and process engineering. In the case of numerical reduction of mechanistic models, special attention is paid to methodologies in which simulation data are used to construct light but robust numerical models while preserving all the physics of the problem, yielding reduced-order datadriven but still white-box models. In addition to reviewing various methodologies and identifying their applications in chemical engineering, including industrial process engineering, as well as fundamental research, the study outlines associated problems and challenges, as well as the risks posed by the era of big data.