Thermal Characterization of a Steam Turbine Casing Including Measuring of Adiabatic Wall Temperatures Using Proprietary Sensors

Modern steam turbines must increasingly be designed for flexible operation. However an increasing amount of cold starts and load changes have a massive impact on fatigue resistance of the material. So the monitoring of thermal parameters of the casing is significant for checking thermally induced stresses and furthermore lifetime calculation. Additionally the measurement data is helpful for CFD validation reasons. This paper presents a new proprietary developed sensor setup and measurement results. The sensors are flush mounted into a steam turbine at different axial and circumferential locations in the recirculation area between the intermediate and the lower pressure turbine. Hence it is possible to detect temperatures, temperature gradients and heat flux in the part of the wall near the fluid. Moreover the field of temperature within the sensor can be modulated by powering an installed heater. So the adiabatic wall temperature can be identified. For measuring the temperature gradient, seven equidistant spaced thermocouples were used in difference circuit. Therefore two different types of thermocouples were applied. Both types have better transfer characteristics compared to a thermocouple of type K. High amplification enables monitoring of small differences in temperature. The temperature measures an integrated resistor thermometer. The sensors are applied on a real 12 MW industrial steam turbine with maximal live steam parameters of 400 °C and 30 bar. The measurements show various operation points and load changes.

Computational Fluid Dynamics Simulation of Airflow and Air Pattern in the Living Room for Reducing Coronavirus Exposure

Viruses can be transmitted in indoor environments. Important factors in Indoor Air Quality (IAQ) are air velocity, relative humidity, temperature, and airflow pattern and Computational fluid dynamics (CFD) can use for IAQ assessment. The objective of this study is to CFD simulation in the living room to the prediction of the air pattern and air velocity. A computational fluid dynamic model was applied for airflow pattern and air velocity simulation. For simulation, GAMBIT, FLUENT, and CFD post software were used as preprocessing, processing, and post-processing, respectively. CFD validation was carried out by comparing the computed data with the experimental measurements. The final mesh number was set to 1,416,884 elementary cells and SIMPLEC algorithm was used for pressure-velocity coupling. PERSTO, and QUIK schemes have been used for the pressure terms, and the other variables, respectively. Simulations were carried out in ACH equals 3, 6 and 8 in four lateral walls. The maximum error and root mean square error from the air velocity were 14% and 0.10, respectively. Terminal settling velocity and relaxation time were equal to 0.302 ×10− 2 m/s and 0.0308 ×10− 2 s for 10 µm diameter particles, respectively. The stopping distance was 0.0089m and 0.011m for breathing and talking, respectively. The maximum of mean air velocity is in scenario 4 with ACH = 8 that mean air velocity is equal to 0.31 in 1.1m height, respectively. The results of this study showed that avoiding family gatherings is necessary for exposure control and suitable airflow and pattern can be improving indoor air conditions.