Heat Exchange of the Cylindrical Liquid Body of a Limited Height with the Environment

The experimental investigations of the intensity of the heat exchange between the internal surface of the thin-wall metal cylinder and the studied liquid medium were carried out in conditions of its cooling (heating), i.e. under nonstationary heat exchange conditions. The existence of the regular thermal mode in the liquid medium surrounded by the thin-wall metal cylinder has been established. Local in time heat loss coefficients were derived using appropriate dimensionless equations for the stationary mode conditions of heat-exchange in a large volume. Heat loss coefficients were determined using regular thermal mode methods and computational-&-experimental heat loss coefficients. The changes in the relative values of the heat loss coefficients were analyzed using the method of regular thermal mode and computational-&-experimental heat loss coefficients. The deviations in the values of given coefficients in time are mainly within ± 10 %. Relative values of the heat loss coefficients deviate within ± 40 % using appropriate dimensionless equations for the conditions of the stationary mode of heat exchange in a large volume. This conclusion is natural because the cooling (heating) process is nonstationary.

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.

Impurity transport studies at the HSX stellarator using active and passive CVI spectroscopy

The transport of carbon impurities has been studied in the helically symmetric stellarator experiment (HSX) using active and passive charge exchange recombination spectroscopy (CHERS). For the analysis of the CHERS signals, the STRAHL impurity transport code has been re-written in the python programming language and optimized for the application in stellarators. In addition, neutral densities both along the NBI line of sight as well as for the background plasma have been calculated using the FIDASIM code. By using the basinhopping algorithm to minimize the difference between experimental and predicted active and passive signals, significant levels of impurity diffusion are observed. Comparisons with neoclassical calculations from DKES/PENTA show that the inferred levels exceed the neoclassical transport by about a factor of four in the core and more than 100 times towards the plasma edge, thus indicating a high level of anomalous transport. This observation is in agreement with experimental heat diffusivites determined from a power balance analysis which exhibit strong anomalous transport as well.

Experimental Heat Loads for Electrothermal Anti-Icing and De-Icing on UAVs

Atmospheric in-flight icing on unmanned aerial vehicles (UAVs) is a significant hazard. UAVs that are not equipped with ice protection systems are usually limited to operations within visual line of sight or to weather conditions without icing risk. As many military and commercial UAV missions require flights beyond visual line of sight and into adverse weather conditions, energy-efficient ice protection systems are required. In this experimental study, two electro-thermal ice protection systems for fixed-wing UAVs were tested. One system was operated in anti-icing and de-icing mode, and the other system was designed as a parting strip de-icing system. Experiments were conducted in an icing wind tunnel facility for varying icing conditions at low Reynolds numbers. A parametric study over the ice shedding time was used to identify the most energy-efficient operation mode. The results showed that longer intercycle durations led to higher efficiencies and that de-icing with a parting strip was superior compared to anti-icing and de-icing without a parting strip. These findings are relevant for the development of energy-efficient systems in the future.

Physics-Informed Neural Networks (PINNs) for Heat Transfer Problems

Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multi-task learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature and velocity measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Responses of Manduca sexta larvae to heat waves

Climate change is increasing the frequency of heat waves and other extreme weather events experienced by organisms. How does the number and developmental timing of heat waves affect survival, growth and development of insects? Do heat waves early in development alter performance later in development? We addressed these questions using experimental heat waves with larvae of the Tobacco Hornworm, Manduca sexta. The experiments used diurnally fluctuating temperature treatments differing in the number (0-3) and developmental timing (early, middle and/or late in larval development) of heat waves, in which a single heat wave involved three consecutive days with a daily maximum temperature of 42 °C. Survival to pupation declines with increasing number of heat waves. Multiple (but not single) heat waves significantly reduced development time and pupal mass; the best models for the data indicated that both the number and developmental timing of heat waves affected performance. In addition, heat waves earlier in development significantly reduced growth and development rates later in larval development. Our results illustrate how the frequency and developmental timing of sublethal heat waves can have important consequences for life history traits in insects.