J. Exp. Theor. Anal., Volume 3, Issue 4 (December 2025) – 6 articles

We describe the problem of estimating the speaker’s head orientation from the asynchronous multi-channel waveforms observed by microphones distributed in a room. In particular, we address a novel problem of estimating head orientation from sound captured by fewer microphones than the number of distinct head orientations to be distinguished. This is because the head orientation is an important clue indicating the speaker’s intended conversational partners. Head orientation estimation technology is an essential technology within environmental intelligence technology, which uses sensors embedded in rooms to monitor and support people’s activities. We propose a head orientation estimation method that aims to achieve high angular resolution using a small number of microphones. The proposed method achieves high estimation accuracy by using the spatial radiation pattern of the sound source as clues and by integrating information from multiple frequency bands. We conducted an experiment to estimate head orientation with an angular resolution of $15\mathrm{degrees}$ under observation conditions using six microphones. Experimental results showed that higher estimation accuracy was obtained than the conventional method using distributed microphone arrays (Oriented Global Coherence Field method) and the conventional method using distributed microphones (Radiation Pattern Matching method). The proposed method utilizing multiple frequency bands achieved the best performance with a mean absolute error of $10.58\mathrm{degrees}$ in the task of classifying 24 distinct head orientations. Full article

Conventional thermomechanical models recently failed to reproduce the temperature profile measured during rapid annular laser heating of a disk, with discrepancies of up to 150 K. One might have thought that these discrepancies resulted from neglecting the so-called “strong” thermomechanical coupling. However, the discrepancies seemed too large to be explained in this way, suggesting that another more significant phenomenon was involved. In this paper, we first present the laser heating experiment that highlights the failure of conventional models. We then demonstrate that the established strong coupling thermomechanical theory cannot account for the observed divergences, as its impact on temperature does not exceed about 1 K. To address this limitation, we propose a new, more comprehensive thermomechanical coupling formalism based on the thermodynamics of irreversible processes (TIP). Its originality lies in the explicit consideration of spatial strain transport, introduced through the notion of strain flux. This approach reveals a previously unrecognized coupling term representing mechanical work production by heat-to-work conversion. Finally, we provide a quantitative estimate of the influence of this new term by reconsidering the heating experiment. The calculation shows that it could explain the discrepancies between theory and measurement. Although applied here to a specific case, this result supports the validity of our approach. It demonstrates that such coupling must be considered whenever a system is subjected to rapid thermal and mechanical transients. Full article

Traditional empirical correlations for predicting saturated flow boiling heat transfer coefficients (HTC) often struggle with accuracy and generalizability, particularly across different refrigerants, heat exchanger geometries, and operating conditions. To address these limitations, this study investigates the application of machine learning for more robust HTC prediction. A comprehensive dataset was compiled, consisting of 22,608 data points from over 140 published studies, covering 18 pure refrigerants under diverse experimental setups. The primary goal was to evaluate the performance of different machine learning approaches—Wide Neural Network (WNN), Linear Regression (LR), and Support Vector Machine (SVM)—in predicting HTCs across varying tube types and heat exchanger configurations. The results indicate that the WNN model achieved the highest predictive accuracy, with a Root Mean Square Error (RMSE) of 1.97 and a coefficient of determination (R²) of 0.91, corresponding to less than 5% prediction error for all refrigerants. These outcomes confirm that machine learning models can effectively capture the complex thermofluid interactions involved in boiling heat transfer. This work demonstrates that data-driven methods provide a reliable and generalizable alternative to empirical correlations. Full article

To demonstrate the difference between non-ideal experiments and idealized analytical models, bending vibrations of a frame structure have been analyzed in the context of hands-on teaching in structural dynamics. Both experimental modal analysis and model-based evaluation of system dynamics have been performed. The investigations have been limited to mechanical vibrations in the low-frequency range. It has been found that even simple mechanical models are very useful to explain, understand, and validate experimental results. The latter have been derived from one key principle of analytical dynamics—the Lagrange formalism. The article is written for students in mechanical engineering and related fields as well as for the academic community. The latter could use the results as a benchmark problem in academic teaching as well as in applied research. Full article

The research presents the thermal performance comparison of silicone and non-silicone thermal pads using a steady-state thermal interface material (TIM) testing apparatus. The TIM tester follows standard guidelines for testing thermal properties. TIMs are applied between two solid surfaces to improve heat transfer by eliminating air gaps that naturally occur due to surface roughness and non-flatness. Since TIMs possess significantly higher thermal conductivity than air, they effectively reduce contact resistance at the interface, thereby minimizing the risk of overheating in electronic systems. In this work, the thermal resistances of silicone and non-silicone thermal pads were compared over a pressure range of 10–50 psi. Results indicate that non-silicone pads consistently exhibit lower thermal resistance than their silicone counterparts under identical testing conditions. Full article

In this review paper, we briefly discuss the occurrence of oil spills and their behavior under natural sea conditions and clean-up methods, as well as their environmental and economic impacts. We discuss methodologies for oil spill modeling used to predict the fate of a spill under dynamic physical and chemical processes. Weathering processes such as evaporation, emulsification, spreading, dissolution, dispersion, biodegradation, and sedimentation are considered within easy-to-use modeling frameworks. We present simple models based on the principles of thermodynamics, mass transfer, and kinetics that under certain conditions can predict oil thickness, volume, area, composition, and the distribution of toxic compounds in water and air over time for various types of oil and their products. Modeling approaches for underwater oil jets, including applications related to the 2010 BP oil spill in the Gulf of Mexico, are reviewed. The influence of sea surface velocity and wind speed on oil spill mapping, spill location, oil spill trajectory over time, areas affected by light, medium, and heavy oil, and comparisons between satellite images and model predictions are demonstrated. Finally, we introduce several recently published articles on more recent oil spill incidents and the application of predictive models in different regions. We also discuss the challenges, advantages, and disadvantages of various models and offer recommendations at the end of the paper. Full article