In the drive towards miniaturised cooling systems, understanding the underlying mechanisms of efficient heat transfer is crucial for system reliability and performance optimisation. Nucleate boiling cooling is widely recognised as one of the most effective mechanisms, but comprehensive insights into nucleate boiling phenomena are essential to address the challenges posed by extreme operational conditions. This paper explores an integrated experimental methodology combining high-speed visualisation, thermal imaging, and acoustic diagnostics to detect boiling within mini cooling channel flows. The approach enables synchronised observation of bubble nucleation, growth, and detachment, while acoustic sensors capture vibrational signatures associated with boiling events. The methodology was first validated through controlled experiments in a mini-channel configuration, ensuring accurate correlation between visual, thermal, and acoustic data. Following this validation, the technique was applied to the spark-ignition prechamber experimental platform, where only acoustic vibration measurements were employed to detect the onset of nucleate boiling under realistic flow conditions. Unlike traditional high-frequency acoustic emission studies, this work demonstrates the use of low-frequency structural vibrations as a reliable, non-intrusive indicator of boiling initiation and progression in confined geometries, offering a practical diagnostic pathway for complex thermal systems.

Abstract This study investigates how temperature influences the cyclic aging behavior of Nickel−Cobalt−Aluminum (NCA) lithium-ion 18650 cells. Controlled cycling tests were conducted across a temperature range of 10−35 °C, down to an extended cycling depth of approximately 30% state of health (SOH), to quantify the effects of thermal conditions on capacity fade, energy efficiency, and cycle time. The results demonstrate temperature-dependent degradation mechanisms that significantly impact battery longevity, with important implications for electric vehicles and stationary energy storage systems requiring reliable, durable performance. Additionally, an analytical approach is applied to identify the knee point in the aging trajectory, which marks the onset of accelerated degradation. This methodology segments the capacity retention curve into three distinct phases: stabilization, equilibrium, and non-equilibrium. By analyzing the coefficient of determination (R2) values of linear fits over sliding windows, the method robustly detects the transition point between the equilibrium and non-equilibrium phases. The systematic evaluation of capacity loss, energy efficiency, and phase transitions provides critical insights for optimizing battery management systems (BMS) and thermal control strategies in high-performance applications. This work enables engineers to better predict battery aging patterns and design more effective thermal management solutions to extend battery lifespan and improve overall system reliability.

The use of hydrogen to enrich or replace hydrocarbon-based flames represents an appealing perspective to reduce the emission of pollutants in gas turbine combustors. This implementation is, however, not straightforward, as the properties of hydrogen consistently differ from those of the most widely used fuels, resulting in a change in the combustion stability of the mixture. This work investigates the non-linear thermoacoustic stability of a simple Rijke tube containing a perfectly premixed, laminar, conical flame fed by either a pure methane-based mixture or a hydrogen-enriched one. The mean heat release rate and the geometry of the system were kept constant for both mixtures. Flame dynamics were modelled using a Level Set Method, from which a Flame Describing Function was derived for each mixture and coupled with a one-dimensional acoustic model within a low-order thermoacoustic framework to asses the stability of the system. The hydrogen-enriched flame was found to exhibit a weaker non-linear response than the methane-based flame, in agreement with previous observations reported in the literature. This behaviour was attributed to the higher unstretched laminar flame speed of the hydrogen-enriched mixture, which enhances the resilience of the flame front to develop geometric non-linearities following an acoustic perturbation, reducing its tendency towards heat release rate saturation. As a consequence, the hydrogen-enriched system generated larger thermoacoustic oscillations and ultimately experienced flashback, whereas the methane-based configuration remained bounded by more consistent non-linear saturation effects.

Permanent magnet synchronous motors (PMSMs) possess high power density and efficiency, which make them suitable for broad applications, including the transportation sector, thus contributing to its decarbonization. As demand for electric vehicles increases, the need for performance improvements on electric motors also rises. An essential requirement is efficient thermal management to prevent and mitigate overheating-related premature motor failure. Among the methods of thermal analysis, the Lumped Parameter Thermal Network (LPTN) model is distinguished by its versatility of applications and its low computational requirements, making it a proper tool for analyzing the temperature distribution within a motor. This paper focuses on radial PMSMs, emphasizing the identification of the primary heat sources and the corresponding heat transfer mechanisms involved. Furthermore, it provides a comprehensive literature review of LPTN models implemented by different authors, analyzing the formulations and highlighting the similarities and differences between the models presented in the existing literature.

The use of hydrogen to enrich or replace hydrocarbon-based flames represents an appealing perspective to reduce pollutant emissions in gas turbine combustors, but it involves significant issues related to their combustion stability. This paper investigates the linear stability of a Rijke tube fueled by two different conical, laminar, perfectly premixed flames: one based on a pure methane-air mixture and the other based on a pure hydrogen-air mixture. In both cases, the heat release rate was kept constant, while the other characteristics of the system were varied. Through the constant heat release rate, the inlet velocities of the mixtures, their Reynolds numbers and the flame aspect ratios were related to the radius of the chamber, which was limited within a realistic range in order to avoid both flashbacks and turbulent transition of the flow. A Flame Transfer Function was derived by means of a linearized Level Set Method approach, highlighting a neat disparity in the flame dynamics of the two mixtures. Linear stability of the system was assessed in order to understand its sensitivity to changes in the transversal dimension of the chamber, revealing clear differences in the behavior of hydrogen and methane mixtures, motivated by the dissimilarities in their gain response, and thus demonstrating a reduction in design flexibility of hydrogen-based combustion chambers.

Lithium-ion batteries are highly affected by calendar ageing effects, which can lead to capacity loss even when the battery is not used at all. The current literature proposes plenty of semi-empirical models to predict the calendar ageing of the lithium-ion batteries, which are mostly based on Arrhenius functions for temperature dependency, exponential models for state of charge dependency and power laws for time dependency. Those models are easy to calibrate, and they provide a sufficiently precise prediction of capacity loss over time. However, it might be difficult to find a physical meaning to the parameters determined in these models, due to their lumped nature and the optimization process used. Therefore, in this work a semi-empirical model able to predict the capacity degradation over time with physically meaningful parameters is proposed. The dependency on temperature is considered by a pre-exponential factor whereas the dependency on state of charge is considered by a power law coefficient. A two-step constrained optimization process is considered to calibrate the model parameters. The model allows to predict the capacity loss over a wide range of different temperature and state of charge conditions, and it is calibrated for 4 different cell chemistries: LMO-NMC, LFP, NCA, and NMC. It was found that the worst storing condition is given by the highest temperature and state of charge conditions. The capacity degradation is provided over a period of 50 years. Two end-of-life conditions were analyzed: a loss of capacity of 20 % as representative of an end-of-life condition for automotive applications, and a loss of capacity of 50 % as an end-of-life condition for deep-space applications. In both cases, it was observed that NMC provided the best performance (the slowest ageing over time) for storing temperatures below 15 degrees C and storing state of charge below 10 %, whereas for temperatures higher than 15 degrees C and state of charge higher than 10 %, the LFP chemistry resulted to be the most longevous.

Due to their lightness, the capacity to adapt to the flow conditions, and the safety when operating near humans, the use of membrane-resistant structures has increased in fields as micro aerial vehicles and yachts sails. This work focuses on the computational methodology required for simulating the aeroelastic coupling of the structure with the incident wind flow. A semi-monocoque structure (composed of a main spar, a set of ribs, and an external membrane) inside a wind tunnel is simulated using two different methodologies. Firstly, a complete fluid-structure interaction is calculated by combining the finite element methodology for the solid and the unsteady Reynolds average Navier-Stokes computational fluid dynamics for the air, including nonlinear effects and prestress. Then, a low-fidelity model is applied, obtaining the linear aeroelastic eigenvalues and the temporal response of the wing. Both methodologies results are in agreement with estimating the transient mean deformation and flutter velocity. However, the modal analysis tends to overestimate the aeroelastic effects, as it calculates potential aerodynamics, predicting an instability velocity lower than that provided by the transient simulations.

The presence of after-treatment systems (ATS) has some side effects as they modify the wave dynamics, back-pressure and acoustic behavior of the exhaust system, that define the boundary conditions for the main silencing device. Gasoline particulate filters (GPF) have a considerable influence on these traits as their working principle depends on wall-flow monoliths; additionally, particulate filters have an inherent transient behavior due to the loading-regeneration cycles during their operation. In this paper, the steady and unsteady behavior of a typical device containing a three-way catalyst (TWC) and a GPF has been characterized in "new" (prior to any use) and "used" (after regeneration) conditions, with special focus on the role of dissipation. Pressure drop measurements were used in steady flow, whereas an acoustic energy balance was performed in the unsteady case that allows for the identification of the reactive and dissipative contributions. The results obtained for the GPF suggest that after it has been used for a relatively short time it oscillates between a maximum and a minimum loading over load–regeneration cycles burning the soot, never reaching back brand–new cleanness due to accumulation into the substrate pores and only suffering from ash accumulation over very long times. The results were then used to analyze the ability of a standard gas-dynamic code to reproduce the influence of the loading on the reactive and dissipative effects observed, with the main conclusion that by properly tuning the model parameters it is possible to reproduce, at least qualitatively, the trends observed in the experiments. The model can thus be used to provide adequate inlet conditions for the design of the exhaust line.

Most of the equivalent circuit battery models available in the literature have been developed specifically for one cell and require extensive measurements to calibrate cell electrical parameters in different operating conditions. In this work, a generalized equivalent circuit model for lithium-iron phosphate batteries is proposed, which only relies on the nominal capacity, available in the cell datasheet. Using data from cells previously characterized, a generalized zeroth-order model is developed. This novel approach allows to avoid time-consuming and expensive experiments and reduces the test matrix. In spite of not relying on detailed data on the dependence of the electrical parameters with respect to state of charge, c-rate and temperature, the model provides an excellent description of the electrical behavior for both low-energy and high-energy cells, the error being always kept below 2 %. The internal resistance of the cell is expressed as a function of a new characteristic coefficient, which is typical of this lithium-ion battery chemistry. This coefficient is fitted to an exponential function of the temperature, which is physically meaningful, as the internal resistance has an Arrhenius-like behavior with respect to temperature. This model, due to its simplicity and flexibility, is particularly useful for control-oriented applications, and for off-line analyses.

In this study an analysis of the employment of nanoparticles and nano encapsulated phase change materials for battery cooling is provided. By using a previously validated electro-thermal model of a lithium-ion battery module, the effect of 5 different nanoparticles and 6 different nano encapsulated phase change materials is estimated. The analysis is provided for a battery module charge process at 4C, with a coolant mass flow of 2 l/ min and with ambient and fluid temperature equal to 20 degrees C. The concentration of the nanofluid is varied between 0.01 % and 5 %. By increasing the concentration, a beneficial effect is observed on the battery cooling, in terms of maximum temperature achieved during the charge process and heat dissipated into the coolant. Among the 30 different combinations of nanoparticles and nano encapsulated phase change materials analyzed in this work, it is concluded that the best results in terms of dissipated heat and maximum temperature are obtained for copper oxide (CuO) combined with octadecane. In this case, at 20 degrees C, a reduction of the maximum temperature of about 2 degrees C is obtained with a volume fraction equal to 5 %, with respect to the case in which there are no nanoparticles. Furthermore, the total heat dissipated in the coolant is increased by 28 %. Finally, the study proposes a design of experiment to evaluate the performance of a phase change material for battery cooling. For this analysis, 4 variables are considered (concentration, melting temperature, heat of fusion and characteristic temperature range) and the effect on the maximum temperature and on the temperature spatial difference is observed: it is found that the thermal evolution of the cells is mostly affected by melting temperature and concentration.

Applications of composite materials in industry have increased due to their high stiffness-to-weight ratio. In the particular case of unidirectional fibers or perpendicular fabrics, the materials behavior is orthotropic, so that an extra degree of freedom, related to the orientation of the fibers, must be included in the structural optimization. Composite material thin walled beam models have been developed for reducing the computational cost of the simulations. Traditionally, these models have been coupled with potential aerodynamics to calculate the aeroelastic response, and thus, the viscous nonlinear effects have been omitted. In order to capture these effects, this manuscript focus on the development of a Reduced Order Model enhanced by an Artificial Neural Network for the analysis of composite structures under aerodynamic loads. The presented methodology shows the training process of the neural network, the comparison with high fidelity simulations and the design optimization of a carbon fiber laminated foam beam. It is demonstrated that the model reduces the computational cost by orders of magnitude, while still capturing structural couplings and being capable of increasing the flutter velocity by more than 10% with respect to the longitudinal orientation.

Decarbonization requirements have extended the use of wind turbines by orders of magnitude. Due to their high stiffness-to-weight ratio, composite materials have been widely used for manufacturing the turbine blades in the recent years. As a consequence of the orthotropic mechanical properties of these materials, the structural behavior of the blade is conditioned by the orientation of the fibers. This article gives a general idea of the benefits of optimizing the composite material ply angle. Along the paper, two different structures are analyzed, a quasi-isotropic material and a structure with oblique fibers. The analysis is conducted using a reduced order model solver which couples a beam element structural solver with the blade element momentum and Theodorsen pitching airfoil theories. The solvers are validated, and then, the flutter conditions are obtained and used to limit the whole operation curve for both blades. The oblique layup structure is evidenced to increase the critical wind velocity by 10% for a defined control law and electrical system. Therefore, the importance of a correct structural analysis is demonstrated to be crucial in the design and manufacturing of the following generation of wind turbine blades.

An investigation on the acoustic transmission loss and the pressure drop caused by a mixer inducing element located downstream of a Diesel Exhaust Fluid (DEF) injection port was carried out in order to reduce the knowledge gap identified for this kind of after-treatment system elements. Transmission Loss measurements were performed at 4 mass flow rate conditions; 0, 100, 200 and 300 kg/h. Additionally, pressure drop measurements were registered for a range of mass flow rates, from 0 to 800 kg/h at steps of 50 kg/h, with similar flow and room temperatures. The post-processing and analysis included the application of a decomposition technique in the case of the transmission loss, which lead to understand the contribution of reflective and dissipative effects to the total attenuation. Reference results are provided for the particular geometry and kind of mixer element characterized, as well as the full extent of the results analysis and the insights of the decomposition analysis technique, and also different modelling possibilities have been explored. The conclusions indicate that, while usually overlooked, these devices may have a significant influence on the exhaust acoustics, mainly due to the influence of their presence on the attenuation of the after-treatment device as a whole. (C) 2021 Elsevier Ltd. All rights reserved.

The experimental characterization of the acoustic characteristics of engine exhaust devices is usually carried out through measurements in cold conditions, due to the intrinsic difficulties associated with proper temperature control in an acoustic rig. While those measurements may be sufficiently indicative for the cold end of the exhaust (the silencing elements) their significance for the hot end (the aftertreatment system) is more doubtful, as a result of the high temperatures and, eventually, the higher amplitude of pressure waves acting on the system. In this paper, a direct assessment is provided on the significance of acoustic measurements in cold conditions for representing the actual behaviour of an aftertreatment system in a hot pulsating, engine-like flow. Making use of wave decomposition techniques, the measured characterization was convoluted with the hot-flow excitation and the device responses were directly compared. The results indicate that, while it is not possible to fully reproduce the behaviour observed in hot pulsating flow, the tendencies are reproduced, at least qualitatively. In particular, the effect of soot loading is fairly reproduced.

Aeroelastic Computational Fluid Dynamics simulations have traditionally been associated to a high computational cost, making them prohibitive in a initial phase of the design. Analytic models, which may not be accurate for nonlinear aerodynamics, have traditionally been utilized in order to size those structures. Recently, some authors have proposed the use of artificial neural networks to reduce the error in the prediction of aerodynamic coefficients of bluff bodies, which have separated flow over a substantial part of its wetted surface. This article proposes a method based on neural networks for calculating the dynamic aerodynamic coefficients of a flat plate. The procedure, which is applied for different network typologies (feed-forward and long-short term memory neural networks), is, then, coupled with a structural solver in order to create an aeroelastic reduced order model. The results are compared with CFD aeroelastic simulations, showing a high reduction of computational cost (99%) without penalties in the accuracy. The instabilities are captured and the mean deformation, amplitude and frequency of the motion are predicted. In addition, the different neural network models are compared evidencing that for the aeroelastic calculation feed-forward networks are most efficient in terms of accuracy and computational cost.

In modeling an Internal Combustion Engine, the combustion sub-model plays a critical role in the overall simulation of the engine as it provides the Mass Fraction Burned (MFB).Analytically, the Heat Release Rate (HRR) can be obtained using the Wiebe function, which is nothing more than a mathematical formulation of the MFB.The aforementioned function depends on the following four parameters: efficiency parameter, shape factor, crankshaft angle, and duration of the combustion.In this way, the Wiebe function can be adjusted to experimentally measured values of the mass fraction burned at various operating points using a least-squares regression, and thus obtaining specific values for the unknown parameters.Nevertheless, the main drawback of this approach is the requirement of testing the engine at a given engine load/speed condition.Furthermore, the main objective of this study is to propose a predictive model of the Wiebe parameters for any operating point of the tested SI engine.For this purpose, an Artificial Neural Network (ANN) is developed from the experimental data.A criterion was defined to choose the best-trained network.Finally, the Wiebe parameters are estimated with the neural networks for different operating conditions.Moreover, the mass fractions burned generated from the Wiebe functions are compared with the respective experimental values from several operating points measured in the engine test bench.Small differences were found between the estimated and experimental mass fractions burned.Therefore, the effectiveness of the developed ANN model as a prediction tool for the engine MFB is verified.

In recent years, due to the increasing need to reduce consumption of reciprocating internal combustion engines, new researches on different subsystems have raised. Among them, the use of nanofluids as a coolant medium seems to be an interesting alternative. In this work, the potential benefits of using nanofluids in the cooling system using an engine lumped model are studied. The methodology of the study starts with a whole description and validation of the model in both steady and transient conditions by means of a comparison with experimental results. Then, the potential benefits that could be obtained with the use of nanofluids are studied in a theoretical way. After that, the model is used to estimate the behavior of the system using different nanofluids in both stationary and transient conditions. The main results show that the advantages of using these new refrigerants are limited.

Among the many unknowns in the study of atomizing sprays, defining an unambiguous way to analyze turbulence is, perhaps, one of the most limiting ones. The lack of proper tools for the analysis of the turbulence field (e.g. specific one/two-point statistics, spectrum, structure functions) limits the understanding of the overall phenomenon occurring, impeding the correct estimation of motion scales (from the Kolmogorov one to the integral one). The present work proposes a methodology to analyze the turbulence in atomizing jets using a pseudo-fluid method. The many challenges presented in these types of flows (such as temporal fluid properties uncertainties, strong anisotropy and lack of a priori chance of determining the motion scales) can be simplified by such a method, as it will be clearly shown by the smooth results obtained. Finally, the method is tested against the one-phase flows turbulent data available in the literature for the Kolmogorov scaling of the one-dimension energy spectra, showing how a pseudo-fluid method could provide a reliable tool to analyze multiphase turbulence, especially in spray's primary atomization. (C) 2020 Elsevier Ltd. All rights reserved.

In this paper a numerical methodology for assessing combustion noise in compression ignition engines is described with the specific purpose of analysing the unsteady pressure field inside the combustion chamber. The numerical results show consistent agreement with experimental measurements in both the time and frequency domains. Nonetheless, an exhaustive analysis of the calculation convergence is needed to guarantee an independent solution. These results contribute to the understanding of in-cylinder unsteady processes, especially of those related to combustion chamber resonances, and their effects on the radiated noise levels. The method was applied to different combustion system configurations by modifying the spray angle of the injector, evidencing that controlling the ignition location through this design parameter it is possible to decrease the combustion noise by minimizing the resonance contribution. Important efficiency losses were, however, observed due to the injector/bowl matching worsening which compromises the performance and emissions levels.