Davide Marsano

The manufacturing industry is facing increasing pressure to improve efficiency and sustainability in response to energy constraints and environmental regulations. Within this context mold cleaning represents a critical and energy-intensive step in resin-based component manufacturing. Although conventional manual sandblasting is widely adopted it is affected by poor repeatability limited process optimization and significant material and energy waste. The objective of this work is to develop and validate a predictive numerical tool capable of supporting the optimization and digitalization of the sandblasting process enabling higher efficiency and improved sustainability. A validated multiphase Computational Fluid Dynamics (CFD) model based on the Discrete Phase Model (DPM) was developed to simulate particle-laden jets interacting with mold surfaces. The methodology involved an accurate characterization of the nozzle flow field to determine particle velocity and distribution at the nozzle exit. The CFD model was rigorously validated through comparison with experimental sandblasting tests evaluating residual material on the mold surface under different nozzle–mold distances and jet inclination angles. Furthermore, a simplified plate-based CFD model was employed to derive optimal nozzle trajectories and pass strategies reducing computational cost while preserving predictive capability. The numerical framework was designed to be integrated into a digital manufacturing environment for a real-time camera-based monitoring. The results demonstrate a strong agreement between numerical predictions and experimental measurements confirming the reliability of the CFD model in describing material removal mechanisms. The proposed approach enables the rational optimization of process parameters and nozzle paths leading to a significant improvement in cleaning efficiency and process repeatability. By reducing unnecessary sand usage process time and rework the methodology contributes to lower energy consumption and material waste. This work introduces an innovative digital tool for sandblasting process optimization supporting the transition toward smarter and more sustainable mold maintenance operations.

In the automotive industry, the increasing stringent standards to reduce fuel consumption and pollutant emissions has driven significant advancements in turbocharging systems. The centrifugal compressor, as the most widely used power-absorbing machinery, plays a crucial role but remains one of the most complex components to study and design. While most numerical studies rely on adiabatic models, this work analyses several Computational Fluid Dynamics (CFD) models with conjugate heat transfer (CHT) of varying complexity, incorporating real solid components. This approach allowed a sensitivity analysis of the performance obtained from the different models compared to the adiabatic case, highlighting the effects of internal heat exchange losses. Moreover, an analysis of the temperature distribution of the wheel was conducted, along with a thermal assessment of the various heat flux contributions across the different components, to gain a deeper understanding of the performance differences. The impact of including the seal plate has been evaluated and different boundary conditions on the seal plate have been tested to assess the uncertainty in the results. Finally, the influence of heat exchange between the shroud and the external environment is also examined to further refine the model’s accuracy. One of the objectives of this work is to obtain a correct temperature profile of the rotor for a subsequent thermo-mechanical analysis.

Modern High-Pressure Turbine (HPT) stages in aircraft engines are designed to operate at very high temperatures to maximize the engine performance and efficiency. Blade coatings and sophisticated cooling systems are the main strategies to safeguard the integrity of HPT blades from the hot combustion gases at temperatures even over 2000 K. The cooling flows, extracted from the compressor flow path, are channeled in a complex layout of pipes within the engine casing and the HPT blades. The engine control system must ensure continuous delivery of cooling gas because any interruptions could seriously compromise the blade integrity. Innovative techniques using Machine Learning combined with the Computational Fluid Dynamics (CFD) simulations can support the development of extended monitoring systems or control systems. With the HPT of the Energy Efficient Engine (E3) as reference case of a 3D cooled gas turbine blade, a database of CFD simulations (using the open-source turbomachinery software MULTALL) is performed by varying the cooling mass flow across different ejection zones in a single blade row, simulating real-world scenarios such as partial or full clogging. The use of the open-source platform allows the simulation of a large number of cases in parallel that would be prohibitively expensive for an academic research activity if based on commercial licenses. From the resulting CFD dataset, a large number of global parameters, that can be measured during engine operation, are extracted, together with the local spanwise temperature distributions near the leading and the trailing edge of the inspected row. The resulting database is used to train two Neural Networks models: the first one can reconstruct the temperature distributions from the few measurable parameters, the second one uses the reconstructed temperature profiles to understand if the cooling system has some malfunctioning and where this occurs (Leading Edge, Suction Side and Trailing Edge or Endwalls). In the paper the potential of the proposed approach for the setup of extended engine monitoring tools is demonstrated.

The objectives of reducing and increasing pollutant emissions during the glass production process also apply to the glass industry, meaning that the accurate modeling of a glass furnace is of critical strategic value. In the available literature, several CFD studies have proposed various models with different levels of complexity. Two basic aspects are shared by the existing models, limiting their accuracy and their impact on furnace design: the combustion space is usually solved with reliance on simplified models (e.g., Flamelet and global kinetic mechanisms); and the glass tank is solved separately, using an iterative approach to couple two (or more) simulated domains. This work presents the development of an innovative CFD model to overcome these limitations and to perform accurate simulations of industrial glass furnaces. The reactive flow is solved using a reduced chemical kinetic mechanism and the EDC (eddy dissipation concept) turbulence–chemistry interaction model to properly reproduce the complex combustion development. The glass bath is solved as a laminar flow with the appropriate temperature-dependent glass properties. The two domains are simulated simultaneously and thermally coupled through an interface. This procedure allows for the more accurate calculation of the heat flow and the temperature distributions on the glass bath, accounting for their subsequent influence on the glass convective motions. The simulation of an existing glass furnace, along with selected comparisons with experimental data, are presented to demonstrate the validity of the proposed model. The developed model provides a contribution that allows us to advance the wider understanding of glass furnace dynamics.

Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

The glass industry plays a pivotal role in modern manufacturing, providing essential materials for a multitude of applications ranging from construction to consumer goods. However, the substantial energy demands, primarily driven by the intense heat required to melt raw materials within furnaces, pose significant challenges. Traditionally, thermal energy is sourced from combustion processes in dedicated chambers. Yet, in the pursuit of efficiency and environmental sustainability, there is a growing preference for integrated systems. Notably, the utilization of electrode installations within glass baths emerges as a promising choice for direct melting augmentation through electricity in a more efficient way. This study introduces a sophisticated computational fluid dynamics (CFD) model, developed by the authors, tailored to simulate the real operation of a glass furnace. The model fully couples the reactive flow in the combustion space (the furnace) with convective motions within the glass tank including the effect of the electrodes. By changing the thermal energy from combustion with varying levels of electrode operation (keeping the same overall heat flux to the glass), the furnace behaviour is investigated. The research specifically examines the effects of electrode operation on crucial furnace parameters, including flame heat release and convective motions inside the glass bath, across different scenarios. Insights are gained into the complex interplay between electrode operation, thermal dynamics, and glass quality within the furnace environment. The findings of this study not only deepen the understanding of fundamental processes within glass manufacturing but also offer actionable insights for industry stakeholders. By elucidating the intricate relationship between electrode operation and glass melting process, this research provides practical guidance for optimizing furnace design and operation, enhancing efficiency and CO2 reduction, with the same product quality, in the glass industry.

This study uses a statistical analysis on given pressure signals to identify the surge condition in radial compressors, providing insight into their dynamics and performance. Specifically, the investigation focuses on a two-stage centrifugal compressor used in refrigeration applications. Through Computational Fluid Dynamics (CFD) Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, static pressure time-series are collected to cover four distinct operational points of the machine, ranging from a stable condition to an unstable one. To assess the stability of the compressor operation, the analysis introduces the Approximate Entropy (ApEn) as a statistical parameter to measure the regularity and predictability of the time-series signals. The results show that the proposed methodology is effective in pattern recognition, able to identify sources of instabilities, and to distinguish between different operating conditions. This approach enhances the comprehension of the operational characteristics of centrifugal compressors and sets the stage for improved monitoring and diagnostic methodologies. ApEn can therefore be a useful index to analyse time-series from sensors easy to install in real compressor configurations. The proposed methodology is included in a set of models, based on machine learning techniques, previously developed by the authors, to identify compressor instability during operation.

Turbulent auto-igniting diffusion flames are common in combustion systems, where the mixing of the fuel jet with the outer oxidant flow is the fundamental mechanism for the flame ignition and development. The paper presents a critical analysis on the Reynolds-Averaged-Navier-Stokes (RANS) simulation of a methane-air flame, done with a two equations turbulence model, in comparison with detailed experimental data. In the first part, the non-reactive jet is simulated with the standard k − ε model and a modified version, obtained with motivated changes of the C_μ and σ_k constants. In the second part the reactive jet is simulated, introducing a consistent modification of the C_γ constant, in the eddy dissipation concept (EDC) model. The comparison with experimental data confirms the validity of the proposed model and highlights the effect of the proper turbulent jet development on the mechanisms of the diffusive flame ignition and propagation.