Nowadays, the operative range limit of compressors is still a key aspect of the research into turbomachinery. In particular, the study of the mass flow rate lower limit represents a significant factor in order to predict and avoid the inception of critical working conditions and instabilities such as stall and surge. The identification of these instabilities and typical precursors of these two phenomena can imply many advantages, in both stationary and aeronautic applications, such as avoiding the loss of production (in industry) and efficiency of systems and reducing the maintenance and repairing cost. Many approaches can be adopted to achieve this target, but one of the most fascinating is the vibro-acoustic analysis of the compressor response during operation. At the Engineering Department of the University of Ferrara, a test bench, dedicated to the study of the performance of an aeronautic turboshaft engine multistage compressor, has been equipped with a high frequency data acquisition system. A set of triaxle accelerometers and microphones, suitable for capturing broad-band vibration and acoustic phenomena, were installed in strategic positions along the compressor and the test rig. A great amount of vibro-acoustic data were first processed through an innovative data analysis technique, and then correlated to the thermodynamic data recorded. Subsequently, the precursor signals of surge were detected and identified demonstrating the reliability of the methodology used for studying compressor instabilities. The experimental data and results offer a valid alternative way of analyzing and detecting unstable compressor behavior characteristics by means of nonintrusive measurements.

Land-based power units have to fulfill even more high levels of production and reliability. In harsh environments (desert and tropical installations, typically), the power unit ingests high amounts of dust that might deposit inside the compressor. In this paper, the analysis of a multistage compressor performance that operates under sandy and humid conditions has been assessed. The compressor units, which equips the Allison 250 C18 compressor, has been subjected to multiple runs under severe conditions of soil dust ingestion. The compressor has been operated according to subsequent runs, and at the end of each run, the performance curve was recorded; the performance losses, in terms of pressure ratio, have been measured during the operations. The characteristic curve of each run is representative of the level of contamination of the unit. Finally, the compressor has been washed, and the performance curve, in the recovered conditions, has been recorded. The results show the modification and the downward shift of the characteristic curves which lead to a gradual loss of the compressor performance. The curves realized after dust ingestion have been compared with the recovered curve after online washing. The measurement shows a promising recovery of the performances, even if the compressor flow path appears affected by localized deposits able to resist to the droplet removal action. Detailed photographic reports of the inlet guide vane (IGV) and the first compressor stages have been included in this analysis. After each run, the IGV, the rotor blade and stator vane of the first stage, and the hub and the shroud surfaces have been photographed. The pictures show the deposition patterns on the blades and the compressor surfaces. The comparison of the pictures of the internal surfaces, before and after the washing, highlights the parts that are more critical to clean and needy of attention during offline washing and overhaul.

Solid particle ingestion is one of the principal degradation mechanisms in the compressor and turbine sections of gas turbines. In particular, in industrial applications, the microparticles not captured by the air filtration system can cause deposits on blading and, consequently, result in a decrease in compressor performance. In the literature, there are some studies related to the fouling phenomena in transonic compressors, but in industrial applications (heavy-duty compressors, pump stations, etc.), the subsonic compressors are widespread. It is highly important for the manufacturer to gather information about the fouling phenomenon related to this type of compressor. This paper presents three-dimensional (3D) numerical simulations of the microparticle ingestion (0.15–1.50 μm) in a multistage (i.e., eight stage) subsonic axial compressor, carried out by means of a commercial computational fluid dynamic (CFD) code. Particles of this size can follow the main air flow with relatively little slip, while being impacted by flow turbulence. It is of great interest to the industry to determine which zones of the compressor blades are impacted by these small particles. Particle trajectory simulations use a stochastic Lagrangian tracking method that solves the equations of motion separately from the continuous phase. The adopted computational strategy allows the evaluation of particle deposition in a multistage axial compressor thanks to the use of a mixing plane approach to model the rotor/stator interaction. The compressor numerical model and the discrete phase model are set up and validated against the experimental and numerical data available in the literature. The number of particles and sizes is specified in order to perform a quantitative analysis of the particle impacts on the blade surface. The blade zones affected by particle impacts and the kinematic characteristics (velocity and angle) of the impact of micrometric and submicrometric particles with the blade surface are shown. Both blade zones affected by particle impact and deposition are analyzed. The particle deposition is established by using the quantity called sticking probability (SP), adopted from the literature. The SP links the kinematic characteristics of particle impact on the blade with the fouling phenomenon. The results show that microparticles tend to follow the flow by impacting on the compressor blades at full span. The suction side (SS) of the blade is only affected by the impacts of the smallest particles. Particular fluid dynamic phenomena, such as corner separations and clearance vortices, strongly influence the impact location of the particles. The impact and deposition trends decrease according to the stages. The front stages appear more affected by particle impact and deposition than the rear ones.

Solar energy is considered one of the most affordable renewable resources for meeting current energy demands and mitigating environmental problems. However, the exploitation of solar energy is challenging because of both diurnal and seasonal variations. Power-to-hydrogen technologies can play a key role to counterbalance the variation of solar irradiance. Moreover, hydrogen-fueled gas turbines are considered promising technologies to decarbonize the electricity sector. To tackle these concerns, this paper presents a multigeneration energy system operated in island mode in which a hydrogen-fueled gas turbine is coupled with a solar photovoltaic plant, an electrolyzer, an absorption chiller, electric and thermal energy storage, as well as a hydrogen storage. Therefore, the energy system is 100% based on renewable energy. The sizes of the components are optimized by maximizing the exploitation of renewable energy sources, while the supply of electricity from the national grid must be null. Moreover, the effect of ambient conditions on the optimal sizing is also investigated by considering the thermal, cooling, and electrical energy demands of two case studies located in two different climatic zones. The paper demonstrates that the adoption of hydrogen-fueled gas turbines coupled with power-to-hydrogen technologies can effectively support the transition toward a clean energy supply. Moreover, this study provides a procedure for the optimal sizing of a multigeneration energy system fully based on solar energy, by also demonstrating that both photovoltaic (PV) panel area and hydrogen storage volume are feasible, if compared to the considered district layout.

The prediction of gas turbine (GT) future health state plays a strategic role in the current energy sector. However, training an accurate prognostic model is challenging in case of limited historical data (e.g., new installation). Thus, this paper develops a generative adversarial network (GAN) model aimed to generate synthetic data that can be used for data augmentation. The GAN model includes two neural networks, i.e., a generator and a discriminator. The generator aims to generate synthetic data that mimic the real data. The discriminator is a binary classification network. During the training process, the generator is optimized to fool the discriminator in distinguishing between real and synthetic data. The real data employed in this paper were taken from the literature, gathered from three GTs, and refer to two quantities, i.e., corrected power output and compressor efficiency, which are tracked during several years. Three different analyses are presented to validate the reliability of the synthetic dataset. First, a visual comparison of real and synthetic data is performed. Then, two metrics are employed to quantitively evaluate the similarity between real and synthetic data distributions. Finally, a prognostic model is trained by only using synthetic data and then employed to predict real data. The results prove the high reliability of the synthetic data, which can be thus exploited to train a prognostic model. In fact, the prediction error of the prognostic model on the real data is lower than 2.5% even in the case of long-term prediction.

Hybrid energy plants (HEPs), which include both fossil fuel technologies and renewable energy systems, can provide an important step toward a sustainable energy supply. In fact, the hybridization of renewable energy systems with gas turbines (GTs), which are fed by fossil fuels allows an acceptable compromise, so that high fossil fuel efficiency and high share of renewables can be potentially achieved. Moreover, electrical and thermal energy storage systems increase the flexibility of the energy plant and effectively manage the variability of energy production and demand. This paper investigates the optimal sizing of a HEP, which combines an industrial GT, renewable energy systems, and energy storage technologies. The considered renewable energy system is a photovoltaic system (PV), while the energy storage technologies are electrical energy storage and thermal energy storage. Moreover, a compression chiller and a gas boiler (GB) are also considered. For this purpose, the load profiles of electricity, heating, and cooling during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy). The sizing optimization problem of the different technologies composing the HEP is solved by using a genetic algorithm, with the goal of minimizing the primary energy consumption (PEC). Moreover, different operation strategies are analyzed and compared so that plant operation is also optimized. The results demonstrate that the optimal sizing of the HEP, coupled with the optimized operation strategy, allows high average cogeneration efficiency (up to 84%), thus minimizing PEC.