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This paper investigates the nature of the low saliency ratio of large permanent magnet generators with fractional-slot concentrated windings (FSCWs). A saliency ratio of at least 1.2 is typically required to enable sensorless control of large generators—a value naturally achieved in integer slot winding topologies but absent in FSCW surface-mounted permanent magnet machines reported in the literature. The low saliency ratio in FSCW designs is attributed to larger teeth, which reduce magnetic saturation and increase d-axis inductance. This work explores methods to enhance the saliency ratio of FSCW machines for offshore wind turbines, facilitating sensorless rotor position estimation. The proposed approaches are categorized into two groups: (1) those that preserve the conventional machine geometry with minimal modification to the magnetic circuit and (2) those involving magnetic circuit alterations. The results show that significant improvement in saliency ratio is only achievable through magnetic circuit modifications, such as rotor shoes, albeit with some performance trade-offs. A multi-objective genetic algorithm is employed to design two optimized 3 MW FSCW machine topologies, achieving saliency ratios of 1.15 and 1.2 with minimal performance loss. Compared to a 3 MW FSCW baseline, the optimized designs show stator power reductions of 3.40% and 6.16% for saliency ratios of 1.15 and 1.2, respectively.

This paper investigates the nature of the low saliency ratio of large permanent magnet generators with fractional-slot concentrated windings (FSCWs). A saliency ratio of at least 1.2 is typically required to enable sensorless control of large generators—a value naturally achieved in integer slot winding topologies but absent in FSCW surface-mounted permanent magnet machines reported in the literature. The low saliency ratio in FSCW designs is attributed to larger teeth, which reduce magnetic saturation and increase d-axis inductance. This work explores methods to enhance the saliency ratio of FSCW machines for offshore wind turbines, facilitating sensorless rotor position estimation. The proposed approaches are categorized into two groups: (1) those that preserve the conventional machine geometry with minimal modification to the magnetic circuit and (2) those involving magnetic circuit alterations. The results show that significant improvement in saliency ratio is only achievable through magnetic circuit modifications, such as rotor shoes, albeit with some performance trade-offs. A multi-objective genetic algorithm is employed to design two optimized 3 MW FSCW machine topologies, achieving saliency ratios of 1.15 and 1.2 with minimal performance loss. Compared to a 3 MW FSCW baseline, the optimized designs show stator power reductions of 3.40% and 6.16% for saliency ratios of 1.15 and 1.2, respectively.

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.

Faults of heating, ventilation, and air-conditioning (HVAC) systems can cause significant consequences, such as negatively affecting thermal comfort of occupants, energy demand, indoor air quality, etc. Several methods of fault detection and diagnosis (FDD) in building energy systems have been proposed since the late 1980s in order to reduce the consequences of faults in heating, ventilation, and air-conditioning (HVAC) systems. All the proposed FDD methods require laboratory data, or simulated data, or field data. Furthermore, the majority of the recently proposed FDD methods require labelled faulty and normal data to be developed. Thus, providing reliable ground truth data of HVAC systems with different technical characteristics is of great importance for advances in FDD methods for HVAC units. The primary objective of this study is to examine the operational behaviour of a typical single-duct dual-fan constant air volume air-handling unit (AHU) in both faulty and fault-free conditions. The investigation encompasses a series of experiments conducted under Mediterranean climatic conditions in southern Italy during summer and winter. This study investigates the performance of the AHU by artificially introducing seven distinct typical faults: (1) return air damper kept always closed (stuck at 0%); (2) fresh air damper kept always closed (stuck at 0%); (3) fresh air damper kept always opened (stuck at 100%); (4) exhaust air damper kept always closed (stuck at 0%); (5) supply air filter partially clogged at 50%; (6) fresh air filter partially clogged at 50%; and (7) return air filter partially clogged at 50%. The collected data from the faulty scenarios are compared to the corresponding data obtained from fault-free performance measurements conducted under similar boundary conditions. Indoor thermo-hygrometric conditions, electrical power and energy consumption, operation time of AHU components, and all key operating parameters are measured for all the aforementioned faulty tests and their corresponding normal tests. In particular, the experimental results demonstrated that the exhaust air damper stuck at 0% significantly reduces the percentage of time with indoor air relative humidity kept within the defined deadbands by about 29% (together with a reduction in the percentage of time with indoor air temperature kept within the defined deadbands by 7.2%) and increases electric energy consumption by about 13% during winter. Moreover, the measured data underlined that the effects on electrical energy demand and indoor thermo-hygrometric conditions are minimal (with deviations not exceeding 5.6% during both summer and winter) in the cases of 50% clogging of supply air filter, fresh air filter, and return air filter. The results of this study can be exploited by researchers, facility managers, and building operators to better recognize root causes of faulty evidences in AHUs and also to develop and test new FDD tools.

Faults of heating, ventilation, and air-conditioning (HVAC) systems can cause significant consequences, such as negatively affecting thermal comfort of occupants, energy demand, indoor air quality, etc. Several methods of fault detection and diagnosis (FDD) in building energy systems have been proposed since the late 1980s in order to reduce the consequences of faults in heating, ventilation, and air-conditioning (HVAC) systems. All the proposed FDD methods require laboratory data, or simulated data, or field data. Furthermore, the majority of the recently proposed FDD methods require labelled faulty and normal data to be developed. Thus, providing reliable ground truth data of HVAC systems with different technical characteristics is of great importance for advances in FDD methods for HVAC units. The primary objective of this study is to examine the operational behaviour of a typical single-duct dual-fan constant air volume air-handling unit (AHU) in both faulty and fault-free conditions. The investigation encompasses a series of experiments conducted under Mediterranean climatic conditions in southern Italy during summer and winter. This study investigates the performance of the AHU by artificially introducing seven distinct typical faults: (1) return air damper kept always closed (stuck at 0%); (2) fresh air damper kept always closed (stuck at 0%); (3) fresh air damper kept always opened (stuck at 100%); (4) exhaust air damper kept always closed (stuck at 0%); (5) supply air filter partially clogged at 50%; (6) fresh air filter partially clogged at 50%; and (7) return air filter partially clogged at 50%. The collected data from the faulty scenarios are compared to the corresponding data obtained from fault-free performance measurements conducted under similar boundary conditions. Indoor thermo-hygrometric conditions, electrical power and energy consumption, operation time of AHU components, and all key operating parameters are measured for all the aforementioned faulty tests and their corresponding normal tests. In particular, the experimental results demonstrated that the exhaust air damper stuck at 0% significantly reduces the percentage of time with indoor air relative humidity kept within the defined deadbands by about 29% (together with a reduction in the percentage of time with indoor air temperature kept within the defined deadbands by 7.2%) and increases electric energy consumption by about 13% during winter. Moreover, the measured data underlined that the effects on electrical energy demand and indoor thermo-hygrometric conditions are minimal (with deviations not exceeding 5.6% during both summer and winter) in the cases of 50% clogging of supply air filter, fresh air filter, and return air filter. The results of this study can be exploited by researchers, facility managers, and building operators to better recognize root causes of faulty evidences in AHUs and also to develop and test new FDD tools.

A significant energy release over a short time is achieved in replicable experiments involving the interaction of deuterium gas with constantan specimens. The experiments were carried out in a gas chamber where the injected deuterium interacted with heated specimens: (i) Many replicable experiments were performed at initial temperatures in the range of 666–681 °C. The temperatures of the specimens began to increase ~8 s after the beginning of deuterium injection as additional increases of 358–382 °C reached after ~30 s. The released excess power was in the range of 183–209 W, its density ranged from ~114–130 W/g, and the ratio of (output power)/(input power) was ≈ 3.76–3.91. (ii) Several replicable experiments were performed at initial temperatures of 950 °C. In all these experiments, explosive evaporation of the wires occurred immediately after the beginning of deuterium injection. The released excess momentary power was greater than 3400 W, its density was 2280 W/g, and the ratio of (output power)/(input power) was ≈ 16 and greater. The outcomes found were as follows: (a) the released excess power was not of electrical origin; (b) the released excess power of chemical origin was less than ~0.18% of the total released excess power; (c) the significant density of the released excess power; and (d) helium release, correlating with the energy release, was observed. The conclusion that the released energy is of nuclear origin was drawn.