Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Aeronautical engineering: A continuing bibliography with indexes (supplement 119)

This bibliography lists 341 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1980. Abstracts on the engineering and theoretical aspects of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems are presented. Research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles are also presented

Semantic-guided predictive modeling and relational learning within industrial knowledge graphs

The ubiquitous availability of data in today’s manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from different production domains requires high manual effort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in different domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a unified semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simplification and abstraction of statistical analysis and predictive modeling, especially in manufacturing. In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports efficient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms. In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm. Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment. We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation. They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world

Performance analysis on Free-piston linear expander

The growing global demand for energy and environmental implications have created a need to further develop the current energy generation technologies (solar, wind, geothermal, etc.). Recovering energy from low grade energy sources such as waste heat is one of the methods for improving the performance of thermodynamic cycles. The objective of this work was to achieve long-term steady state operation of a Free-Piston Linear Expander (FPLE) and to compare the FPLE with the currently existing expander types for use in low temperature energy recovery systems. A previously designed FPLE with a single piston, two chambers, and linear alternator was studied and several modifications were applied on the sealing and over expansion. An experimental test bench was developed to measure the inlet and outlet temperatures, inlet and outlet pressures, flow rate, and voltage output. A method of thermodynamic analysis was developed by using the first and second law of thermodynamics with air as the working fluid. The experimental tests were designed to evaluate the performance of the FPLE with varying parameters of inlet air pressure, inlet air temperature, and electrical resistance. The initial and steady-state operation of the FPLE were successfully achieved. An uncertainty analysis was conducted on the measured values to determine the accuracies of the calculated parameters. The trends of several output parameters such as frequency, average root mean square (RMS) voltage, volumetric efficiency, electrical-mechanical conversion efficiency, isentropic efficiency, irreversibility, actual expander work, and electrical power were presented. Results showed that the maximum expander frequency was found to be 44.01 Hz and the frequency tended to increase as the inlet air pressure increased. The FPLE achieved the maximum isentropic efficiency of 21.5%, and produced maximum actual expander work and electrical work of 75.13 W and 3.302 W, respectively

12th International Conference on Vibrations in Rotating Machinery

Since 1976, the Vibrations in Rotating Machinery conferences have successfully brought industry and academia together to advance state-of-the-art research in dynamics of rotating machinery. 12th International Conference on Vibrations in Rotating Machinery contains contributions presented at the 12th edition of the conference, from industrial and academic experts from different countries. The book discusses the challenges in rotor-dynamics, rub, whirl, instability and more. The topics addressed include: - Active, smart vibration control - Rotor balancing, dynamics, and smart rotors - Bearings and seals - Noise vibration and harshness - Active and passive damping - Applications: wind turbines, steam turbines, gas turbines, compressors - Joints and couplings - Challenging performance boundaries of rotating machines - High power density machines - Electrical machines for aerospace - Management of extreme events - Active machines - Electric supercharging - Blades and bladed assemblies (forced response, flutter, mistuning) - Fault detection and condition monitoring - Rub, whirl and instability - Torsional vibration Providing the latest research and useful guidance, 12th International Conference on Vibrations in Rotating Machinery aims at those from industry or academia that are involved in transport, power, process, medical engineering, manufacturing or construction

Aeronautical Engineering: A continuing bibliography with indexes (supplement 165)

This bibliography lists 466 reports, articles and other documents introduced into the NASA scientific and technical information system in August 1983

Experimental investigations on the aerodynamic and aeroacoustic characteristics of small UAS propellers

Unmanned aerial system (UAS) is a hot topic in both industry and academia fields. As a popular planform, the rotary-wing system gains more attentions. The small UAS propeller is the most important component in this system, which transfers electric energy into kinetic energy to accomplish fly missions. In the present work, several experimental studies have been performed to investigate the aerodynamic and aeroacoustic characteristics of small UAS propellers. First of all, by conducting force and flow filed measurements, the unsteady dynamic thrust and the wake structure of the propeller has been studied to explore the fundamental physics to help researchers and engineers to obtain a better understanding. Secondly, two kinds of bio-inspired the propellers have been designed and manufactured. Through a set of force, sound, and flow filed measurements, the aerodynamic and aeroacoustic performance of these propellers has been compared to the baseline propeller to evaluate the effects of aerodynamic efficiency and noise attenuation. It was found that the serrated trailing edge propeller could reduce the turbulent trailing edge noise up to 2 dB, and the maple seed inspired propeller could reduce the noise up to 4 dB with no effect on the aerodynamic performance. In addition, since the rotary-wing system consists more than one propeller, the rotor to rotor interaction on the aerodynamic and aeroacoustic performance also has been studied. By enlarging the separation distance between two propellers, the thrust fluctuation and noise generation could be restricted. Not only the design of the device itself has effect on the flying performance, the extreme weather also would affect it. Therefore, an icing research study on the small UAS propeller has been conducted to illustrate how does the ice formed on the propeller and how does the icing influence the aerodynamics performance and power consumption. During these experimental studies, the force measurements were achieved by a high sensitive force and moment transducer (JR3 load cell), which had a precision of ñ0.1N (ñ 0.25% of the full range). The sound measurements were conducted inside of the anechoic chamber located in the aerospace engineering department at Iowa State University. This chamber has a physical dimensions of 12ÃÂ12ÃÂ9 feet with a cut-off frequency of 100 Hz. The detailed flow structure downstream of the propeller was measured by a high-resolution digital PIV system. The PIV system was used to elucidate the streamwise flow structure downstream of the propeller. Both “free-run” and “phase-locked” PIV measurements were conducted to achieve the ensemble-average flow structure and detailed flow structure at certain phase angles

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section