Design and optimization of squirrel cage geometries in aircraft engines toward robust whole engine dynamics

In this work, an coupled end-to-end approach for the optimization of the rotor dynamic behavior of a dual-spool aircraft engine along with fatigue life optimization of squirrel cages (SQC) is presented. A realistic model to simulate the rotor dynamics is created, where the high-pressure (HP) rotor is supported by two squirrel cages. The aim of this work is to find a robust rotor dynamics design by shifting a critical speed to higher rotational speed, and at the same time improving the squirrel cage design with respect to fatigue life. Fully automatized coupled analysis process chain is implemented, allowing to compute the influence of the SQCs geometry variation onto the full rotor dynamics and structural performance of the SQC. Two global optimization techniques are employed to explore the SQCs design space and find optimal 3D geometries, using the aforementioned coupled process. Optimization results are compared and discussed in detail, indicating the importance of the numerical optimization to improve fatigue life of the squirrel cage. It is shown that optimized and non-optimized SQC designs, both fulfilling rotor dynamics goals, can have significantly different performance regarding their fatigue life. Moreover, the advantage of the coupled process is illustrated, allowing to find superior SQC designs by considering both disciplines simultaneously in comparison with a sequential (uncoupled) approach, when the target elastic properties of an SQC, selected only based on the rotor dynamics requirements, may lead to sub-optimal fatigue life

Monte carlo computational software and methods in radiation dosimetry

The fast developments and ongoing demands in radiation dosimetry have piqued the attention of many software developers and physicists to create powerful tools to make their experiments more exact, less expensive, more focused, and with a wider range of possibilities. Many software toolkits, packages, and programs have been produced in recent years, with the majority of them available as open source, open access, or closed source. This study is mostly focused to present what are the Monte Carlo software developed over the years, their implementation in radiation treatment, radiation dosimetry, nuclear detector design for diagnostic imaging, radiation shielding design and radiation protection. Ten software toolkits are introduced, a table with main characteristics and information is presented in order to make someone entering the field of computational Physics with Monte Carlo, make a decision of which software to use for their experimental needs. The possibilities that this software can provide us with allow us to design anything from an X-Ray Tube to whole LINAC costly systems with readily changeable features. From basic x-ray and pair detectors to whole PET, SPECT, CT systems which can be evaluated, validated and configured in order to test new ideas. Calculating doses in patients allows us to quickly acquire, from dosimetry estimates with various sources and isotopes, in various materials, to actual radiation therapies such as Brachytherapy and Proton therapy. We can also manage and simulate Treatment Planning Systems with a variety of characteristics and develop a highly exact approach that actual patients will find useful and enlightening. Shielding is an important feature not only to protect people from radiation in places like nuclear power plants, nuclear medical imaging, and CT and X-Ray examination rooms, but also to prepare and safeguard humanity for interstellar travel and space station missions. This research looks at the computational software that has been available in many applications up to now, with an emphasis on Radiation Dosimetry and its relevance in today's environment. © 2021 by the author(s)

Efficient intent classification and entity recognition for university administrative services employing deep learning models

The design and implementation of a domain specific conversational agent requires efficient Natural Language Understanding (NLU). The task is harder when multiple languages have to be supported, and training datasets can be beneficial. This work focuses on the development of an intelligent system, an automated multilingual customer service conversational agent (chatbot) for university students, which supports both Greek and English and combines Intent Classification or Intent Extraction (IE) and Named Entity Recognition (NER) to understand the content (i.e. type of actions conveyed and respective entities) of users' messages. We focus on the development of the fundamental tasks required by a conversational agent to provide customer services in the education industry and manage requests with instant responses and increased customer satisfaction. Instead of handling IE and NER separately, as it is common in the related work, we develop a joint model that combines Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF) layers and generates outputs both for IE and NER. We introduce a novel, open access dataset for customer services in education industry, the UniWay dataset, that has been used for training and evaluating our model, comprises students' questions in English and Greek about essential information related to their studies. A comparative evaluation of the proposed model versus state-of-the-art standalone and joint model solutions in UniWay and xSID datasets, results in improvement of the performance for the IE task up to 1.4% and it is on par with the state-of-the-art for the NER task. These results justify the intuition that closed domains can benefit from less sophisticated architectures, but less costly in terms of computational and memory resources, that jointly resolve multiple NLU tasks

A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors

This paper examines the application of machine learning techniques in NILM methodologies based on the first three odd harmonic order current vectors as the only attributes of the appliances. Proper formulation of the measured current waveform of appliances' combinations is also presented. We apply our methodology on performed measurements of typical Low Voltage residential installations considering harmonic order currents as the input features for both the training and disaggregation scheme. Our results support the hypothesis that the identification performance is enhanced when higher harmonic currents are included in the NILM methodology. © 2019 IEEE