Wyniki wyszukiwania dla: TURBINE LINEAR REGRESSION MACHINE LEARNING OPTIMIZATION ORC - MOST Wiedzy

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Wyniki wyszukiwania dla: TURBINE LINEAR REGRESSION MACHINE LEARNING OPTIMIZATION ORC
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Wyniki wyszukiwania dla: TURBINE LINEAR REGRESSION MACHINE LEARNING OPTIMIZATION ORC

  • Impact of rotor geometry optimization on the off-design ORC turbine performance

    Publikacja
    • Ł. Witanowski
    • P. Klonowicz
    • P. Lampart
    • P. Klimaszewski
    • T. Suchocki
    • Ł. Jędrzejewski
    • D. Zaniewski
    • P. Ziółkowski

    - ENERGY - Rok 2023

    The paper describes the method of CFD based Nelder-Mead optimization of a 10 kW single-stage axial turbine operating in an ORC system working on R7100. The total-to-static isentropic efficiency is defined as an objective function. Multi-point linear regression is carried out to determine the significance of the objective function arguments and to pick up the set of particular variables and characteristic quantities (e.g. flow angles)...

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  • Multi-objective optimization of the ORC axial turbine for a waste heat recovery system working in two modes: cogeneration and condensation

    Publikacja

    - ENERGY - Rok 2023

    Due to the demand of the district heating network and electric power grid ORC turbines can operate in the condensation and cogeneration modes. This approach requires the design of an expander which is characterized by high efficiency in each mode of operation. The paper is devoted to a multi-objective efficiency optimization of a one stage axial ORC turbine working on MM (Hexamethyldisiloxane). An Implicit Filtering algorithm (IF)...

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  • Ireneusz Czarnowski Prof.

    Osoby

    IRENEUSZ CZARNOWSKI is a graduate of the Faculty of Electrical Engineering at Gdynia Maritime University. He gained a doctoral degree in the field of computer science at Poznan University of Technology and a postdoctoral degree in the field of computer science at Wroclaw University of Science and Technology. Since 1998 is associated with Gdynia Maritime University, currently is a professor of computer science in the Department...

  • TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads

    TensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing...

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  • Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects

    Publikacja

    Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...

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  • Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features

    Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...

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  • Machine learning applied to acoustic-based road traffic monitoring

    Publikacja

    - Rok 2022

    The motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...

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  • Machine learning applied to acoustic-based road traffic monitoring

    The motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...

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  • Speech Analytics Based on Machine Learning

    In this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...

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  • Complex multidisciplinary optimization of turbine blading systems

    Publikacja

    - ARCHIVES OF MECHANICS - Rok 2012

    The paper describes the methods and results of direct optimization of turbine blading systems using a software package Opti_turb. The final shape of the blading is obtained from minimizing the objective function, which is the total energy loss of the stage, including the leaving energy. The current values of the objective function are found from 3D RANS computations (from a code FlowER) of geometries changed during the process...

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