Prosessisimulointityökalun kehittäminen pienelle kaasuturbiinille

Power consumption is increasing in the next decades and demands to fulfill this need are expected to turn towards cleaner and more efficient energy production. While renewables are expected to increase, their growth rate cannot compensate for the increase in power consumption alone, and gaseous fuels, such as natural gas, are expected to play a big role along renewables in this transition to produce energy cleaner and more efficiently. Demand for efficient and exact energy also drives decentralization, meaning that energy can be produced where demand is, to fulfill needs precisely and quickly. Many industrial applications also require process heat and with decentralized combined heat and power production, great efficiency increase is possible. Small gas turbines excel in this type of combined heat and power production with versatile fuels, also including natural gas. With this current continuing trend in the energy market, increase in the gas turbine installations can be expected. Interest towards gas turbines increases the importance of gas turbine performance models. In off-design conditions, performance is significantly affected by the load and ambient conditions. With accurate models, the performance of engines can be predicted for each application and designing costs and time can be reduced. During operation, drive can be optimized to reach higher efficiencies and with engine monitoring, maintenances can be planned to be condition-based, not predictive based. In this master’s thesis, performance prediction model was created for intercooled and recuperated gas turbine process with two spools, both spools including generator, compressor and turbine. The model was requested by the company to replace currently used model, with one which could better correspond to the company’s need. The developed model was steady-state, full range performance model which used Newton-Raphson iteration. The developed model was compared to old model and results were in-line. The new model was as requested by the company excluding some attributes which could not be included in the scope of this thesis but will be added to the model later

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

High-Fidelity Computational Analysis of the Aerothermal Performance of In-serviced Jet Engine Blades

In today’s civil aviation, the struggle for higher jet engine efficiencies has pushed the manufactures into the continuous challenge of developing new and better design and optimization strategies. In the information age, it is only natural that a great deal of this effort is going to be carried out by means of computational analysis. That is to say, these design and optimization strategies rely heavily on the use of computational models, and thus the search for a better design is hinged upon the search for a better model. A notable product of this search is the “robust design” philosophy, which aims to consider the variability in geometry and operating conditions that every component will invariably experience in real-world conditions. In general, the key element in this evolution process is for the model to be capable of accounting for more and more aspects of the reality of the problem at hand, while still being affordable in terms of computational costs. In this case, the problem is represented by the aero-thermal behavior of the jet engine’s most characteristic components: the blades. As mentioned above, to increase the fidelity of the model, key aspects that characterize the real operation of these components can be included in it, beginning with the geometry. While most of the computational performance analysis is conducted on nominal designs, it is important to consider that, during most of their service life, the turbine blades are going to operate with a geometry that is increasingly affected by deviation from nominal. This is due to both manufacturing variation and in-service damage. These geometric deviations can be measured on the blades after an engine overhaul, providing highly useful information on the damage modes of the engine. By digitalizing these geometries, engineers can quantify and parametrize the geometric deviation. Furthermore, by creating computational grids around these geometries, a high-fidelity CFD study revolving around the performance of the real blades can be carried out, shedding light on the correlation between the geometric deviation parameters and aerodynamic performance loss. Naturally, this geometric deviation also has a significant impact on the thermal behavior of the blades, affecting the distribution of the Heat Transfer Coefficient (HTC) over the blades’ surfaces. Even when modelling the nominal case, it is often common practice to use a simplified version of the geometry, where the internal cooling system is replaced with source terms. Although this reduces the costs of the CFD simulations, it obviously subtracts from the model’s accuracy. Furthermore, it is particularly important to model the fluid-solid thermal exchange, and the rotor-stator unsteady interaction. All these fidelity-related aspects that can impact the model’s accuracy are investigated in the present work

Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Fault detection methods for vapor-compression air conditioners using electrical measurements

Includes bibliographical references (p. 409-424).Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2008.(cont.) This method was experimentally tested and validated on a commercially available air handler and duct system. In the second class of faults studied, liquid refrigerant, rather than vapor, enters the cylinder of a reciprocating compressor during operation. Since the higher cylinder pressures that result can cause substantial damage and are difficult to measure directly, a method for detecting this fault is proposed that only uses observations of the compressor voltage and current. The performance of this fault detection method was also experimentally validated with electrical and mechanical measurements on a semi-hermetic compressor. The final diagnostic method detects refrigerant leakage in a residential air conditioning system by identifying changes in the system's cycling behavior. This method also uses measurements of the compressor's electrical power, as well as a small set of temperature measurements, to determine the presence of the fault. This fault detection method was developed and tested on an occupied residence.This thesis proposes novel methods that use measurements of electrical terminal variables to identify common mechanical faults in vapor-compression air-conditioners. The importance of air-conditioning in many applications and the current cost of energy both provide powerful incentives for developing fault detection methods, as faults can have a significant impact on the system's functionality and efficiency. While many extant fault detection and diagnostic (FDD) methods depend upon arrays of mechanical sensors, concerns about sensor reliability and the overall complexity of these methods motivated this research into electrically-based FDD methods, which typically incorporate smaller numbers of more reliable sensors. These electrically-based methods use models of the electromechanical energy conversion process to correlate observed changes in the electrical variables to changes caused by faults in the mechanical load. Such an approach allows both electrical and mechanical faults to be identified via the same sensor apparatus, and makes it possible to identify faults that manifest themselves on a wide range of timescales.FDD methods for three different classes of common faults are studied in this research. The first diagnostic method identifies blockage or leakage in a duct via electrical measurements made at the fan motor terminals. The estimates of the motor's speed and torque developed at the operating point are used in tandem with a fan curve to directly estimate the airflow through a duct system without any additional mechanical measurements.by Christopher Reed Laughman.Ph.D