110 research outputs found
SET2022 : 19th International Conference on Sustainable Energy Technologies 16th to 18th August 2022, Turkey : Sustainable Energy Technologies 2022 Conference Proceedings. Volume 4
Papers submitted and presented at SET2022 - the 19th International Conference on Sustainable Energy Technologies in Istanbul, Turkey in August 202
Metallurgical Process Simulation and Optimization
Metallurgy involves the art and science of extracting metals from their ores and modifying the metals for use. With thousands of years of development, many interdisciplinary technologies have been introduced into this traditional and large-scale industry. In modern metallurgical practices, modelling and simulation are widely used to provide solutions in the areas of design, control, optimization, and visualization, and are becoming increasingly significant in the progress of digital transformation and intelligent metallurgy. This Special Issue (SI), entitled “Metallurgical Process Simulation and Optimization”, has been organized as a platform to present the recent advances in the field of modelling and optimization of metallurgical processes, which covers the processes of electric/oxygen steel-making, secondary metallurgy, (continuous) casting, and processing. Eighteen articles have been included that concern various aspects of the topic
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
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
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
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
Z-Numbers-Based Approach to Hotel Service Quality Assessment
In this study, we are analyzing the possibility of using Z-numbers for
measuring the service quality and decision-making for quality improvement in the
hotel industry. Techniques used for these purposes are based on consumer evalu-
ations - expectations and perceptions. As a rule, these evaluations are expressed
in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the
respondent opinions based on crisp or fuzzy numbers formalism not in all cases
are relevant. The existing methods do not take into account the degree of con-
fidence of respondents in their assessments. A fuzzy approach better describes
the uncertainties associated with human perceptions and expectations. Linguis-
tic values are more acceptable than crisp numbers. To consider the subjective
natures of both service quality estimates and confidence degree in them, the two-
component Z-numbers Z = (A, B) were used. Z-numbers express more adequately
the opinion of consumers. The proposed and computationally efficient approach
(Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden-
tify the factors that required improvement and the areas for further development.
The suggested method was applied to evaluate the service quality in small and
medium-sized hotels in Turkey and Azerbaijan, illustrated by the example
Mining Technologies Innovative Development
The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research
Process Modeling in Pyrometallurgical Engineering
The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions
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
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