Gas turbine engine compressor increases the pressure and temperature of the air stream through it before entering the combustion chamber. Due to the extreme in rotational speed and high temperature, the gas turbine en...
Gas turbine engine compressor increases the pressure and temperature of the air stream through it before entering the combustion chamber. Due to the extreme in rotational speed and high temperature, the gas turbine engine compressor is subjected to a complex force system that causes different failures mode in the compressor disc. Therefore, the design and calculation of compressor discs always consist of very high requirements with the problem of reducing the volume and increasing the durability of the disc structure. This paper presents a computational method that optimizes the design of a compressor disc geometry that meets performance and durability parameters but has the lowest mass based on finite element method. The calculation program is implemented by software Abaqus 6.13, algorithms and analysis results are processed through the Python 3 programming language. A post-publication change was made to this article on 11 Jun 2020 to correct the pdf so that it matched the webpage.
In recent years, the prospects of performing fundamental and applied studies at the next-generation high-intensity laser facilities have greatly stimulated the interest in performing large-scale simulations of laser i...
In recent years, the prospects of performing fundamental and applied studies at the next-generation high-intensity laser facilities have greatly stimulated the interest in performing large-scale simulations of laser interaction with matter with the account for quantum electrodynamics (QED) processes such as emission of high energy photons and decay of such photons into electron-positron pairs. These processes can be modelled via probabilistic routines that include frequent computation of synchrotron functions and can constitute significant computational demands within accordingly extended Particle-in-Cell (QED-PIC) algorithms. In this regard, the optimization of these routines is of great interest. In this paper, we propose and describe two modifications. First, we derive a more accurate upper-bound estimate for the rate of QED events and use it to arrange local sub-stepping of the global time step in a significantly more efficient way than done previously. Second, we present a new high-performance implementation of synchrotron functions. Our optimizations made it possible to speed up the computations by a factor of up to 13.7 depending on the problem. Our implementation is integrated into the PICADOR and Hi-Chi codes, the latter of which is distributed publicly (https://***/hi-chi/pyHiChi).
Today’s manufacturing companies have begun to increasingly use digital tools to increase their company production efficiency, to ensure a low-price level, high quality, and fast delivery time of the product or servic...
Today’s manufacturing companies have begun to increasingly use digital tools to increase their company production efficiency, to ensure a low-price level, high quality, and fast delivery time of the product or service in the conditions of increasing competition in the globalized economy. An important part of improving the company’s efficiency indicators is the ever-more relevant organization of transport operations on the production floor and the digitization and automation of these processes. More and more companies have adopted or plan to do so in the near future with autonomous mobile robots (AMR) to manage production logistics. The rapid development of the Internet of Things (IoT) and the advanced hardware and control software of AMR enable autonomous operations in dynamic environments, which gives them the ability to communicate and negotiate independently with other resources, such as machines and systems, and thus decentralize decision-making in production processes. Decentralized decision-making allows the system to dynamically respond to changes in system state and environment. Such developments have affected traditional planning and control methods and decision-making processes, but also place greater demands on the software used and integrated Artificial Intelligence (AI) algorithms for the execution of these decisions. In this study, we provide an overview of how to pilot the integration of an AMR system with AI functionality in the production logistics of the food industry using the concept of a 3D virtual factory. The paper proposes an approach for the performance analysis of AMR for the transportation of goods on the production factory floor, which is based on 3D layout creation and simulation, monitoring of key performance indicators (KPIs), and integration of AI for proactive decision-making in production planning. The relevance and feasibility of the proposed approach are demonstrated by a food industry case study.
The world is moving towards non-fossil fuel-powered cars due to limited supplies and environmental risks in the form of pollution and global warming. Automotive industries have shifted their research and development f...
The world is moving towards non-fossil fuel-powered cars due to limited supplies and environmental risks in the form of pollution and global warming. Automotive industries have shifted their research and development from mechanically designed powertrain system to the into electrically operational vehicles. This drawback has created the need to develop automotive electrification projects and emerging technologies for advance combination of both propulsion systems known as hybrid electric vehicles (HEVs) or combined source electric vehicle. Hybrid Electric Vehicle is the first success of the front running solution like today. Almost all-Hybrid Electric Vehicles are primarily battery powered. Although there are zero emissions, in order to have an efficient, optimal, safe, and long-lasting battery system, it is necessary to keep the temperature of the battery system within the specified limits. HEVs are developed under global protocols to increase vehicle fuel efficiency and minimize vehicle emissions. Such combined system has changed from their emergent state and into the optimistic solution that can solve environmental problem facing planet earth. To date, we do not have any algorithm capable of predicting the SOH of all types of batteries by monitoring the parameters over time. There is a strong need for a solution in the form of a control algorithm/program, which can optimize the operation of the battery cooling system for maximum battery life with minimum power consumption. Hence, the paper highlighted a special algorithm/pattern that shows repeated performance control for the best efficiency and long life of HEV batteries. In addition, MATLAB & PSIM Simulink software is used for this new solution for all battery system parameters to be monitored and optimized AI-based algorithms can be used to control the cooling system of the high voltage batteries used in HEV.
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