gas turbine
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2022 ◽  
Vol 51 ◽  
pp. 101959
Lauren N. Rupiper ◽  
Brent B. Skabelund ◽  
Rhushikesh Ghotkar ◽  
Ryan J. Milcarek

2022 ◽  
Vol 309 ◽  
pp. 118391
Miel Sharf ◽  
Iliya Romm ◽  
Michael Palman ◽  
Daniel Zelazo ◽  
Beni Cukurel

2022 ◽  
Vol 14 (2) ◽  
pp. 870
Mohammad Alsarayreh ◽  
Omar Mohamed ◽  
Mustafa Matar

Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

2022 ◽  
Vol 0 (0) ◽  
Venkateshwarlu Mogullapally ◽  
Sanju Kumar ◽  
Bukkapatna Ananthappa Rajeevalochanam ◽  
Rashmi Rao

Abstract Bladed disks are important components of gas turbine engine. Rotor disk spool drum assemblies of gas turbine engine constitute 20–25% of total engine weight. Increasing thrust-to-weight ratio and engine life is paramount for designers. Blisk reduces significantly weight of rotor, compared against conventional disks for aero engines. This paper brings out specific challenges faced while re-designing bladed disk into blisks including structural integrity aspects under various operating loads. This paper presents a case study on re-design of typical compressor bladed disk into a blisk, without changing the flow path or airfoil configuration, within space constraints. Weight reduction of rotor disk is carried out using shape optimization technique. Blisk configuration is derived from existing bladed disk general arrangement. This paper describes methodology of weight optimization of blisk using ‘HyperStudy’ tool considering static and dynamic 3D models with ANSYS solver. APDL fatigue life macro is developed for fatigue life prediction, using strain-life approach. In this paper 3D bladed disk, baseline and optimized 3D blisk modal analyses results are used to ensure minimum interferences for engine operating conditions. The developed methodology for optimization can be appreciated by significant weight reduction (30%), while meeting design criteria and increased fatigue life.

Ahmad Kamal Mohd Nor ◽  
Srinivasa Rao Pedapati ◽  
Masdi Muhammad ◽  
Víctor Leiva

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

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