scholarly journals Research on Real-Time Model of Turboshaft Engine with Surge Process

2022 ◽  
Vol 12 (2) ◽  
pp. 744
Author(s):  
Xinglong Zhang ◽  
Lingwei Li ◽  
Tianhong Zhang
Keyword(s):  
Real Time ◽  
Mass Flow ◽  
Detection Methods ◽  
Rotor Speed ◽  
Gas Temperature ◽  
Component Level ◽  
Time Model ◽  
Level Model ◽  
The Stability ◽  
Turboshaft Engine

The main data source for the verification of surge detection methods still rely on test rigs of the compressor or the whole engine, which makes the development of models of the whole engine surge process an urgent need to replace the high-cost and high-risk surge test. In this paper, a novel real-time surge model based on the surge mechanism is proposed. Firstly, the turboshaft engine component level model (CLM) and the classic surge dynamic model, Moore-Greitzer (MG) model is established. Then the stability of the MG model is analyzed and the compressor characteristics in the classical MG model are extended to establish the extended MG model. Finally, this paper considers the coupling relationship of the compressor’s rotor speed, mass flow and pressure between CLM and the extended MG model to establish the real-time model of the turboshaft engine with surge process. The simulation results show that this model can realize the whole surge process of the turboshaft engine under multiple operating states. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means that the model proposed can be further applied to the research of surge detection and anti-surge control.

Design and Analysis of a Real-Time Hot Gas Temperature Estimator for Heavy-Duty Gas Turbines

2015 ◽  
Author(s):  
Eric A. Müller ◽  
Adrian Ticǎ
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Gas Temperature ◽  
Heavy Duty ◽  
Component Level ◽  
Load Range ◽  
Hot Gas ◽  
Dynamic Tracking ◽  
Heavy Duty Gas Turbine

The knowledge about a relevant process and lifetime indicative quantity, such as the hot gas temperature, is crucial for the control of a gas turbine. Since this indicative process quantity usually cannot be directly measured, it has to be estimated. The paper describes a model-based method to accurately estimate in real-time the hot gas temperature of a heavy-duty gas turbine. The method follows a well-balanced trade-off between resulting prediction accuracy and involved computational complexity. It takes advantage of the capability of a component-level dynamic model to predict the system behaviour and of the capacity of a dynamic tracking filter to adapt to the current gas turbine conditions. In a simulation study, it is shown that the proposed design can provide an accurate hot gas temperature estimation over the entire gas turbine load range, along the gas turbine lifecycle, and during fast transient manoeuvres.

Grey-Box Modeling for HCCI Engine Control

2013 ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Engine Control ◽  
Gas Temperature ◽  
Real Time Control ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

scholarly journals Aero-Engine Real-Time Models and Their Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiajie Chen ◽  
Zhongzhi Hu ◽  
Jiqiang Wang
Keyword(s):  
Real Time ◽  
Future Research ◽  
Component Level ◽  
Time Model ◽  
Modeling Methods ◽  
Time Performance ◽  
Model Based ◽  
Time Modeling ◽  
Aero Engine ◽  
Flight Envelope

Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods.

Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels

2014 ◽  
Vol 136 (10) ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Gas Temperature ◽  
Real Time Control ◽  
Neural Network Models ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

Research on Integrated Control Method of Tiltrotor with Variable Rotor Speed Based on Two-Speed Gearbox

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Yong Wang ◽  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Zhigui Xu
Keyword(s):  
Control Method ◽  
Integrated Control ◽  
Classical Mechanics ◽  
Fast Response ◽  
Rotor Speed ◽  
Component Level ◽  
Variable Model ◽  
Element Analysis ◽  
Shift Control ◽  
Turboshaft Engine

Abstract The process of rotor speed variation under tiltrotor cruise state has been studied, and the integrated variable speed control method of tiltrotor based on two-speed gearbox is proposed. Firstly, a nonlinear model predictive controller (NMPC) based on state variable model of the component-level model of the turboshaft engine is designed. Then based on the integrated engine model, the two-speed dual path tiltrotor driveline comprehensive simulation model was developed by utilizing gear kinematics theory, blade element analysis and the theory of classical mechanics. Finally, both a Parallel Shift Control (PSC) strategy and a Sequential Shift Control (SSC) strategy in tiltrotor cruise state were analyzed and compared with conventional PID controller. It is shown that the rotors’ speed can synchronously vary by 50 percent under the PSC strategy in tiltrotor cruise state. Meanwhile, disengaging the engine in turn by freewheel clutches can reduce the rotors’ speed from 190 rpm to 102.5 rpm along the specified path under the SSC strategy. The overshoot and droop amount of the power turbine speed can be reduced to less than 1.5 % with the steady error no more than 0.2 % through NMPC, which realizes the fast response control of the turboshaft engine.

A new method to improve the real-time performance of aero-engine component level model

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Changpeng Cai ◽  
Qiangang Zheng ◽  
Haibo Zhang
Keyword(s):  
Real Time ◽  
Model Simulation ◽  
Interpolation Method ◽  
Component Level ◽  
The Real ◽  
Time Performance ◽  
Starting Point ◽  
Engine Component ◽  
Aero Engine ◽  
Level Model

AbstractIn order to improve the real-time performance of aero-engine component-level models, an automatic fast positioning interpolation method is proposed. Based on the maximum parameter slope, this method can automatically determine the interpolation cut in point, change the disadvantage of low efficiency of traditional sequential interpolation from the starting point, effectively reduce the interpolation interval, thus greatly improving the efficiency of interpolation. The method is applied to the calculation of gas thermodynamic parameters and the interpolation of the characteristic of rotating parts ,so as to ameliorate the real-time performance of the single-stage flow path calculation of the component-level model. Simulation results show that, compared with the traditional method, the method proposed in this paper improves the fan characteristic calculation efficiency by 47.5%, reduces the time of single complete flow calculation by 74.3% when the dynamic and steady-state accuracy changes are less than 0.4%, which greatly improves the real-time performance of the component-level model.

RPS24c Isoform Facilitates Tumor Angiogenesis Via Promoting the Stability of MVIH in Colorectal Cancer

2020 ◽  
Vol 20 (5) ◽  
pp. 388-395 ◽  
Author(s):  
Yue Wang ◽  
Youjun Wu ◽  
Kun Xiao ◽  
Yingjie Zhao ◽  
Gang Lv ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Ribosomal Protein ◽  
Real Time ◽  
Tumor Angiogenesis ◽  
Real Time Pcr ◽  
Molecular Mechanisms ◽  
Migration And Invasion ◽  
Binding Interaction ◽  
Tubule Formation ◽  
The Stability

Background: Colorectal cancer (CRC) is the second leading cause of death worldwide, and distant metastasis is responsible for the poor prognosis in patients with advanced-stage CRC. RPS24 (ribosomal protein S24) as a ribosomal protein, multiple transcript variant encoding different isoforms have been found for this gene. Our previous studies have demonstrated that RPS24 is overexpressed in CRC. However, the mechanisms underlying the role of RPS24 in tumor development have not been fully defined. Methods: Expression of RPS24 isoforms and lncRNA MVIH in CRC tissues and cell lines were quantified by real-time PCR or western blotting assay. Endothelial tube formation assay was performed to determine the effect of RPS24 on tumor angiogenesis. The cell viability of HUVEC was determined by MTT assay, and the migration and invasion ability of HUVEC were detected by transwell assay. PGK1 secretion was tested with a specific ELISA kit. Results: Here, we found that RPS24c isoform was a major contributor to tumor angiogenesis, a vital process in tumor growth and metastasis. Real-time PCR revealed that RPS24c isoform was highly expressed in CRC tissues, while other isoforms are present in both normal and CRC tissues with no statistical difference. Moreover the change of RPS24 protein level is mainly due to the fluctuation of RPS24c. Furthermore, we observed that silencing RPS24c could decrease angiogenesis by inhibiting tubule formation, HUVEC cell proliferation and migration. Additionally, we investigated the molecular mechanisms and demonstrated that RPS24c mRNA interacted with lncRNA MVIH, the binding-interaction enhanced the stability of each other, thereby activated angiogenesis by inhibiting the secretion of PGK1. Conclusion: RPS24c facilitates tumor angiogenesis via the RPS24c/MVIH/PGK1 pathway in CRC. RPS24c inhibition may be a novel option for anti-vascular treatment in CRC.

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

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