Recuperator Performance Assessment in Humidified Micro Gas Turbine Applications Using Experimental Data Extended With Preliminary Support Vector Regression Model Analysis

Paepe, Ward De; Pappa, Alessio; Coppitters, Diederik; Carrero, Marina Montero; Tsirikoglou, Panagiotis; Contino, Francesco

doi:10.1115/1.4049266

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…For the VUB test rig, a specific crossflow spray saturation tower (without packing material to limit the pressure drop) was developed [27] and integrated. The performance of the cycle [12] as well as the recuperator [28] and saturation tower [29] under humidified conditions has been tested and validated extensively.…”

Section: System Descriptionmentioning

confidence: 99%

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Colquhoun

Somehsaraei

Paepe

2022

Volume 4: Cycle Innovations; Cycle Innovations: Energy Storage

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Due to high penetration of renewables, the EU energy system is undergoing a transition from large-scale centralized generation towards small-scale distributed generation. The increasing share of intermittent renewables such as solar and wind has become the main driver for dispatchable distributed energy generation technologies to maintain the grid flexibility and stability. In this context, micro gas turbines (MGTs) with high fuel and operation flexibility could play a crucial role to guarantee the grid stability, enabling deeper penetration of the intermittent renewable energy sources. Despite this, the MGT market is still considered to be niche and there are R&D&I challenges that need to be addressed to further promote this technology in distributed generation applications. Innovative MGT cycles based on a cycle humidification concept, can be considered to obtain higher system performance. However, given the fact that MGTs are installed close to the consumption points, where they are operated by non-technical prosumers with very limited access to maintenance services, they should also offer high availability and reliability to avoid unexpected outages and secure the supply. Therefore, intelligent monitoring systems are needed that can support non-expert end-users to detect degradation and plan maintenance before a breakdown occurs. In this study we investigated and developed advanced methods based on artificial neural networks (ANNs) for condition monitoring of a humidified MGT cycle under real-life operational conditions. To create a high-performing model, extensive data preprocessing has been conducted to remove data outliers and select optimum model features, which provide best results. Additionally, the model hyperparameters such as learning rate, momentum and number of hidden nodes have been altered to achieve the most accurate predictions. The results of this study have provided a baseline ANN model capable of conducting condition monitoring of a micro-Humid Air Turbine (mHAT) system, which will be applied to additional studies in the future.

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Section: System Descriptionmentioning

confidence: 99%

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Colquhoun

Somehsaraei

Paepe

2022

Volume 4: Cycle Innovations; Cycle Innovations: Energy Storage

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“…Intercooling can be carried out using two methods [24,25]: a surface cooler (plate or tubular-plate heat exchanger) or contact cooling due to injected water evaporation. The use of surface heat exchangers is accompanied by significant aerodynamic losses [26,27]. It is simpler and more effective to use water injection into the high-pressure compressor air part [28,29].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Thermopressor with Incomplete Evaporation for Gas Turbine Intercooling Systems

Løvås

Konovalov

et al. 2022

Energies

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One of the promising ways to increase fuel and modern gas turbine energy efficiency is using cyclic air intercooling between the stages of high- and low-pressure compressors. For intercooling, it is possible to use cooling in the surface heat exchanger and the contact method when water is injected into the compressor air path. In the presented research on the cooling contact method, it is proposed to use a thermopressor that implements the thermo-gas-dynamic compression process, i.e., increasing the airflow pressure by evaporation of the injected liquid in the flow, which moves at near-sonic speed. The thermopressor is a multifunctional contact heat exchanger when using this air-cooling method. This provides efficient high-dispersion liquid spraying after isotherming in the high-pressure compressor, increasing the pressure and decreasing the air temperature in front of the high-pressure compressor, reducing the work on compression. Drops of water injected into the air stream in the thermopressor can significantly affect its characteristics. An increase in the amount of water increases the aerodynamic resistance of the droplets in the stream. Hence, the pressure in the flow parts of the thermopressor can significantly decrease. Therefore, the study aims to experimentally determine the optimal amount of water for water injection in the thermopressor while ensuring a positive increase in the total pressure in the thermopressor under conditions of incomplete evaporation. The experimental results of the low-consumption thermopressor (air consumption up to 0.52 kg/s) characteristics with incomplete liquid evaporation in the flowing part are presented. The research found that the relative water amount to ensure incomplete evaporation in the thermopressor flow part is from 4 to 10% (0.0175–0.0487 kg/s), without significant pressure loss due to the resistance of the dispersed flow. The relative increase in airflow pressure is from 1.01 to 1.03 (5–10 kPa). Based on experimental data, empirical equations were obtained for calculating the relative pressure increase in the thermopressor with evaporation chamber diameters of up to 50 mm (relative flow path length is from 3 to 10 and Mach number is from 0.3 to 0.8).

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“…The surrogate model is a mathematical model that can replace the complex and time-consuming numerical analysis model in 2 of 24 optimization design [3]. The more common surrogate models in engineering applications include the Kriging model [4], neural network [5,6] and support vector regression [7,8], among others. For this approximate replacement surrogate model, the optimization result largely depends on the sample set and the approximate accuracy of the surrogate model.…”

Section: Introductionmentioning

confidence: 99%

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

Xiao

Shi²,

Wu³

et al. 2021

Processes

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In this study, the blade shape of the squirrel-cage fan system inside the range hood was optimized using the surrogate model to improve the maximum volume flow rate. The influence of computational fluid dynamics (CFD) noise was concerned. The regression Kriging model (RKM) was used as a surrogate model to reflect the relationship between the design parameters of the blade and the volume flow rate. The parallel filling criterion after re-interpolation was used to improve the optimization efficiency further and ensure global optimization. Through experimental verification, we found that the relative error between the volume flow rate of the optimal sample of RKM and the experiment was only 0.4%. Compared with the prototype, the maximum volume flow rate of the optimal sample of RKM was increased by 2.9%, and the efficiency under the corresponding working conditions was increased by 2%. RKM was used to predict the velocity field of the volute and impeller exit section to explore the feasibility of the RKM in the flow field prediction. Research shows that the RKM cannot accurately predict the velocity of each grid on the cross-section. Still, it can accurately predict the changing trend of the velocity.

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Recuperator Performance Assessment in Humidified Micro Gas Turbine Applications Using Experimental Data Extended With Preliminary Support Vector Regression Model Analysis

Cited by 5 publications

References 24 publications

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Investigation of Thermopressor with Incomplete Evaporation for Gas Turbine Intercooling Systems

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

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