development of a simple tool able to study flight cycles gas turbine performance and emissions

Abstract During flight cycles, engine conditions may vary drastically from the design ones. Particularly, the high-security standard and the necessity to reduce emissions, have led to a continuous attention to the efficiency in all flight conditions. The objective of the present study is to develop a quick and easy way to analyze and investigate an aero-engine performance during different flight cycle conditions. Firstly, taking into consideration the GE 90 engine and its design characteristics, with the help of the in-house software ESMS (Energy System Modular Solver), many different flight conditions are tested, varying TIT, altitude, and Mach number independently. A big effort has been dedicated to this preliminary phase in order to get a perfect convergence for all the circumstances. Secondly, the aim was to adopt a wide-ranging approach to the problem: results have been stored and a sort of mapping has been created for that particular engine. This has allowed the final user to reproduce any flight cycle and to get the results in terms of thermodynamic parameters, as well as power and fuel consumption, without the necessity to run the code for each scenario. Time is drastically reduced and this lets the user quickly change input parameters to obtain the desired results. Thirdly, using a correlation, emissions, and in particular the emission index parameter of NOx, has been evaluated for all the flight point simulated. This procedure has been applied to a common flight path for the GE 90 engine and results have been compared to the nominal ones, proving the robustness of the software and the solidity of the procedure

Analysis of the GT26 single-shaft gas turbine performance and emissions

Abstract The progressive developments in terms of gas turbine materials as well as blade cooling systems have led to a continuous growth in the turbine inlet temperature (TIT) and the overall pressure ratio (OPR). This means higher thermal efficiency and power output. Other techniques to achieve better performance can be the adoption of a heat recovery system, an intercooler system or the reheat, as well as a combined cycle application. Furthermore, the higher the TIT and OPR the higher the NOx emissions. Nowadays, with an always stricter emissions legislation, it is particularly important to keep emission levels as low as possible. In the present work, a performance analysis has been conducted with the in-house modular tool ESMS (Equation Solver Modular System). The software simply represents the engine with separate blocks, solving the energy and the continuity equations. Firstly, the design process has been performed on the Ansaldo Energia GT26 machine, equipped with reheat, based on the manufacturer datasheet. Secondly, off-design simulations have been done, changing respectively the fuel mass flow in the 1st burner (EV) and in the 2nd burner (SEV). Therefore, both TIT and power output change. A sensitivity analysis of the thermal efficiency η and the power output with respect to both fuel flows shows how, for part load operations with a half of the design power output, it is better to change the SEV fuel flow only. It can also demonstrate that the high-pressure turbine (HPT) power output is more insensitive to SEV fuel flow than the low-pressure turbine (LPT) one. EV fuel flow variations affect both the HPT and the LPT behaviour. Eventually, a correlation for the NOx emissions has been characterized and results illustrate that NOx emissions are strictly related to the EV fuel flow: in fact, the O2 level in the SEV burner is sensibly lower than in the first one, thus contributing to lower emissions

Metodologie di uncertainty quantification per sistemi di raffreddamento in applicazioni di turbine a gas

Historically, the design of turbomachinery components was mainly done through experimental tests; over the years, with the increase of computing resources, there has been increasing use of computational analysis. Numerical simulations are important tools for designers because they allow having a complete understanding of the problem, in relatively short times and with low general costs. Although these analyses have a good predictive level, they are often used when input quantities that characterize the problem are roughly known. These gaps lead to the inclusion of uncertainties within the code, which propagate and eventually influence the solution. In the last fifteen years, statistical aspects have been combined into numerical simulations in order to assess the influence of the unknown parameters in the initial stages of the project. The final common objective is to optimize the various components in order to find out the configuration in which the machine is independent of the uncertainties that may afflict it, thus arriving at a robust design. The aim of this thesis was to explore and apply several methodologies of "uncertainty quantification" (UQ) to numerical codes used in turbomachinery applications, which allow estimating the uncertainties that affect the results of numerical simulations. Both sampling-based methods and stochastic expansion methods were investigated. After an initial benchmarking phase, the software DAKOTA was selected to carry out the UQ analyses. The first part of the work involved a 1-D thermal analysis on a full annular lean-burn aeronautical combustor tested at CIAM during the LEMCOTEC (Low Emissions COre-engine TEChnologies) European project. The analysis was carried out using the one-dimensional code "Therm-1D", developed by DIEF of the University of Florence. Three main uncertainty analyses were investigated depending on the input parameters considered: geometrical, heat transfer coefficient tuning factors, and thermal loads. In particular, the classical Monte Carlo analysis is compared with four stochastic expansion processes: Gauss quadrature, total order with LHS sampling, stochastic collocation, and Smolyak. The analyses proved how these methods give optimum results with a sensible lower amount of simulations. Lastly, an analysis including all the input variables considered was performed and results were compared with experimental data. Working on a 1-D solver has allowed obtaining a large amount of data with modest computational costs: this part was crucial in order to better understand the different methodologies and to have a clearer picture of the potentialities of the software. The second part of the work focused on applying the acquired concepts to a high-fidelity code. Based on an experimental study, a full 3-D computational fluid dynamic (CFD) study using the software ANSYS was carried out in order to assess the film cooling performance of a prismatic gas turbine vane made by additive manufacturing. Both steady and unsteady simulations were performed: the first ones using a RANS approach and the latter using a hybrid LES-RANS approach. For the UQ analysis, only RANS simulations in conjunction with a specific stochastic expansion method were adopted to save computational resources. The influences of the geometric uncertainties of the holes were evaluated: the hole dimension, the streamwise inclination angle and the inlet fillet radius of the hole. Output parameters considered were the film cooling effectiveness, the blowing ratio and the discharge coefficients of the holes. Results will show how a polynomial chaos approach that required 8 evaluations is able to reproduce what the standard Monte Carlo analysis does (with more than 1000 evaluations) with an optimum grade of accuracy. Moreover, results prove how the position tolerance of the holes on the blade, as well as the hole dimension, is extremely important for the film cooling effectiveness, in particular when dealing with additive manufacturing processes.Storicamente, la progettazione dei componenti di turbomacchine è stata effettuata principalmente attraverso prove sperimentali; nel corso degli anni, con l'aumento delle risorse informatiche, si è registrato un crescente ricorso all'analisi computazionale. Le simulazioni numeriche sono strumenti importanti per i progettisti perché permettono di avere una visione completa del problema, in tempi relativamente brevi e con costi generali contenuti. Anche se queste analisi hanno un buon livello predittivo, sono spesso utilizzate quando le quantità di input che caratterizzano il problema sono note approssimativamente. Queste lacune portano ad incertezze nel codice, che si propagano ed influenzano il risultato finale. Negli ultimi quindici anni, gli aspetti statistici sono stati inclusi in simulazioni numeriche per valutare l'influenza dei parametri non noti nelle fasi iniziali del progetto. L'obiettivo finale comune è quello di ottimizzare i vari componenti per individuare la configurazione in cui la macchina è indipendente dalle incertezze che la affliggono, arrivando così al “robust design”. Lo scopo di questa tesi è stato quello di studiare e applicare diverse metodologie di "Uncertainty quantification" (UQ) ai codici numerici utilizzati nelle applicazioni relative alle turbomacchine, che permettono di stimare l'incertezza che influenza i risultati delle simulazioni numeriche. Sono stati studiati sia metodi basati sul campionamento che metodi di espansione stocastica. Dopo una fase iniziale di benchmark, è stato selezionato il software DAKOTA per effettuare le analisi dei UQ. La prima parte del lavoro ha riguardato un'analisi termica 1D su un combustore aeronautico anulare a fiamma magra testato al CIAM durante il progetto europeo LEMCOTEC (Low Emissions COre-engine TEChnologies). L'analisi è stata effettuata utilizzando il codice monodimensionale "Therm-1D", sviluppato dal DIEF dell'Università di Firenze. Sono state analizzate tre principali analisi di incertezza in funzione dei parametri di input considerati: fattori geometrici, fattori di regolazione del coefficiente di scambio termico e carichi termici. In particolare, la classica analisi Monte Carlo viene confrontata con quattro processi di espansione stocastica: Quadratura di Gauss, ordine totale con campionamento LHS, collocazione stocastica e Smolyak. Le analisi hanno dimostrato come questi metodi forniscano risultati analoghi con un numero sensibilmente inferiore di simulazioni. Infine, è stata effettuata un'analisi di tutte le variabili di input considerate e i risultati sono stati confrontati con i dati sperimentali. Lavorare su un solutore 1-D ha permesso di ottenere una grande quantità di dati con costi computazionali modesti: questa parte è stata fondamentale per comprendere meglio le diverse metodologie e per avere un quadro più chiaro delle potenzialità del software. La seconda parte del lavoro si è concentrata sull'applicazione dei concetti acquisiti ad un codice tridimensionale. Sulla base di uno studio sperimentale, è stato condotto uno studio completo di fluidodinamica computazionale (CFD) utilizzando il software ANSYS per valutare le prestazioni di raffreddamento del film di una paletta prismatica di turbina a gas prodotta mediante stampa 3D. Sono state effettuate sia simulazioni stazionarie che non stazionarie: la prima con approccio RANS e la seconda con approccio ibrido LES-RANS. Per l'analisi UQ sono state adottate solo simulazioni RANS insieme ad uno specifico metodo di espansione stocastica per risparmiare risorse computazionali. Sono state valutate le influenze delle incertezze geometriche dei fori: la dimensione del foro, l'angolo di inclinazione del flusso e il raggio di raccordo di ingresso del foro. I parametri di uscita considerati sono stati l'efficacia di raffreddamento del film, il blowing ratio e i coefficienti di efflusso dei fori. I risultati mostreranno come un approccio del polynomial chaos, che ha richiesto 8 valutazioni, è in grado di riprodurre accuratamente ciò che un’analisi Monte Carlo fa con oltre 1000 valutazioni e con un grado di accuratezza ottimale. Inoltre, i risultati dimostrano come la tolleranza di posizione dei fori sulla paletta, così come la dimensione del foro, sia estremamente importante per l'efficacia del raffreddamento del film, in particolare quando si tratta di processi di stampa 3D

Civil aero-engine performance prediction using a low-order code and uncertainty quantification estimation

In the last decades, the attention for pollutant emissions in the civil air transport field has grown up continuously. Especially considering the performances of current turbofan engines, even a modest increase in overall efficiency can lead to great benefits in terms of emissions reduction. Therefore, dedicated performance prediction tools are mandatory in order to carry out an estimation of such outputs. The aim of the present study is to develop a procedure devoted to a preliminary output prediction of an aero engine for civil transportation and an uncertainty quantification analysis based on main performance parameters. For the first step, following the strategy already adopted in previous work on this topic [1], the GEnX, a high-bypass turbofan engine, has been considered as the reference cases. The main design characteristics available from the constructor for this engine have been employed to model the engine with a 0-D numerical tool (ESMS), developed by the University of Florence [2]. Great effor..