Abstract Strong hydrodynamic instabilities are often observed in rotating cavities of turbomachinery in the aerospace industry for specific thermal and operating conditions. This highly unsteady motion of the flow, often referred to as ‘pressure bands’ phenomenon can be detrimental and jeopardize the structural integrity of aeronautical engines by damaging turbomachinery components or turbopumps in the context of space applications. In order to avoid this fluid structure interaction, a deeper understanding of the stability of the flow of rotating cavities becomes compulsory. A few studies enabled to point out the spatial distribution and origins of such multi-frequency flow. However control solutions have not yet been set up. In this context, an academic stator/rotor cavity is studied in this paper with a sensitivity analysis to base flow modification based on linear stability analysis. This latter points out the regions of the stator boundary layer of each mode where the flow should be modified. Possible passive control strategies are then proposed from the interpretation of the results. The more relevant solution being suction and injection actuators directly introduced on the stationary disk of the cavity.

Abstract A preliminary 2D numerical investigation of the active control of unsteady cavitation by means of one single synthetic jet actuator (SJA) is presented. The SJA has been applied to hinder the intrinsic instabilities of a cloud cavitating flow of water around a NACA 0015 hydrofoil with an angle of attack of 8° and ambient conditions. It has been placed inside the inception region at a distance of 16% of the chord from the leading edge. Concerning the numerical approach, a Eulerian homogeneous mixture/mass transfer model has been used, in combination with an extended Schnerr-Sauer cavitation model and a Volume of Fluid (VOF) interface tracking method. The synthetic jet has been modeled by means of a user-defined velocity boundary conditions based on a sinusoidal waveform. A sensitivity analysis has been first performed in order to evaluate the influence of the main control parameters, namely the momentum coefficient C μ , the dimensionless frequency F + and the jet angle α jet . By combining the cavitating vapor content and the impact on the hydrodynamic performance, the best performing SJA configuration has been retrieved. Then, a deeper analysis of the vapor cavity dynamics and the vorticity field has been conducted in order to understand the modification of the main flow produced by the synthetic jet. The best SJA configuration was observed at C μ = 0.0002, F + = 0.309 and α jet = 90°, which led to a reduction of both the average vapor content and the average torsional load in the measure of 34.6% and 17.8% respectively. A reduction of the average pulsation frequency of the pressure upstream confirmed the beneficial effect of the SJA. The analysis of the coupled dynamics between vapor cavity-vorticity and their POD-based modal structures highlighted that the benefit of the SJA lies on preventing the growth of a thick sheet cavity which tends to cause the development of the highly cavitating cloud dynamics after the cavity breakup. This is mainly due to an additional vorticity close to the hydrofoil surface just downstream the SJA, as well as a local pressure modification close the SJA during the blowing stroke.

Abstract The expansion of renewable energy generation must go hand in hand with measures for reliable energy supply and energy storage. A combination of hydrogen and oxygen as storing media provided from electrolysis at high pressure and zero emission power plants is a very promising option. The Graz cycle is an oxy-fuel combined power cycle that can operate with internal H 2 /O 2 combustion and steam as working fluid. It offers thermal efficiencies up to 68.5% (LHV). This work applies a second law analysis to the Graz cycle and determines its exergetic efficiency. Exergy destruction is broken down to the cycle’s components thus providing insights on the location and magnitude of the cycle’s inefficiencies. A sensitivity analysis identifies the cycle’s exergetic and energetic efficiency as a function of representative parameters, offering an approach for future improvements. The combination of the cycle with an electrolysis plant is subsequently analyzed as an electric energy storage system. The round trip efficiency of the storage and back conversion system is computed by taking into account the additional compression of the reactants. As part of this analysis, the effect of the electrolyzer’s operational pressure is studied by comparing several commercial electrolyzers.

Abstract Modern high pressure turbine (HPT) blade design stands out due to high complexity comprising three-dimensional blade features, multi-passage cooling system (MPCS) and film cooling to allow for progressive thermodynamic process parameters. During the last decade, probabilistic design approaches have become increasingly important in turbomachinery to incorporate uncertainties such as geometric variations caused by manufacturing scatter. In part B of this two-part paper, real geometry effects are considered within a probabilistic finite element (FE) analysis that aims at sensitivity evaluation. The knowledge about the geometric variability is derived based on a blade population of more than 400 individuals by means of parametric models that are introduced in part A (cf. Högner et al. [1]). The HPT blade population is statistically assessed which allows for reliable sensitivity analysis and robustness evaluation taking the variability of the airfoil, profiled endwalls (PEW) at hub and shroud, wedge surfaces (WSF) and the MPCS into account. The probabilistic method — Monte-Carlo simulation (MCS) using an extended Latin Hypercube Sampling (eLHS) technique — is presented subsequently. Afterwards, the FE model that involves thermal, linear-elastic stress and creep analysis is described briefly. Based on this, the fully automated process chain involving CAD model creation, FE mesh morphing, FE analysis and post-processing is executed. Here, the mesh morphing process is presented involving a discussion of the mesh quality. The process robustness is assessed and quantified referring to the impact on input parameter correlation. Finally, the result quantities of the probabilistic FE simulation are evaluated in terms of sensitivities. For this purpose, regions of interest are determined, wherein the statistical analysis is conducted to achieve the sensitivity ranking. A significant influence of the considered geometric uncertainties onto mechanical output quantities is observed which motivates to incorporate these in modern design strategies or robust optimization.

Abstract Compressor performance tests are mainly focused on the typical range of operation, resulting in limited knowledge of compressor behavior in the low-speed region. The main target of this work comprises the generation of compressor characteristics at low part-load by giving particular insight into the physical aspect of this operating condition. It is necessary for running transient and part-load performance simulation and can be considered as the first crucial step toward an optimal engine starting schedule. Modelling the low part-load operating regime requires accurate component performance maps extended to the low-speed area, where engine starting and altitude relight occur. In this work, a robust methodology for generating compressor maps in the low part-load operating regime is developed. Compressor geometry and typical operation range compressor map are required as inputs. Two different modelling processes are incorporated within this methodology. Extrapolation based on the principle of similarity laws with modified law exponents constitutes the first modelling process, which seems inaccurate when predicting compressor performance at fixed-rotor conditions. Interpolation based on the fixed-rotor characteristic constitutes the second modelling process, which can be either linear or adaptive. The adaptive interpolation scheme was developed by the authors and generates low-speed characteristics using the same allocation trend as the one obtained from given performance data. It is observed that performance data points of each β-line follow an exponential trend in mass flow differences while increasing rotational speed, with a calculated average relativized Root Mean Square (RMS) error of less than 5%. Adapting the same trend in mass flow to the low-speed region, a compressor performance map with continuous exponential trend in all characteristics (for part- and full-load conditions) can be achieved. Implementing the developed methodology on the High Pressure Compressor (HPC) of the Energy Efficient Engine (E 3 ) project is also presented, showcasing its applicability and the merit of it being incorporated into any conventional performance prediction tool. Furthermore, a sensitivity analysis for input variables, namely compressor exit effective area and pressure loss model coefficients is carried out, demonstrating the significant impact of the former on the shape of the low part-load characteristics. Generation of compressor characteristics at low-speeds with this methodology can be viewed as an enabler for running credible transient starting simulation and transient diagnostics, thereby defining an optimal starting schedule, applicable to both power generation and aerospace industry.

Abstract With increased interest in reducing emissions, the staged combustion concept for gas turbine combustors is gaining in popularity. For this work, the effect of CO 2 dilution on laminar burning velocities of premixed methane/air flames was investigated at elevated temperature through both experiments and numerical simulations. Validation of the experimental setup and methodology was completed through experimental testing of methane/air mixtures at 1 bar and 298 K. Following validation, high temperature experiments were conducted in an optically accessible constant volume combustion chamber at 1 bar and 473 K. Laminar burning velocities of premixed methane/air flames with 0%, 5%, 10% and 15% CO 2 dilution were determined using the constant pressure method enabled via schlieren visualization of the spherically propagating flame front. Results show that laminar burning velocities of methane/air mixtures at 1 bar increase by 106–145% with initial temperature increases from 298 K to 473 K. Additions of 5%, 10% and 15% CO 2 dilution at 1 bar and 473 K cause a 30–35%, 51–54% and 66–68% decrease in the laminar burning velocity, respectively. Numerical results were obtained with CHEMKIN [1] using the GRI-Mech 3.0 [2] and the San Diego [3] mechanisms. Excellent agreement was observed between the GRI-Mech 3.0 [2] and experimental data at ϕ ≤ 1.2 for methane/air mixtures at 1 bar and 298 K. However, for mixtures at ϕ < 1.3, 1 bar and 473 K, mixtures at ϕ < 1.2 for 5% and 10% dilutions, and for mixtures at ϕ < 0.9 for 15% dilution, laminar burning velocities predicted by the GRI-Mech 3.0 mechanism [2] were slightly higher than experimental results. The San Diego mechanism [3] showed good agreement with experimental data at ϕ ≤ 0.9 for methane/air mixtures at 1 bar and 298 K. However, for mixtures at ϕ > 0.9, 1 bar and 298 K, mixtures at ϕ > 1.2, 1 bar and 473 K, and mixtures at ϕ > 1.1 with 5%, 10% and 15% dilution, the San Diego mechanism [3] predicted slower laminar flame speeds than the experimental results. On the other hand, the laminar burning velocities predicted by the San Diego mechanism [3] were slightly faster than the experimental results for leaner mixtures. Additionally, the dilution, thermal-diffusion, and chemical effects of CO 2 on the laminar burning velocities of methane/air mixtures were investigated numerically by diluting the mixtures with both chemically active and inactive CO 2 following the determination of the most important elementary reactions on the burning rate through sensitivity analysis. Lastly, it was shown that CO 2 dilution suppresses the flame instabilities during combustion, which is attributable to the increase in the burned gas Markstein length (L b ) with the addition of diluent.

Abstract The effects of premixedness degrees on combustion instabilities of separated stratified swirling flames have been investigated experimentally in the Beihang Axial Swirler Independently-Stratified (BASIS) burner. The degree of premixedness is modulated by the fuel split between two injection positions in the outer stream. In the spectra of pressure oscillations, both the dominant frequency and amplitude of partially premixed flames are positively correlated with fuel split ratios. The partially premixed flame is found to feature a large-scale periodic convective motion based on CH* chemiluminescence images, which have been analyzed under different fuel split ratios by a point-to-point Fast Fourier Transform (FFT) method. The development of above convective motion is explained by combining the variation of pressure and heat release in the oscillation period. Local Rayleigh index maps show that the driving factor of combustion instability for the partially premixed flame mainly comes from the upstream of the combustor. Finally, thermoacoustic network analysis is applied to predict observed frequencies under both perfectly and partially premixed conditions. The supposed additional convective time due to equivalence ratio fluctuations and the elongated flame region for the partially premixed flame is validated by its longer time delay in the sensitivity analysis of the n-τ flame model.

Abstract High-pressure turbines of modern gas-turbine engines use single crystal blades that exhibit material anisotropy. Due to manufacturing tolerances, each blade in the bladed disk will have different crystal anisotropy axis orientation, thereby creating mistuning in the structure. In this paper, the blade anisotropy angles are considered as uncertain design parameters to study the variation in forced response of a mistuned bladed disk. For realistic, high fidelity model of a bladed disk, linear bonded contact conditions at blade roots and shrouds are considered. The following two kinds of analysis are performed: (a) statistical analysis using polynomial chaos expansion and (b) global sensitivity analysis using Sobol indices. An effective strategy based on gradient information is used to minimize the computational cost involved in statistical and sensitivity analysis. For the first time, the possibility of introducing intentional blade material anisotropy mistuning to reduce the amplification of forced response is investigated.

Abstract Sensor fault detection is a crucial aspect for raw data cleaning in gas turbine industry. To this purpose, a comprehensive approach for Improved Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named I-DCIDS) was developed by the authors to detect and classify several classes of fault. For single-sensors or redundant/correlated sensors, the I-DCIDS methodology can identify seven classes of fault, i.e. Out of Range, Stuck Signal, Dithering, Standard Deviation, Trend Coherence, Spike and Bias. Since the considered faults are detected by means of a methodology which relies on basic mathematical laws and user-defined parameters, sensitivity analyses are carried out in this paper on I-DCIDS parameters to derive some rules of thumbs about their optimal setting. The sensitivity analyses are carried out on four heterogeneous and challenging datasets with redundant sensors installed on Siemens gas turbines.

Abstract The great amount of support schemes that initially fueled the fast, and often uncontrollable, Renewable Energy Sources (RESs) growth have been strongly reduced or revoked in many countries. Currently, the general trend is to try to equate the RESs to the traditional power plants. From the energy market point of view, this entails exposing the RESs more to the market competition and mechanics. This could be done, for example, requiring the stochastic RESs to submit a production schedule in advance and to be financially responsible for any deviation from this. This could push the Wind Farm (WF) operators to make accurate forecasts, fostering the electric system resiliency and an efficient use of balancing resources. From the forecasting point of view this is not a trivial problem, since the schedule submission is often due 10–12 hours before the actual delivery. Since forecast errors are unavoidable, the submitted schedule could turn out to be infeasible, forcing the WF to recur to correcting actions which are generally costly. Focusing on this, the analysis estimates the revenue reduction which would affect a WF operating in the energy market due to forecast errors. To do this in a realistic way, a case study is selected, and realistic forecast scenarios are generated using a copula approach. Important forecast error features like autocorrelation and dependency on forecasted power level and forecast lead-time are modeled. The revenue reduction due to balancing actions is calculated on an annual basis, using typical days, derived through the production data clustering. Losses ranging from 5% to 35% has been found, depending on the days and on the market prices. A sensitivity analysis to the costs of balancing actions is performed. In this way, the effect of different market architectures and, possibly, of different RESs penetration level is considered in the analysis. Finally, the effectiveness of the curtailment as a technique to reduce the impact of forecast errors in highly penalizing market environments is assessed.

Renewable energies are increasingly contributing to the overall volume of the electricity grid and demand besides high efficiency, greater flexibility of the conventional fossil power plants. To optimize these objectives, extensive CFD calculations are required in most cases. For example, transient CFD calculations are only rarely combined with an optimizer because of their high demand on computational resources and time. Surrogate models, which are mathematical methods to learn and approximate the relationship between input and output parameters, are a common way to solve these problems. Once they are trained, they can perform the evaluations within seconds and replace the expensive simulation. Of course, real calculations are still needed to generate the training data. Therefore, it is useful to apply efficient and sequentially extensible design plans. This paper presents a new surrogate model method, based on a deep neural network learning the non-stationary hyperparameters of combined Gaussian process covariance matrices. It is used to approximate the complex and time consuming transient CFD simulation of a combined high-intermediate pressure steam turbine double shell outer casing. To minimize the exergy loss, the exhaust geometry is optimized in a single and multi-objective optimization on the surrogate models. The multi-objective optimization also includes the uniform velocity distribution of the steam in different areas of the casing, to predict the thermal loading of the steam turbine inner casing and to avoid an imbalanced thermal loading. A sequential sampling approach combined with a sensitivity analysis is used to find the minimum number of samples needed to train the surrogate models in order to gain sufficient prediction quality. Additionally, the paper describes the initial geometry, its numerical setup and the required control mechanisms to avoid noisy designs, which might complicate the surrogate model training. There is also a comparison of the initial and chosen optimal designs.

The transient/dynamic modeling of energy systems can be used for different purposes. Important information can be obtained and used for the design phase. The control system and strategies can be safely tested on transient/dynamic models in simulation, increasing the confidence during experimental phase. Furthermore, these models can be used to acquire useful information for safety case. The analysis of energy systems in dynamic conditions is generally performed considering fixed values for both geometrical and operational parameters such as volumes, orifices, but also initial temperatures, pressure, etc. However, such characteristics are often subject to uncertainty, either because they are not known accurately or because they may depend on the operating conditions at the beginning of the relevant transient. With focus on a micro-gas turbine fuel cell hybrid system, compressor surge may or may not occur during transients, depending on the aforementioned cycle characteristics; hence compressor surge events are affected by uncertainty. In this paper, a stochastic analysis was performed taking into account an emergency shut-down in a fuel cell gas turbine hybrid system, modelled with TRANSEO, a deterministic tool for the transient and dynamic simulations of energy systems. The aim of the paper is to identify the main parameters that impact on compressor surge margin. The stochastic analysis was performed through the RSA (Response Sensitivity Analysis) method, a sensitivity-based approximation approach that overcomes the computational burden of sampling methods such as MCS (Monte-Carlo Simulation). The results show that the minimum surge margin occurs in two different ranges of rotational speed: a high-speed range and a low-speed range, the latter being more sensitive for surge occurrence. The temperature and geometrical characteristic of the outer pressure vessel, where the fuel cell is installed, are the two main parameters that affect the surge margin during an emergency shut down.

Residential buildings account for approximately one fifth of the total energy consumption and 12 % of the overall CO 2 emissions in the OECD countries. Replacing conventional boilers by a co-generation of heat and power in decentralized plants on site promises a great benefit. Especially, micro gas turbine (MGT) based combined heat and power systems are particularly suitable due to their low pollutant emissions without exhaust gas treatment. Hence, the overall aim of this work is the development of a recuperated inverted MGT as heat and power supply for a single family house with 1 kW el . First, an inverted MGT on a Brayton cycle MGT was developed and experimentally characterized, in previous work by the authors. This approach allows exploiting the potential of using the same components for both cycles. As a next step, the applicability of the Brayton cycle components operated in inverted mode needs to be evaluated and the requirements for a component optimization need to be defined, both, by pursuing thermodynamic cycle simulations. This paper presents a parametrization and validation of in-house 1D steady state simulation tool for an inverted MGT, based on experimental data from the inverted Brayton cycle test rig. Moreover, a sensitivity analysis is conducted to estimate the influence of every major component on the overall system and to identify the necessary optimizations. Finally, the component requirements for an optimized inverted MGT with 1 kW el and 16 % of electrical efficiency are defined. This work demonstrates the high potential of an inverted MGT for a decentralized heat and power generation when optimizing the system components.

The turbine inlet temperature has been increasing over the years to improve gas turbine efficiency and specific power. Blade cooling technology is essential to keep component temperatures below their critical value, and this makes the aero-thermal coupled phenomenon more significant. Blade life assessment is closely related to blade metal temperature distribution and gradients, and blade cooling analysis is always considered starting from the preliminary design stage. However, traditional blade cooling analysis for preliminary design is always based on external boundary conditions determined by experience, which affects the prediction accuracy as the interaction effect between the main flow and coolant is not considered. In this paper, an aero-thermal coupled blade cooling model is further developed by combining the improved streamline curvature method with a one-dimensional thermo-fluid network. This model is capable of predicting blade surface temperature distribution and internal coolant flow conditions in the preliminary phase of blade cooling design with a limited amount of input information. Experimental data for the NASA C3X profile with film cooling was selected for validation. In addition, a sensitivity analysis was performed on different film cooling mass flow rates to demonstrate the model flexibility for different boundary conditions.

The increasing demands on jet engines require progressive thermodynamic process parameters, which typically lead to higher aerothermal loadings and accordingly to designs with high complexity. State of the art high pressure turbine (HPT) nozzle guide vane (NGV) design involves vane profiles with three-dimensional features including a high amount of film cooling and profiled endwalls (PEW). Typically, the specific massflow, also called capacity, which governs the engine’s operation, is set by the HPT NGV. Hence, geometric variations due to manufacturing scatter of the HPT NGV’s passage can affect relevant aerodynamic quantities and the entire engine behavior. Within the traditional deterministic design approach, the influences of those geometric variations are covered by conservative assumptions and engineering experience. This paper addresses the consideration of variability due to manufacturing of HPT NGVs through probabilistic CFD investigations. In order to establish a statistical database, 80 HPT NGVs are digitized with a high precision optical 3D scanning system to record the outer geometry. The vane profiles are parametrized by a section based approach. For this purpose, traditional profile theory is combined with a novel method that enables the description of NGV profile variability taking the particular leading edge (LE) shape into account. Furthermore, the geometric variability of PEWs is incorporated by means of principle component analysis (PCA). On this basis, a probabilistic system assessment including a sensitivity analysis in terms of capacity and total pressure loss coefficient is realized. Sampling-based methods are applied in order to conduct a variety of 3D CFD simulations for a typical population of profile and endwall geometries. This probabilistic investigation using realistic input parameter distributions and correlations contributes to a robust NGV design in terms of relevant aerodynamic quantities.

Manufacturing uncertainties always lead to significant variability in compressor performance. In this work, the tip clearance uncertainties inherent in a transonic axial compressor are quantified to determine their effect on performance. The validated tip clearance losses model in conjunction with the 3D reynolds averaged navier-stokes (RANS) solver are utilized to simulate these uncertainties and quantify their effect on the adiabatic efficiency, total pressure ratio and choked mass flow. The sensitivity analysis method is employed to figure out which parameters play the most significant roles in determining the overall performance of compressor. To propagate these uncertainty factors, the non-intrusive polynomial chaos expansion (PCE) algorithm is used in this paper and the probability distributions of compressor performance are successfully predicted. A robust design optimization has been carried out based on the combination of the genetic algorithm (GA) and the uncertainty quantification (UQ) method, leading to a robust compressor rotor design for which the overall performance is relatively insensitive to variability in tip clearance without reducing the sources of the manufacturing noise. The optimization results show that the mean value of the adiabatic rotor efficiency is improved by 1.4 points with the overall variation of that reduced by 64.1%, while the total pressure ratio is slightly improved when compared to the prototype.

The performance of small scale compressors mass-produced for domestic appliances is influenced by geometric manufacturing uncertainty which affects overall product performance and reliability. Precise manufacturing processes through tighter tolerances ensure high geometric accuracy and lower compressor performance spread, but it challenges high volume production capabilities and cost. Good understanding of geometric sensitivities is necessary for robust design and tolerance definition. This paper presents the application of geometric Sensitivity Analysis (SA), Uncertainty Quantification (UQ) and robust optimization method to a small scale compressor. Geometric SA was carried out on parametric blade geometry and the key influential parameters were identified. The tip clearance was found to be the most influential parameter followed by a blade surface thickness. The tip section of the blade was more influential than hub section and the suction surface was found to be more important than the pressure surface. Findings from the SA were used to define parameters that were measured and controlled to ensure impeller quality during production. Within the parametric bounds studied, impeller chord did not feature as a critical parameter. The machining tool path was optimized accordingly and a 12% reduction in cutting time was achieved. The numerical sensitivities were also compared with experimental data and a trend-level agreement was seen. Meta-models for aerodynamic performance were built using DoE generated geometries which were used to perform manufacturing UQ using a Monte Carlo estimator. Robust design optimization was carried out using stochastic optimization algorithm coupled with the meta-model based Monte-Carlo simulator. This framework was used to choose a robust nominal shape that achieved 18% lower standard deviation in stage pressure rise. The predicted performance spread was compared with production data and a satisfactory agreement was seen.

Additive Manufacturing (AM) applications are poised to rapidly expand in aero-engines, providing opportunities to manufacture complex geometries which are unobtainable through traditional manufacturing techniques. The powder based AM process, selective laser melting (SLM), results in susceptibility to defects such as porosity, lack of fusion, and unmelted powder. These random defects often serve as fatigue crack initiation sites, thus influencing fatigue crack initiation and growth behavior. Consequently, the influence of random defects on high cycle fatigue performance for the AM titanium compressor disk is investigated in this study. First, the relationships between the defect area and the fatigue performances involving fatigue limit and the crack propagation threshold are quantified. Second, the effect of defect probability distribution is examined. Finally, the durability analyses on SLM titanium compressor disks in Ti-6A1-4V alloy are performed based on the fatigue life prediction model for defect-containing AM material. Then sensitivity analysis is conducted to determine the critical defect size influencing the fatigue limit and fatigue life.

The paper presents an application of statistical calibration techniques to a bracket design fatigue model simulated in COMSOL Multiphysics ® . The calibration will tune the bracket’s material properties and fatigue characteristics. For illustrative purposes, the test data used to calibrate the simulation model will be generated from the same simulation routine with the addition of an intentionally applied bias and random noise to simulate model form and physical testing errors. The accuracy and conclusions from the statistically calibrated model will be compared with the uncalibrated model as well as a model calibrated with conventional error minimization methods. Multiple metrics will be shown which can be used for model validation, including a discrepancy map which characterizes inadequacies in the simulation. The metrics used in the comparison will also include results from optimization, sensitivity analysis, and propagation of uncertainties motivated by manufacturing variations during bracket fabrication. The results will demonstrate the importance of calibrating a model before drawing design conclusions.

The present work presents a very detailed techno-economic analysis of the technology made up of two complementary models. A performance model implemented in Thermoflex environment is used to explore alternative integration layouts to enable the simultaneous operation on solar and fossil energy. Then, a detailed cost analysis approaches the capital and operation costs of the plant from an EPC (Engineering, Procurement and Construction) standpoint. These two models are then combined in annual simulations to obtain the final Levelized Cost of Electricity from which a solid conclusion about the true potential of solar gas turbines can be ascertained. A sensitivity analysis with respect to the main boundary conditions is also provided.