Mauro Venturini

Development of a One-Dimensional Modular Dynamic Model for the Simulation of Surge in Compression Systems

The paper deals with the development of a nonlinear one-dimensional modular dynamic model for the simulation of transient behavior of compression systems. The model is based on balance equations of mass, momentum, and energy, which are derived through a general approach and are written by using the finite difference method. The model also takes rotating mass dynamics into account through a lumped parameter approach. Moreover, it reproduces the behavior of the system in the presence of the surge phenomenon through steady-state performance maps, which represent the compressor operation in the inverse flow region by means of a third degree polynomial curve. The model is implemented through the Matlab Simulink tool, where the system of ordinary differential equations is solved by using a fourth- and fifth-order Runge-Kutta method. A sensitivity analysis is carried out to evaluate the influence on compressor outlet pressure oscillations of the model parameters, of the supplied torque, of ambient conditions and of the shape of the compressor characteristic curves. The results show that the model proves effective in capturing the physical essence of surge phenomenon without being computationally too heavy.

Artificial intelligence for the diagnostics of gas turbines-Part I: Neural network approach

In the paper, neural network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy, and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, multi-input/ multioutput NN architectures (i.e., NNs calculating all the system outputs) are compared to multi-input/single-output NNs, each of them calculating a single output of the system. The results obtained show that NNs are sufficiently robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, multi-input/ multioutput NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics.

Computational Fluid Dynamics Simulation of Fouling on Axial Compressor Stages

Three-dimensional numerical simulations of the effect of fouling on an axial compressor stage were carried out. As a case study, the NASA Stage 37 was considered for the numerical investigation, which was performed by means of a commercial computational fluid dynamic code. The numerical model was validated against the experimental data available from literature. Computed performance maps and main flow field features showed a good agreement with the experimental data. The model was considered representative of a realistic compressor stage. The model was then used to simulate the occurrence of fouling by imposing different combinations of added thickness and surface roughness levels. The effect of fouling on compressor performances was studied. Reductions in the flow coefficient and in the pressure coefficient were found to be of the same order of magnitude of the experimental results found in literature. The model developed seems to overcome some of the limitations of other models found in literature that tend to significantly underestimate the actual values of performance reduction. The numerical results were also used to analyze and debug the stage performance scaling procedure used in stage-stacking models in order to represent fouling in multistage compressors. The analysis highlighted that scaling can adequately represent the behavior of the fouled stage in the choked flow region, but it does not capture the reduction in the maximum of the pressure coefficient, which is instead revealed by the numerical simulations. Finally, blockage due to fouling was investigated both qualitatively and quantitatively.

Influence of Blade Deterioration on Compressor and Turbine Performance

Gas turbine operating state determination consists of the assessment of the modification due to deterioration and fault of performance and geometric data characterizing machine components. One of the main effects of deterioration and fault is the modification of compressor and turbine performance maps. Since detailed information about actual modification of component maps is usually unavailable, many authors simulate the effects of deterioration and fault by a simple scaling of the map itself. In this paper, stage-by-stage models of the compressor and the turbine are used in order to assess the actual modification of compressor and turbine performance maps due to blade deterioration. The compressor is modeled by using generalized performance curves of each stage matched by means of a stage-stacking procedure. Each turbine stage is instead modeled as two nozzles, a fixed one (stator) and a moving one (rotor). The results obtained by simulating some of the most common causes of blade deterioration (i.e., compressor fouling, compressor mechanical damage, turbine fouling, and turbine erosion), occurring in one or more stages simultaneously, are reported in this paper. Moreover, compressor and turbine maps obtained through the stage-by-stage procedure are compared with the ones obtained by means of map scaling. The results show that the values of the scaling factors depend on the corrected rotational speed and on the load. However, since the variation in the scaling factors in the operating region close to the design corrected rotational speed is small, the use of the scaling factor as health indices can be considered acceptable for gas turbine health state determination at full load. Moreover, also the use of scaled maps in order to represent compressor and turbine behavior in deteriorated conditions close to the design corrected rotational speed can be considered acceptable.

Artificial Intelligence for the Diagnostics of Gas Turbines—Part II: Neuro-Fuzzy Approach

In the paper neuro-fuzzy systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the setup of neural network (NN) models (Bettocchi, R., Pinelli, M., Spina, P. R., and Venturini, M., 2007, ASME J. Eng. Gas Turbines Power, 129(3), pp. 711-719) was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a cycle program, calibrated on a 255 MW single-shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy, and robustness towards measurement uncertainty during simulations. In particular, adaptive neuro-fuzzy inference system (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by multi-input/multioutput (MIMO) and multi-input/single-output (MISO) neural networks trained and tested on the same data.

Design, Analysis and Optimization of a Micro-CHP System Based on Organic Rankine Cycle for Ultralow Grade Thermal Energy Recovery

This paper presents the results of the application of an advanced thermodynamic model developed by the authors for the simulation of Organic Rankine Cycles (ORCs). The model allows ORC simulation both for steady and transient analysis. The expander, selected to be a scroll expander, is modeled in detail by decomposing the behavior of the fluid stream into several steps. The energy source is coupled with the system through a plate heat exchanger (PHE), which is modeled using an iterative sub-heat exchanger modeling approach. The considered ORC system uses solar thermal energy for ultralow grade thermal energy recovery. The simulation model is used to investigate the influence of ORC characteristic parameters related to the working medium, hot reservoir and component efficiencies for the purpose of optimizing the ORC system efficiency and power output. Moreover, dynamic response of the ORC is also evaluated for two scenarios, i.e. (i) supplying electricity for a typical residential user and (ii) being driven by a hot reservoir. Finally, the simulation model is used to evaluate ORC capability to meet electric, thermal and cooling loads of a single residential building, for typical temperatures of the hot water exiting from a solar collector.

Set Up of an Experimental Facility for the Investigation of Wet Compression on a Multistage Compressor

At present, inlet fogging and wet compression are two of the most widely used approaches to enhance gas turbine performance, especially during hot seasons. However, potentially negative effects of these practices on long-term operational integrity of gas turbines should be evaluated carefully; in particular, wet compression may lead to the erosion of first compressor stages, due to the impact of water droplets within the flow at compressor intake. This issue is still controversial in technical literature, since only limited historical field operating data and information are available. Therefore, a test facility was specifically set up in the laboratories of the University of Ferrara, to evaluate the effects of wet compression on a small-size compressor. This paper presents the experimental facility developed for wet compression investigation and some preliminary results.Copyright

Experimental Implementation of a Micro-Scale ORC-Based CHP Energy System for Domestic Applications

Because of the renewed interest in renewable energy as well as increased emphasis on alternative technologies, micropower-generating systems have attracted considerable research interest over the last decade. However, micro-scale power generation for low grade heat recovery applications, i.e. as low as 1–3 kW - for domestic use, are characterized by very low efficiencies and relatively high specific cost. For economic viability, these factors make it imperative that the heat source remains “free”, such as solar or geothermal energy. In this paper, a small-scale Organic Rankine Cycle (ORC) is presented. The small-scale ORC module was built and tested at San Diego State University lab, aimed at producing electricity and hot water from ultra-low grade heat source that can be tapped from solar collectors and low temperature exhaust heat. The system was built for economic viability and flexibility, tailored for a domestic use. The tests demonstrated that the system offered CHP capability, with electric and thermal power output suitable for a domestic application. It also offered high operational flexibility, since the scroll expander could work with a high temperature range, accommodating an even-significant drop of the heat source temperature. Therefore, it can be conveniently used to capture solar and low-temperature energy sources. The system could be produced at an overall cost of less than

Optimized Operating Point Selection for Gas Turbine Health State Analysis by Using a Multi-Point Technique

3,000 (USD 2010).Copyright

Set Up of a Robust Neural Network for Gas Turbine Simulation

Gas turbine operating state determination can be performed using Gas Path Analysis (GPA) techniques, which use measurements taken on the machine to calculate the characteristic parameters that are indices of the machine health state. The number and type of characteristic parameters that can be evaluated depend on the number and type of the available measured variables. Thus, when there are not enough measured variables to determine all the characteristic parameters, some of them have to be estimated independently of the actual gas turbine health state. In this way, variations due to aging or deterioration which, in the actual machine, may occur on these last characteristic parameters, cause estimation errors on the characteristic parameters assumed as problem unknowns. The available instrumentation in field applications is often inadequate to ensure reliable operating state analysis when GPA-based techniques are used. This problem may be partially overcome using a multiple operating point minimization technique. This consists of the determination of the characteristic parameters that minimize the sum of the square differences between measured and computed values of the measurable variables in multiple operating points. In this way the lack of data is overcome by data obtained in different operating points. This paper describes a procedure for gas turbine operating state determination based on a multiple operating point minimization technique and presents a study aimed at selecting the best set and number of operating points that should be used.Copyright