A previously developed turbine modelling methodology, requiring minimal blade passage information, produced a customizable turbine stage component. This stage-by-stage turbine nozzlemodel component was derived from the synthesis of classical turbine theory and classical nozzle theory enabling the component to accurately model a turbine stage. Utilizing Flownex, a thermohydraulic network solver, the turbine stage component can be expanded to accurately model any arrangement and category of turbine. This project focused on incorporating turbine blade passage geometrical information, as it relates to the turbine specific loss coefficients, into the turbine stage component to allow for the development of turbine models capable of predicting turbine performance for various structural changes, anomalies and operating conditions. The development of turbine loss coefficient algorithms as they relate to specific blade geometry data clusters required the investigation of several turbine loss calculation methodologies. A stage-by-stage turbine nozzle-model incorporating turbine loss coefficient algorithms was developed and validated against real turbine test cases obtained from literature. Several turbine models were developed using the loss coefficient governed turbine stage component illustrating its array of capabilities. The incorporation of the turbine loss coefficient algorithms clearly illustrates the correlation between turbine performance deviations and changes in specific blade geometry data clusters.

The purpose of this study was to serve as a starting point in gaining understanding and experience of simulating a typical Pool Type Research Reactor with the thermal hydraulic software code Flownex®. During the study the following evaluations of Flownex® were done: * Assessment of the simplifying assumptions and possible shortcomings built into the software. * Definition of the applicable modelling methodology and further simplifying assumptions that have to be made by the user. * Evaluation of the accuracy and compatibility with the Pool Type Research Reactor. * Comparing the results of this study with similar studies found in the open literature. For the study the IAEA MTR 10 MW benchmark reactor (IAEA, 1992a) was used. A steady state simulation using Flownex® was done on a single fuel assembly, and this was compared with a model that was developed using the software package EES (Engineering Equation Solver). The results have shown good agreement between the different packages. After this verification, a steady state simulation of the entire core was done to obtain the characteristics of the reactor operating under normal condition. Finally, transient simulations were done on various LOFAs (Loss of Flow Accidents). The results of the various LOFAs were compared with studies that were previously done on the IAEA MTR 10 MW reactor. / Thesis (M.Ing. (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2012.

The purpose of this study was to serve as a starting point in gaining understanding and experience of simulating a typical Pool Type Research Reactor with the thermal hydraulic software code Flownex®. During the study the following evaluations of Flownex® were done: * Assessment of the simplifying assumptions and possible shortcomings built into the software. * Definition of the applicable modelling methodology and further simplifying assumptions that have to be made by the user. * Evaluation of the accuracy and compatibility with the Pool Type Research Reactor. * Comparing the results of this study with similar studies found in the open literature. For the study the IAEA MTR 10 MW benchmark reactor (IAEA, 1992a) was used. A steady state simulation using Flownex® was done on a single fuel assembly, and this was compared with a model that was developed using the software package EES (Engineering Equation Solver). The results have shown good agreement between the different packages. After this verification, a steady state simulation of the entire core was done to obtain the characteristics of the reactor operating under normal condition. Finally, transient simulations were done on various LOFAs (Loss of Flow Accidents). The results of the various LOFAs were compared with studies that were previously done on the IAEA MTR 10 MW reactor. / Thesis (M.Ing. (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2012.

This study was based on the uncertainty and sensitivity analysis of a generic 10 MW Materials Test Reactor (MTR). In this study an uncertainty and sensitivity analysis methodology called code scaling applicability and uncertainty (CSAU) was implemented. Although this methodology follows 14 steps, only the following were carried out: scenario specification, nuclear power plant (NPP) selection, phenomena identification and ranking table (PIRT), selection of frozen code, provision of code documentation, determination of code applicability, determination of code and experiment accuracy, NPP sensitivity analysis calculations, combination of biases and uncertainties, and total uncertainty to calculate specific scenario in a specific NPP. The thermal hydraulic code Flownex®1 was used to model only the reactor core to investigate the effects of the input parameters on the selected output parameters of the hot channel in the core. These output parameters were mass flow rate, temperature of the coolant, outlet pressure, centreline temperature of the fuel and surface temperature of the cladding. The PIRT process was used in conjunction with the sensitivity analysis results in order to select the relevant input parameters that significantly influenced the selected output parameters. The input parameters that have the largest effect on the selected output parameters were found to be the coolant flow channel width between the plates in the hot channel, the width of the fuel plates itself in the hot channel, the heat generation in the fuel plate of the hot channel, the global mass flow rate, the global coolant inlet temperature, the coolant flow channel width between the plates in the cold channel, and the width of the fuel plates in the cold channel. The uncertainty of input parameters was then propagated in Flownex using the Monte Carlo based uncertainty analysis function. From these results, the corresponding probability density function (PDF) of each selected output parameter was constructed. These functions were found to follow a normal distribution. / MIng (Nuclear Engineering), North-West University, Potchefstroom Campus, 2014

This study was based on the uncertainty and sensitivity analysis of a generic 10 MW Materials Test Reactor (MTR). In this study an uncertainty and sensitivity analysis methodology called code scaling applicability and uncertainty (CSAU) was implemented. Although this methodology follows 14 steps, only the following were carried out: scenario specification, nuclear power plant (NPP) selection, phenomena identification and ranking table (PIRT), selection of frozen code, provision of code documentation, determination of code applicability, determination of code and experiment accuracy, NPP sensitivity analysis calculations, combination of biases and uncertainties, and total uncertainty to calculate specific scenario in a specific NPP. The thermal hydraulic code Flownex®1 was used to model only the reactor core to investigate the effects of the input parameters on the selected output parameters of the hot channel in the core. These output parameters were mass flow rate, temperature of the coolant, outlet pressure, centreline temperature of the fuel and surface temperature of the cladding. The PIRT process was used in conjunction with the sensitivity analysis results in order to select the relevant input parameters that significantly influenced the selected output parameters. The input parameters that have the largest effect on the selected output parameters were found to be the coolant flow channel width between the plates in the hot channel, the width of the fuel plates itself in the hot channel, the heat generation in the fuel plate of the hot channel, the global mass flow rate, the global coolant inlet temperature, the coolant flow channel width between the plates in the cold channel, and the width of the fuel plates in the cold channel. The uncertainty of input parameters was then propagated in Flownex using the Monte Carlo based uncertainty analysis function. From these results, the corresponding probability density function (PDF) of each selected output parameter was constructed. These functions were found to follow a normal distribution. / MIng (Nuclear Engineering), North-West University, Potchefstroom Campus, 2014

This research investigates the use of an alternate means of modelling the performance of a train of feed water heaters in a steam cycle power plant, using machine learning. The goal of this study was to use a simple artificial neural network (ANN) to predict the behaviour of the plant system, specifically the inlet bled steam (BS) mass flow rate and the outlet water temperature of each feedwater heater. The output of the model was validated through the use of a thermofluid engineering model built for the same plant. Another goal was to assess the ability of both the thermofluid model and ANN model to predict plant behaviour under out of normal operating circumstances. The thermofluid engineering model was built on FLOWNEX® SE using existing custom components for the various heat exchangers. The model was then tuned to current plant conditions by catering for plant degradation and maintenance effects. The artificial neural network was of a multi-layer perceptron (MLP) type, using the rectified linear unit (ReLU) activation function, mean squared error (MSE) loss function and adaptive moments (Adam) optimiser. It was constructed using Python programming language. The ANN model was trained using the same data as the FLOWNEX® SE model. Multiple architectures were tested resulting in the optimum model having two layers, 200 nodes or neurons in each layer with a batch size of 500, running over 100 epochs. This configuration attained a training accuracy of 0.9975 and validation accuracy of 0.9975. When used on a test set and to predict plant performance, it achieved a MSE of 0.23 and 0.45 respectively. Under normal operating conditions (six cases tested) the ANN model performed better than the FLOWNEX® SE model when compared to actual plant behaviour. Under out of normal conditions (four cases tested), the FLOWNEX SE® model performed better than the ANN. It is evident that the ANN model was unable to capture the “physics” of a heat exchanger or the feed heating process as a result of its poor performance in the out of normal scenarios. Further tuning by way of alternate activation functions and regularisation techniques had little effect on the ANN model performance. The ANN model was able to accurately predict an out of normal case only when it was trained to do so. This was achieved by augmenting the original training data with the inputs and results from the FLOWNEX SE® model for the same case. The conclusion drawn from this study is that this type of simple ANN model is able to predict plant performance so long as it is trained for it. The validity of the prediction is highly dependent on the integrity of the training data. Operating outside the range which the model was trained for will result in inaccurate predictions. It is recommended that out of normal scenarios commonly experienced by the plant be synthesised by engineering modelling tools like FLOWNEX® SE to augment the historic plant data. This provides a wider spectrum of training data enabling more generalised and accurate predictions from the ANN model.