The study summarized in this paper links a model of thermal energy storage (TES) unit performance to a subsystem model including heat exchangers that cool down the storage at night when air temperatures are low; this cool storage is subsequently used to precool the air flow for a power plant air-cooled condenser during peak daytime air temperatures. The subsystem model is also computationally linked to a model of Rankine cycle power plant performance to predict how much additional power the plant could generate as a result of the asynchronous cooling augmentation provided by this subsystem. The goal of this study is to use this model to explore the parametric effects of changing phase change material (PCM), melt temperature, and the energy input and rejection control settings for the system. With this multi-scale modeling, the performance of the TES unit was examined within the context of a larger subsystem to illustrate how a high efficiency, optimized design target can be established for specified operating conditions that correspond to a variety of applications. Operating conditions of interest are the mass flow rate of fluid through the flow passages within the TES, the volume of the TES, and the amount of time the system remains in the extraction process in which thermal energy is inputted to the device by melting PCM, and the PCM melt temperature. These conditions were varied to find combinations that maximized efficiency for a 50 MW power plant operating in the desert regions of Nevada during an average summer day. By adjusting the flow rate within the fluid flow passages and the volume of the TES to achieve complete melting of the PCM during a set extraction time, indications of the parametric effects of system flow, melt temperature, and control parameters were obtained. The results suggest that for a full-sized power plant with a nominal capacity of 50 MW, the kWh output of the plant can be increased by up to 3.25% during the heat input/cold extraction period, depending on parameter choices. Peak power output enhancements were observed to occur when the system operated in the extraction phase during limited hours near the peak temperatures experienced throughout a day, while total kWh enhancement was shown to increase as the extraction period increased. For the most optimized conditions, cost analyses were performed, and it was estimated that the TES system has the potential to provide additional revenue of up to $1,366 per day, depending on parameter choices as well as the local cost of electricity. Results obtained to date are not fully optimized, and the results suggest that with further adjustments in system parameters, weather data input, and control strategies, the predicted enhancement of the power output can be increased above the results in the initial performance predictions reported here.

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This study links a model of thermal energy storage (TES) performance to a subsystem model with heat exchangers that cool down the storage at night; this cool storage is used to precool the air flow for a power plant air-cooled condenser during peak day temperatures. The subsystem model is also computationally linked to a model of Rankine cycle power plant performance to predict additional power the plant could generate due to the additional cooling. The model was used to explore the effects of varying phase change material (PCM) melt temperature and the energy input and rejection control settings with the goal of maximizing efficiency for a 50 MW power plant operating in the desert regions of Nevada for an average summer day. The results suggest that the kWh output of the modeled plant can be increased by up to 3.25% during the heat input/cold extraction period, and a cost analysis estimates that the TES system has the potential to provide additional revenue of up to $686,000 per year, depending on electricity cost and parameter choices.

There is currently a global-scale transition from fossil fuel energy technologies towards increasing use of electrically driven energy technologies, especially transportation and heat, fueled by renewable energy sources, which is making fire safety in electrically powered systems increasingly important. The work presented here provides a coherent understanding of flame spread parametric trends and associated fire safety issues in electrical systems for structural, transportation, and space applications. This understanding was obtained through use of an artificial neural network (ANN) that was trained to predict the flame spread rate along “laboratory” wires of different sizes and compositions (copper, nichrome, iron, and stainless-steel tube cores and HDPE, LDPE, and ETFE insulation sheaths) and exposed to different ambient conditions (varying flows, pressure, oxygen concentration, orientation, and gravitational strength). For these predictions, a comprehensive data base of 1200 data points was created by incorporating flame spread rate results from both in-house experiments (400 data points) as well external experiments from other sources (800 data points). The predictions from the ANN showed that it is possible to merge together various data sets, including results from horizontal, inclined, vertical, and microgravity experiments, and obtain unified predictive results. While these initial results are very encouraging with an overall average error rate of 14%, they also show that future improvements to the ANN could still be made to increase prediction accuracy.