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This paper expands the one-engine-inoperative conventional oversizing consideration to account for aircraft propulsion systems with multiple energy sources and thrust-generating media. Components in a generic hybrid propulsion system are categorized into power-generation, power-transmission, and thrust-generation. For a given architecture, each possible single component failure is simulated to identify elements affected or eliminated by the respective loss of power, through the use of connection matrices. Failures are linked to losses in supplied and propulsive power, creating a list of oversizing factors for all individual components. Each element is oversized according to its corresponding maximum oversizing rate, defining the ideally redundant propulsion system. Case studies for conventional, all-electric, and hybrid-electric powertrains highlight the need for balancing the number of components between minimizing excess power and increasing the probability of a failure. Additionally, it is shown that asymmetrical configurations should not have a major imbalance of power to avoid significant oversizing. The proposed methodology is applied to a 19-passenger, commuter aircraft. Increasing oversizing rate close to ideal leads to lower optimum energy consumption and boosts redundancy. However, payload capacity penalties are required, up to four passengers for ideal oversizing. Heavier variants without penalties are up to 4% more efficient in terms of energy-per-weight in their carrying capacity against counterparts of the same oversize rate with reduced payload capacity. The proposed method maintains the principles of the conventional oversizing process and highlights the tradeoffs needed between redundancy and performance in sizing novel propulsion systems. [ABSTRACT FROM AUTHOR]

The research in thermal energy storage (TES) systems has a long track record. However, there are several technical challenges that need to be overcome, to become omnipresent and reach their full potential. These include performance, physical size, weight and dynamic response. In many cases, it is also necessary to be able to achieve the foregoing at greater and greater scale, in terms of power and energy. One of the applications in which these challenges prevail is in the integration of a thermal energy storage with the gas turbine (GT) compressor inlet conditioning system in a combined cycle power plant. The system is intended to provide either GT cooling or heating, based on the operational strategy of the plant. As a contribution to tackle the preceding, this article describes a series of 3-dimensional (3D) numerical simulations, employing different Computational Fluid Dynamics (CFD) methods, to study the transient effects of inlet temperature and flow rate variation on the performance of an encapsulated TES with phase change materials (PCM). A sensitivity analysis is performed where the heat transfer fluid (HTF) temperature varies from -7°C to 20°C depending on the operating mode of the TES (charging or discharging). The flow rate ranges from 50% to 200% of the nominal inflow rate. Results show that all examined cases lead to instant thermal power above 100kWth. Moreover, increasing the flow rate leads to faster solidification and melting. The increment in each process depends on the driving temperature difference between the encapsulated PCM and the HTF inlet temperature. Lastly, the effect of the inlet temperature has a larger effect as compared to the mass flow rate on the efficiency of the heat transfer of the system.

This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles. [ABSTRACT FROM AUTHOR]