Search Results (8)

Hybrid aircraft offer a logical pathway to reducing aviation’s carbon footprint. The thermal management system (TMS) is often neglected in the assessment of hybrid aircraft performance despite it being of major importance. After presenting the TMS architecture, this study performs a sensitivity analysis on several parameters of a retrofitted hybrid fuel cell aircraft’s performance considering three hierarchical levels: the aircraft, fuel cell system, and TMS component levels. The objective is to minimize CO₂ emissions while maintaining performance standards. At the aircraft level, cruise speed, fuel cell power, and ISA temperature were varied to assess their impact. Lowering cruise speeds can decrease emissions by up to $49\%$, and increasing fuel cell power from 200 kW to 400 kW cuts emissions by $18\%$. Higher ambient air temperatures also significantly impact cooling demands. As for the fuel cell, lowering the stack temperature from 80 °C to 60 °C increases the required cooling air mass flow by $49\%$ and TMS drag by $40\%$. At the TMS component level, different coolants and HEX offset-fin geometries reveal low-to-moderate effects on emissions and payload. Overall, despite some design choice improvements, the conventional aircraft is still able to achieve lower CO₂ emissions per unit payload. Full article

Proton exchange membrane fuel cell system (PEMFCS)-based battery-hybridized turboprop regional aircraft emerge as a promising solution to the urgency of reducing the environmental impact of such airplanes. The development of integrated simulation frameworks consisting of versatile and easily adaptable models and control strategies is deemed highly strategic to guarantee proper component sizing and in-flight, onboard energy management. This need is here addressed via a novel efficiency-driven PEMFCS model and a degradation-aware battery-PEMFCS unit specification-independent control algorithm. The proposed model simplifies stack voltage and current estimation while maintaining accuracy so as to support, in conjunction with the afore-introduced versatile control strategy, the development of architectures appropriate for subsequent fully integrated (i.e., at the entire aircraft design level) simulation frameworks. The model also allows assessing the balance of plant impact on the fuel cell system’s net power, as well as the heat generated by the stack and related cooling demand. Finally, the multi-stack configuration meeting the DC bus line 270 V constraint, as currently assumed by the aviation industry, is determined. Full article

This study focuses on the great challenges for combined cooling and power supply on hypersonic aircrafts. To address the issues of low thermal efficiency and high fuel consumption of heat sink by the existing CO₂ supercritical Brayton cycle, a novel fuel-based CO₂ transcritical cooling and power (FCTCP) system is constructed. A steady-state simulation model is built to investigate the impacts of combustion chamber wall temperatures and fuel mass flow rates on the FCTCP system. Thermal efficiency of the CO₂ transcritical cycle reaches 25.2~32.8% under various combustion chamber wall outlet temperatures and endothermic pressures. Compared with the supercritical Brayton cycle, the thermal efficiency of novel system increases by 54.5~80.9%. It is found from deep insights into the thermodynamic results that the average heat transfer temperature difference between CO₂ and fuel is effectively reduced from 153.4 K to 16 K by split cooling of the fuel in the FCTCP system, which greatly enhances the matching of CO₂–fuel heat exchange temperatures and reduces the heat exchange loss of the system. Thermodynamic results also show that, in comparison to the supercritical Brayton cycle, the cooling capacity and power generation per unit mass flow rate of working fluid in the FCTCP system increased by 75.4~80.8% and 12.9~51.6%, respectively. The FCTCP system exhibits a substantial performance improvement, significantly enhancing the key characteristic index of the combined cooling and power supply system. This study presents a novel approach to solving the challenges of cooling and power supply in hypersonic aircrafts under limited fuel heat sink conditions, laying the groundwork for further exploration of thermal management technologies of hypersonic aircrafts. Full article

The implementation of heat sinks in high-power pulse electronic devices within hypersonic aircraft cabins has been facilitated by the emergence of innovative phase change materials (PCMs) characterized by excellent thermal conductivity and high latent heat. In this study, a representative material, layered porous media filled with paraffin wax, was utilized, and a three-dimensional numerical model based on the enthalpy-porosity approach was employed. A thermal response research was conducted on the Phase Change Heat Exchange Unit with Layered Porous Media (PCHEU-LPM) with different cooling methods. The results indicate that water cooling proved to be suitable for the PCHEU-LPM with a heat flux of 50,000 W/m². Additionally, parametric studies were performed to determine the optimal cooling conditions, considering the inlet temperature and velocity of the cooling flow. The results revealed that the most suitable conditions were strongly influenced by the coolant inlet parameters, along with the position of the PCM interface. Finally, the identification of the parameter combination that minimizes temperature fluctuations was achieved through the Response Surface Analysis method (RSA). Subsequent verification through simulation further reinforced the reliability of the proposed optimal parameters. Full article

In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics. Full article

Predictive Maintenance (PM) strategies have gained interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. Taking advantage of condition monitoring data from aircraft systems, Prognostics and Health Maintenance (PHM) practitioners have been predicting the life span of aircraft components by applying Remaining Useful Life (RUL) concepts. Additionally, in prognostics, the construction of Health Indicators (HIs) plays a significant role when failure advent patterns are strenuous to be discovered directly from data. HIs are typically supported by data-driven models dealing with non-stationary signals, e.g., aircraft sensor time-series, in which data transformations from time and frequency domains are required. In this paper, we build time-frequency HIs based on the construction of the Hilbert spectrum and propose the integration of a physics-based model with a data-driven model to predict the RUL of aircraft cooling units. Using data from a major airline, and considering two health degradation stages, the advent of failures on aircraft systems can be estimated with data-driven Machine Learning models (ML). Specifically, our results reveal that the analyzed cooling units experience a normal degradation stage before an abnormal degradation that emerges within the last flight hours of useful life. Full article

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process. Full article

Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification. Full article