Aircraft Auxiliary Power Unit Research Articles

Acoustic monitoring of an aircraft auxiliary power unit

In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft’s operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University’s Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines.

High-fidelity CFD modeling of pollutant dispersion from aircraft auxiliary power units (APUs) at a realistic airport and the effects on airport air quality

Many studies have investigated air-traffic impact on airport air quality through main-engine emissions, however very few have characterized the APU-related impact. As field-monitoring remains limited, our study used CFD simulations to provide a high-fidelity representation of 19 APU dispersion plumes in a realistic airport configuration. Results of three dominant wind configurations show that the global dispersion plume (from all APUs) was transported by incident winds. Depending on wind direction, pollutant trapping within a large downwash region was observed downstream of the terminal-building. Local differences were observed depending on source locations; emissions within the downwash region were pushed against the terminal-facade which promoted emission spillover on terminal-roof, while plumes within the unperturbed boundary layer were transported by incident winds. Quantitatively, APU pollutant concentrations dropped overall by 3–4 orders of magnitude around the terminal-building, while diluted concentrations by 5–6 orders of magnitude were monitored at the airport exit. Given source concentrations with an appropriate experimental apparatus, this model can serve as an airport air quality decision-making tool to finely characterize individual source impacts. Such information can help assist airport operation management (taxi to gate/runway, gate assignment, safe corridors for ground-handling personnel) so as to reduce/prevent the impact of pollutant hot spots depending on wind configurations.

Aviation application of dual-weighted neural network based on biomimetic pattern recognition

Abstract In order to solve the difficult problem of fault diagnosis in aviation equipment, this paper uses a biomimetic pattern recognition method. Compared to traditional pattern recognition, biomimetic pattern recognition is able to construct closed, complex geometries to cover all kinds of samples. Biomimetic pattern recognition is a “cognitive” approach, where the process between two similar things is considered asymptotic. It mathematically means that all similar samples in the feature space are continuously indistinguishable. The paper establishes a dual-weighted neural network model of an aircraft auxiliary power unit. Comparing the diagnostic process and results with traditional manual neural networks. The results show that biomimetic pattern recognition is a feasible and efficient diagnostic method for the fault diagnosis of aviation equipment. It provides a new way of thinking and method for the practical application of aviation equipment fault diagnosis.

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

This paper presents a comprehensive evaluation of the near-field acoustics of an aircraft auxiliary power unit (APU), based on experimental data acquired from an in-situ APU. The aim is to establish whether near-field acoustics can be implemented for online condition monitoring. The APU of Cranfield University’s demonstrator aircraft, a Boeing 737-400, has been instrumented to acquire acoustics (near-field and far-field) and vibration data in synchronization with aircraft state parameters under a wide range of operating conditions. The acquired data is first implemented to determine the efficacy of employing near-field / far-field microphones, and vibration sensors, to monitor the combustion noise and tonal frequency levels from the APU components. Subsequently, an evaluation of the broadband characteristics of the vibroacoustic data and its variations against APU states and performance parameters is conducted based on several categories of feature extraction techniques. The findings suggest that nearfield acoustics lacks the ability to capture the combustion noise process. In addition, the tonal frequencies are also lost due to the level of background noise, fluctuations in the APU speeds, and scattering effects. For the same reasons, the phase couplings occurring between the signals generated by the APU components cannot be detected using acoustic data. Nevertheless, the overall analysis substantiates that the near-field acoustic data can be used to predict the APU operating states and has the potential to be implemented for developing APU performance parameter estimation models to enable condition monitoring.

Thermodynamic Modeling of Modified Recompression Supercritical Co2 Power Cycle Using Waste Heat from Fuel Cell: Application to Aircraft Auxiliary Power Unit

Thermodynamic Modeling of Modified Recompression Supercritical Co2 Power Cycle Using Waste Heat from Fuel Cell: Application to Aircraft Auxiliary Power Unit

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit (APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So, actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network (HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-BiLSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-BiLSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.

Assessment of Augmented Reality Technology’s Impact on Speed of Learning and Task Performance in Aeronautical Engineering Technology Education

ABSTRACT Objective: This study compared learning and skill transfer among university aviation students using interactive Augmented Reality (AR) technology versus traditional paper-based instruction. While similar AR use and research in university education exists, this study piloted a comparative method assessing knowledge retention and transfer. Background: AR technology is a popular tool used in technical education. But learner behaviors observed like game play and exploration during this study could impact future learning strategy design as AR use increases. Method: 36 university undergraduate students enrolled in a university aeronautical engineering technology program were divided into AR and paper-based groups and compared on first-time task execution times for starting an aircraft auxiliary power unit (APU). A two-sample Kolmogorov-Smirnov test comparing times for task completion was used. Results: Learner task times using AR were consistently faster, replicating similar AR studies, compared to learners using paper-based. However, AR test subjects also took longer interacting with the technology, including gaming-style “play” and exploration of the digital twin AR flight deck environment. This is believed to enhance learner innovation, knowledge retention and transfer, warranting further study. Conclusion: AR users had significantly reduced task execution times. Pre-task “gamification and play” were also observed among the AR users, which could impact how educators and the industry assess and leverage learning strategies when using AR for job task training.

Development of a Far-Field Noise Estimation Model for an Aircraft Auxiliary Power Unit

Aircraft Auxiliary Power Unit (APU) is one of the major aircraft systems and is reported to be a key driver of unscheduled maintenance. So far, the research has been focused on the implementation of the APU thermodynamic state data to isolate and diagnose faults. To advance the available diagnostic techniques, research work has been initiated to explore the potential of employing far-field microphone data for the identification and isolation of APU faults. This paper aims to address the first step required in the overall effort and proposes a novel methodology for the development of a noise model that can be used for evaluating noise as a source of fault diagnostics. The methodology integrates experimentally acquired full-scale aircraft state and noise data, a physics-based APU thermodynamic model, and semi-empirical noise models to estimate the noise produced by an aircraft APU based on a limited parameter-set. The methodology leads to a model which works by estimating the unknown thermodynamic parameters from the limited dataset and then passes on the relevant parameters to noise estimation models (combustion/jet noise models). An inherent part of the model is the effect of multipath propagation and ground reflections for which a relationship has been analytically derived that considers all the necessary parameters. The developed model has been validated against experimental noise and thermodynamic data acquired from a Boeing 737-400 aircraft APU under several different operating conditions. The acquired noise estimates suggest that the proposed approach provides an accurate estimation of the far-field noise under a wide range of APU operating conditions, both at the sub-system and APU level. The model would act as an enabler to simulate APU noise data under degraded functional states and subsequently developing fault diagnostic schemes based on the far-field noise data.

Impact of fuel hydrogen content on non-volatile particulate matter emitted from an aircraft auxiliary power unit measured with standardised reference systems

Replacement of conventional petroleum jet fuel with sustainable aviation fuels (SAFs) can significantly reduce non-volatile Particulate Matter (nvPM) emissions from aircraft main engines and auxiliary power units (APUs). As part of the Initiative Towards sustAinable Kerosene for Aviation (ITAKA) project, the impact of fuel hydrogen content on nvPM number and mass emissions and particle size distributions were investigated using a GTCP85 APU burning blends of conventional (Jet A-1) and Hydrotreated Esters and Fatty Acids (HEFA)-derived (Used Cooking Oil and Camelina) aviation fuels. The measurements were conducted during two separate test campaigns performed three years apart, each employing a different regulatory compliant sampling and measurement reference system for aircraft engine nvPM emissions. The objective was to investigate the correlation of fuel hydrogen content with nvPM number and mass emissions at the engine exit plane (EEP) independent of fuel composition, measurement system, and ambient conditions. The nvPM number and mass emissions and size distributions systematically decreased with increasing fuel hydrogen content regardless of the fuel composition or APU operating condition. The measured nvPM emissions were particle loss-corrected to the EEP and normalised to a common fuel hydrogen content. Similar rates of nvPM reductions were observed for both test campaigns at all investigated APU operating conditions, confirming that engine exit nvPM reductions correlate with fuel hydrogen content for fuels of relatively similar compositions. This analysis method can be applied to emissions data from other engine types to compare the reduction in nvPM emissions for sustainable aviation fuels and blends.

Innovative power train configurations for aircraft auxiliary power units focused on reducing carbon footprint

Auxiliary power units constitute a non-negligible source of pollution not only in flight conditions but also at airports and their surrounding areas. This work compiles a technical feasibility assessment focused on the analysis of innovative alternatives to the traditional aircraft gas turbine based auxiliary power unit arrangement. The proposed innovations involve both new power train configurations and the use of different fuels, with the purpose of reducing both the primary energy consumption and the environmental impact of these systems. The core innovation proposed here consists in the use of a supercharged rotary engine as an alternative configuration to traditional auxiliary power units in aircraft. The rotary engine can achieve higher thermal efficiencies than gas turbines, which corresponds to a lower specific fuel consumption and, thus, a lower carbon footprint. Additionally, the proposed alternatives will involve hybrid configurations, providing thermal energy generation with electrical storage devices. Finally, the assessed arrangements will be based on the “more electric aircraft” concept, which implies a simplification of the aircraft non-propulsive power system design and allows the addition of batteries to the generating unit. In order to perform this assessment, different thermal models and simulations have been developed, allowing the quantification of the improvements related to the alternative proposed configuration.

A Hybrid Method of Remaining Useful Life Prediction for Aircraft Auxiliary Power Unit

The aircraft auxiliary power unit (APU) is mainly used to provide electricity and compressed air to the aircraft. Not only can it help to start main engines, but also it can provide essential thrust for the emergency landing. It is required to be reliable as high as possible when it is installed in the aircraft. However, the reality is that the degradation of APU is a nonlinear process. In this term, only a data-driven method or a physics-based method can hardly make accurate prediction for the remaining useful life (RUL) of the aircraft APU. Therefore, a hybrid RUL prediction method is proposed by fusing an artificial intelligence-based model and a physics-based model in this article. To be specific, Wiener process is used to formulate a physical model for generating degradation data. Then, the generated data and the corrected data are both utilized as the input of long short-term memory (LSTM) to enhance RUL prediction of the aircraft APU. The contributions of this study include: 1) A hybrid method for RUL prediction of the aircraft APU is proposed. 2) The influences of ambient environment and training data dependency of LSTM are solved. 3) The proposed method provides a novel RUL prediction method for the on-wing aircraft APU, which is evaluated by the real data.

In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

Aircraft auxiliary power unit performance assessment and remaining useful life evaluation for predictive maintenance

Modern commercial aircraft are usually configured with aircraft condition monitoring system to collect the operating data of subsystems and components, which can be used for airborne system health monitoring and predictive maintenance. This paper presents a baseline model based aircraft auxiliary power unit performance assessment and remaining useful life prediction method using aircraft condition monitoring system reports data, which can facilitate a cost-effective management of auxiliary power units of aircraft fleet. Firstly, the performance baseline model for auxiliary power unit is established using random forest method. Then a health index characterizing the performance degradation of in-service auxiliary power units is obtained based on the performance baseline model. Finally, the performance degradation trend is predicted using Bayesian dynamic linear model. To improve the prediction accuracy, four performance baseline models are established from the data of auxiliary power units under different operating conditions, among which an optimal model is determined. This data-driven baseline model can be used to quantify the performance degradation of auxiliary power units in service, and can be further used to evaluate the remaining useful life of auxiliary power unit using a Bayesian dynamic model. The developed approach is applied on a real data set from 22 auxiliary power units of a commercial aircraft fleet. The results show that the computed health index can effectively characterize the auxiliary power units performance degradation and the remaining useful life relative prediction errors are less than 4% when auxiliary power unit enters the rapid degradation stage. This would allow operators to accurately assess the performance degradation for the auxiliary power units and further proactively plan future maintenance events based on remaining useful life prediction.

Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

The reliability of the on-wing aircraft Auxiliary Power Unit (APU) decides the cost and the comfort of flight to a large degree. The most important function of APU is to help start main engines by providing compressed air. Especially on the condition of sudden shutdown in the air, APU can offer additional thrust for landing. Therefore, its condition monitoring has drawn much attention from the academic and industrial field. Among the on-wing sensing data which can reflect its condition, Exhaust Gas Temperature (EGT) is one of the most important parameters. To ensure the reliability of EGT, one kind of data-driven anomaly detection framework for EGT sensing data is proposed based on the Gaussian Process Regression and Kernel Principal Component Analysis. The situations of one-dimensional and two-dimensional input data for EGT anomaly detection are considered, respectively. The cross-validation experiments are carried out by utilizing the real condition data of APU, which are provided by China Southern Airlines Company Limited Shenyang Maintenance Base. The anomalous stuck condition of EGT sensing data is also detected. Experimental results show that the proposed EGT sensing data anomaly detection method can achieve better performance of false positive ratio, false negative ratio and accuracy.

Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine.

The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.

A Highly Flexible Approach on the Steady-State Analysis of Innovative Micro Gas Turbine Cycles

Steady-state simulation is an important method to investigate thermodynamic processes. This is especially true for innovative micro gas turbine (MGT)-based cycles as the complexity of such systems grows. Therefore, steady-state simulation tools are required that ensure large flexibility and computation robustness. As the increased system complexity result often in more extensive parameter studies also a fast computation speed is required. While a number of steady-state simulation tools for MGT-based systems are described and applied in literature, the solving process of such tools is rarely explained. However, this solving process is crucial to achieve a robust and fast computation within a physically meaningful range. Therefore, a new solver routine for a steady-state simulation tool developed at the DLR Institute of Combustion Technology is presented in detail in this paper. The solver routine is based on Broyden's method. It considers boundaries during the solving process to maintain a physically and technically meaningful solution process. Supplementary methods are implemented and described which improve the computation robustness and speed. Furthermore, some features of the resulting steady-state simulation tool are presented. Exemplary applications of a hybrid power plant (HyPP), an inverted Brayton cycle (IBC), and an aircraft auxiliary power unit (APU) show the capabilities of the presented solver routine and the steady-state simulation tool. It is shown that the new solver routine is superior to the standard Simulink algebraic solver in terms of system evaluation and robustness for the given applications.

Characterization of an Aircraft Auxiliary Power Unit Test Rig for Cycle Optimization Studies

Detailed information of the thermodynamic parameters, system performance, and operating behavior of aircraft auxiliary power units (APU) cycles is rarely available in literature. In order to set up numeric models and study cycle modifications, validation data with well-defined boundary conditions is needed. Thus, the paper introduces an APU test rig based on a Garrett GTCP36-28 with detailed instrumentation, which will be used in a further step as a demonstration platform for cycle modifications. The system is characterized in the complete feasible operating range by alternating bleed air load and electric power output. Furthermore, simulations of a validated numerical cycle model are utilized to predict the load points in the operating region which were unstable during measurements. The paper reports and discusses turbine shaft speed, compressor air mass flow, fuel mass flow, efficiencies, compressor outlet pressure and temperature, turbine inlet and outlet temperature as well as exhaust gas emissions. Furthermore, the results are discussed with respect to the difference compared to a Hamilton Sundstrand APS3200. Though the efficiencies of the GTCP36-28 are lower compared to the APS3200, the general behavior is in good agreement. In particular, the effects of separate compressors for load and power section are discussed in contrast to the GTCP36-28 system design comprising a single compressor. In general, it was shown that the GTCP36-28 is still appropriate for the utilization as a demonstration platform for cycle modification studies.

An efficient cloud prognostic approach for aircraft engines fleet trending

ABSTRACTThe implementation of prognostics and health management solutions is becoming increasingly important. Many industrials are interested in this maintenance process, especially Predix by General Electric and EngineWise by Pratt & Whitney. Predix allows to create innovative industrial internet applications that transform operational data in real-time into actionable information in several domains (aviation, healthcare, …). EngineWise is a world leader in the design, manufacture and service of aircraft engines and auxiliary power units. The prognostic process is a main step to predict failures before they occur by determining remaining useful life (RUL) of equipment. However, it also poses challenges such as reliability, availability, infrastructure and physics servers. To address these challenges, this paper investigates a cloud-based prognostic system that defines an approach ‘Prognostic as a Service.’ This approach will provide an efficient prognostic solution in the cloud computing. In this paper, three data-driven algorithms, Artificial Neural Network, Neuro-Fuzzy System and Bayesian Network, are discussed and implemented to estimate the RUL. They are tested for aircraft engines fleet. Furthermore, and in order to test the efficiency of our solution, we have studied the performance of the prognostic system according to the accuracy, precision and mean squared error.

Auxiliary power unit failure prediction using quantified generalized renewal process

Auxiliary Power Unit (APU) is an essential component utilized in modern civil aircraft. In order to meet APU availability requirements, failure prediction should be performed in an effective way. To this end, APU performance deterioration can be modeled by a Generalized Renewal Process (GRP). In this paper, aircraft APU failure prediction is implemented using a Weibull-based GRP (WGRP). However, the effect of maintenance activities on aircraft APU required in WGRP is difficult to be quantified. To solve this problem, a new model named Quantified Generalized Renewal Process (QGRP) is developed in this paper. In this model, APU performance-related test parameters after repairs are utilized to quantify the maintenance effect. Based on the proposed QGRP model, the conditional failure rate and hazard rate of each aircraft APU at a future point in time can be calculated based on the APU's virtual age and be combined to predict the number of failures of a fleet of APUs. The performance of the proposed QGRP model is validated using a three-year data set provided by China Southern Airlines. The results show that the QGRP model is effective in aircraft APU failure prediction.

Determination of the effective density and fractal dimension of PM emissions from an aircraft auxiliary power unit

Gas turbine particulate matter (PM) emissions contribute to air quality degradation and are dangerous to both human health and the environment. Currently, PM mass concentrations determined from gravimetric measurements are the default parameter for gas turbine emissions compliance with PM regulations. The measurement of particle size however, is of significant interest due to its specific effects on health and climate science. The mass concentration can be determined from the number-size distribution measurement but requires the experimental evaluation of effective density of a number of particles to establish the power-law relationship. In this study, the effective density of PM emissions from an aircraft Auxiliary Power Unit (APU) with petroleum diesel, conventional aviation fuel (Jet A-1) and a multicomponent surrogate fuel (Banner NP 1014) as combusting fuels have been compared. An experimental configuration consisting of a Differential Mobility Analyzer, a Centrifugal Particle Mass Analyzer and a Condensation Particle Counter (DMA-CPMA-CPC) was deployed for this purpose. Overall, a decrease in the effective density (220–1900km−3) with an increase in the particle size was observed and found to depend on the engine operating condition and the type of fuel undergoing combustion. There was a change in the trend of the effective densities between the PM emissions generated from the fuels burnt and the engine operating conditions with increasing particle size.