A method to predict propulsion architecture for future jetliners

The electrification of propulsion technologies in aerospace engineering has been considered as the future-vision for aviation industries. The Selection of electrified propulsion architecture for a particular mission-flight has become a new challenge. In this paper, a method to study different propulsion architectures and battery sizing for jetliners using multi-physics modeling is presented. The designed approach is then carried out to investigate conventional and hybrid/electric propulsion architectures of a commercial jetliner (Avro RJ-85). Based on the comparative study, an effective propulsion architecture is also suggested. The designed method is expected to help predict effective propulsion architecture for future aviation

An unknown input observer-EFIR combined estimator for electro-hydraulic actuator in sensor fault tolerant control application

This paper presents a novel unknown input observer (UIO) integrated extended finite impulse response (EFIR) estimator (UIOEFIR) and its application for an effective sensor fault tolerant control of an electro-hydraulic-actuator (EHA). The proposed estimator exploits the UIO structure in the EFIR filter. Thus, it requires only a small number of historical data (N) whilst ensuring threefold: i) Sensor fault and system-state estimation accuracy under time-correlated noise ii) The number of estimator-design-parameters is significantly minimized. iii) Robust residual generation. A Lyapunov-stability-based theory is carried out to study its convergence condition. Next, an EHAbased test rig has been setup and sensor FTC is performed by carrying this estimator as a part of fault diagnosis algorithm to evaluate its performance by both simulation and realtime experiments. Results highlight that under optimal setting (N = Nopt), the estimator performance is near-accurate to the very-well-developed Extended Kalman Filter-based unknown input observer in an undisturbed condition but significantly outperformed while dealing with time-correlated noise under the same control environment. The estimator also shows its robustness under below-optimal setting (downgrading Nopt by 50%.) while performing in real-time sensor fault-tolerant control

Modeling and fault tolerant control of an electro-hydraulic actuator

In the modern industry, electro-hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/ force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor-fault conditions has been carried out. In the proposed FTC system, the extended Kalman-Bucy unknown input observer (EKBUIO) -based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions

Force reflecting joystick control for applications to bilateral teleoperation in construction machinery

This paper presents a simple and effective force reflecting joystick controller for applications to bilateral teleoperation in construction machinery. First, this controller is a combination of an advanced force reflecting gain tuner and two local adaptive controllers, master and slave. Second, the force reflecting gain tuner is effectively designed using recursive least square method and fuzzy logics to estimate directly and accurately the environmental characteristics and, consequently, to produce properly a force reflection. Third, the local adaptive controllers are simply designed using fuzzy technique and optimized using a smart leaning mechanism to ensure that the slave follows well any given trajectory while the operator is able to achieve truly physical perception of interactions at the remote site. An experimental master-slave manipulator is setup and real-time control tests are carried out under various environmental conditions to evaluate the effectiveness of the proposed controller

A modified-optimal energy management strategy of fuel cell- battery hybrid energy storage system for marine application

Considering the present limitations of battery technology, a hybrid combination of fuel cells (FCs) and batteries can be considered as one of the environment friendly, reliable, and efficient energy solutions for marine ship applications. However, proper energy and power management are some of the critical issues for fuel cell-based energy storage system (ESS) because the degradation of a fuel cell lifetime is strongly affected by its operating condition. In this paper, a modified-optimal energy management strategy (MOEMS) is proposed which determines the power-split ratio between the FC and battery efficiently. By integrating the popular gradient-descent algorithm into a state machine control the proposed controller is realized. The performance of the MOEMS is compared with conventional EMS with simulation. Results suggest that the proposed EMS improve the fuel cell lifetime by 44% while reducing the fuel consumption by 7% compared to the basic EMSs

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population