Engineering Model Research Articles

Multi-mode Optimization of Diesel Engine Turbocharging

The thermogasdynamic engine system consisting of a number of subsystems of varying complexity is considered. The solution to the optimization problem in general is carried out by a multi-step iterative process of exchanging information between the levels repeatedly with refinement for all successive iterations of the initial data. Mathematical models of gas-dynamic processes in the flow section in variable modes, the simulation model of a gas turbine engine, and the model of multi-mode optimization of a gas turbine engine have been developed. The optimization of general-mode parameters is carried out in the space of points each of which reaches a minimum expression for the mode parameters.

An Engine Deterioration Model for Predicting Fuel Consumption Impact in a Regional Aircraft

A deterioration cycle model is presented, designed to consider the turbomachinery efficiency losses that are expected during real engine in-service operations. The cycle model was developed using information from practical experience found in the literature to account for both short- and long-term deterioration effects; the former occurring during the first flight cycles, the latter due to regular in-service operation. This paper highlights the importance of analyzing the inter-turbine temperature margin (ITTM) to track engine deterioration to determine the extent of an in-service engine life. The proposed model was used to assess the ITTM and fuel consumption impact in the CRJ-700 regional aircraft (powered by two CF34-8C5B1 engines) for three representative missions: short (0.4 h), average (1.4 h), and long (2.5 h), considering different levels of engine deterioration, from the new engine level up to fully deteriorated. The fuel consumption at the new engine level (zero deterioration) was validated against a real-time engine model embedded in a Level-D flight simulator, the so-called Virtual Research Flight Simulator located at the Laboratory of Applied Research in Active Control, Avionics, and AeroServoElasticity. The errors found in this validation for the trip mission fuel consumption in the short, average, and long missions were −3.6, +0.9, and +0.6%, respectively. The cycle model predictions suggest the ITTM for a new engine is 55.2 °C, whereas for a fully deteriorated engine, it is 26.4 °C. Finally, in terms of fuel consumption, the results presented here show that an average CF34-8C5B1 engine shows an increase in the cumulative fuel consumption of 2.25% during its life in service, which translates to a 4.5% impact in aircraft fuel consumption.

Numerical Investigation on Energetic, Combustion, and Emissions Parameters of a Diesel Engine Fueled With Diesel/Butanol and Diesel/Pentanol

Abstract In this article, the effect of butanol/diesel and pentanol/diesel blended fuel with different proportions on the energetic, combustion, and emissions parameters of a diesel engine was studied. First, an engine model was established using Diesel-RK software. The modeled engine was a direct injection diesel engine having a fixed compression ratio, injection timing, and speed of 17.5:1, 23 degbTDC, and 1500 rpm, respectively. After that, a set of experiments was conducted using a 5% pentanol and 95% diesel by volume fuel blend maintaining the same operating conditions as the simulation, and the results of the experiment were compared with the numerical result using the same blend. The simulated results were found to be in respectable agreement with experimental findings. The analysis of the simulated results shows that at 100% load using 15% butanol and 85% diesel by volume and 15% pentanol and 85% diesel by volume brake thermal efficiency was increased by 0.96% and 0.8%, respectively. The emission of NOx was reduced by 24.4% and 10.75% on average using butanol and pentanol blends, respectively. The instantaneous heat release rate and ignition delay increase with the higher alcohol–diesel blends, whereas the peak pressure rise and combustion temperature decrease. Smoke emissions were reduced by 3.31–8.75%, and specific particulate matter emissions decreased by 20.9% and 15.07%, respectively, on average with the addition of butanol/pentanol in neat diesel.

Simulation Analysis of a Methanol Fueled Marine Engine for the Ship Decarbonization Assessment

Methanol as marine fuel represents one of the most cost-effective and practical solutions towards low-carbon shipping. Methanol fueled internal combustion engines have a high level of technological readiness and are already available on the market; however, technical data in terms of fuel consumption and emissions are not yet easily accessible. For this reason, the present study deals with the simulation of a virtual spark-ignition methanol engine, carried out in a Matlab-Simulink© R2023a environment to assess the CO2 emissions in several working conditions of a possible ship power system. The thermodynamic model of the methanol fueled engine is derived from a marine gas engine simulator, already validated by the authors in a previous work. This article presents the relevant modifications necessary to adapt the engine to the methanol fuel mode with regard to the different fuel characteristics. The simulation analysis compares the results of the virtual methanol engine with available data from a similar, existing gas engine, highlighting the differences in efficiency and carbon dioxide emissions.

Real-time optimization control of variable rotor speed based on Helicopter/ turboshaft engine on-board composite system

In order to adequately leverage the advantages of coaxial compound helicopters (CCH) to enhance overall performance and efficiency through variable rotor speed, and to improve the fuel economy of the composite propulsion system, a real-time optimization control method of variable rotor speed based on helicopter/turboshaft engine on-board composite system is proposed. Firstly, a steady-state model of the CCH required power and an on-board inverse model of the turboshaft engine based on an improved deep neural network propulsion system model (AdamDNN-PSM) are established. On this basis, an on-board adaptive model based on adaptive square root Kalman filter (ASRUKF) estimator is employed to correct the precision of the on-board inverse model and to track the real engine state. Finally, by adopting the feasible sequence quadratic programming (FSQP) method, an optimal speed rotor (OSR) is obtained through real-time optimization control, which reduces fuel consumption during cruising while considering both the geometric and aerodynamic constraints of the rotor. The simulation results demonstrate that the accuracy and real-time performance of the on-board inverse model have been significantly improved. The real-time optimization control method for OSR effectively reduces fuel consumption during cruising and enhances the CCH performance of the entire flight envelope.

Coupling mechanical model and failure of aeroengine ceramic matrix composite based on genetic algorithm

In order to understand the coupling mechanical model of aeroengine ceramic matrix composite, the author proposed a coupling mechanical model and failure study of aeroengine ceramic matrix composite based on genetic algorithm. The author first analyzed that the real-time model of an aircraft engine and the matching accuracy of the engine directly affect the accuracy of aircraft engine fault diagnosis, a least squares support vector regression (AGA LSSVR) method based on adaptive genetic algorithm was proposed to modify the real-time model of an aircraft engine, effectively improving the matching accuracy of the model. Secondly, the impact of parameter selection in least squares support vector machines on model correction was analyzed, and an adaptive genetic algorithm was used to search for the optimal parameters in the parameter selection space. Finally, compare the correction effects of methods such as (BP) neural network, support vector regression machine, AGA LSSVR in airborne models. The results indicate that: The proposed AGA LSSVR has good correction accuracy, which verifies the effectiveness of the correction model.

Analysis of Jet Blast Distance of a Refined Engine Nozzle Model for Departing Aircraft

To evaluate the influence distance and evolution law of the jet blast under the full thrust state of the aircraft engine, we constructed a refined single-engine nozzle model in this study. Using a specific B737-800 engine as an illustrative example, this model takes into account both the internal combustion of the engine and the shearing effect of the tail cone on the airflow; based on a structured grid, the grid and far-field independence of the numerical solution were verified. The numerical simulation results obtained via the Fluent software were compared with the data outlined in the Boeing aircraft characteristics manual for a thorough analysis. Our results indicated that the calculated jet influence distance from the refined nozzle model aligns more closely with the data in the aircraft characteristics manual, albeit with superior accuracy compared to the simplified nozzle model employed in previous studies. The findings of this study can serve as a valuable reference for computing jet influence distances across various aircraft types and engine models, providing data support for the study of safety intervals for aircraft crossing the runway behind the takeoff point.

Finite-time H∞ rate anti-bump control for a class of switched systems under state-dependent switching

This paper mainly investigates the finite-time rate anti-bump switching (RABS) control problem for switched systems. A novel multiple convex Lyapunov function is first proposed by constructing a convex combination of positive definite matrices for the RABS control problem of switched systems. By imposing a prespecified dwell time on state-dependent switching, a combined switching law is devised based on the new Lyapunov function to ensure that Zeno phenomenon is prevented. Then, a bumpless control scheme is proposed to reduce the big and undesired jump in the rate of system at switching instants while achieving the disturbance suppression in finite time. In the end, through an ingenious Simulink model to characterize the combined switching law and finite-time H∞ rate anti-bump controller, an actual engine model is used to demonstrate the validity of the proposed approach.

Diesel engine vibration analysis using artificial neural networks method: Effect of NH3 additive in biodiesels

Diesel engine parameters, such as fuel and its additives, play an essential role in minimising the effects of engine vibration. This study aimed to use artificial neural networks (ANN) to model and analyse diesel engine vibration characteristics at different engine speeds using NH3 as an additive in hazelnut (HD), peanut (PD), and waste-cooking oil (WD) biodiesels. The results showed good correlations between the ANN models and experimental results using regression analysis methods. The ANN models for diesel engines showed high accuracy. The ANN models indicated that a 5 % NH3 additive decreased engine vibration for HD and PD.In comparison, 10 % and 15 % NH3 additive ratios increased engine vibration for HD, PD, and WD due to low combustion quality. The lowest vibration levels occurred with P100, P95A5, P90A10, and P85A15 at 1200 rpm. H100 and H95A5 produced the highest diesel engine resultant vibration (DERV) values. All ANN models generated the lowest and highest DERV values at 1200 rpm and 2100 rpm, respectively. The RMS method showed that H95A5, P85A15, and W85A15 contributed the most to diesel engine vibration. Using a low amount of NH3 additive positively affected DERV for HD and PD but not for WD.

Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping

Condition-based maintenance of aero-engines requires real-time gas-path fault diagnosis, which is crucial for reducing costs and enhancing aircraft attendance. It is imperative to ensure a low false alarm rate in fault diagnosis algorithms. Inherent variations between engines and age-related performance deterioration can lead to an increase in false alarms, necessitating online algorithm tuning. However, insufficient fault data in online tuning may lead to class imbalance and algorithmic forgetfulness of fault features, compromising effectiveness. To tackle this issue, this paper introduces a novel hybrid diagnosis algorithm that utilizes feature filtering and mapping. This method entails an engine model, a feature extractor, a feature filter, a fault classifier, and several feature mappers. The feature mappers are designed to generate fault features based on the incoming fault free data for rebalancing the tuning dataset. And the feature filter is introduced to prevent weight collapse during the online tuning. Validation through a 400-hour life cycle simulation test and a real data test indicate that the method outperforms the benchmark methods. It overcomes the class-imbalanced learning problem, showcasing its potential for aero-engine gas-path fault diagnosis.

RETRACTED: Research on the kinetics characteristics and in-cylinder combustion of opposed rotary piston engine for different power output modes

As a new type of internal combustion engine, opposed rotary piston (ORP) engine has the advantages of simple structure and high working frequency, and can achieve high power density. Consequently, it stands as an ideal power source for hybrid power systems and unmanned aerial vehicles. The ORP engine has different power output modes, corresponding to different piston rotation and volume evolutions of combustion chamber. However, the effect of power output modes on kinetics characteristics and in-cylinder combustion of ORP engines are still uncovered. In this paper, the kinematic model of an ORP engine is established for scenarios involving power output by different shafts (Shaft 1 and Shaft 3). Meanwhile, the piston rotation patterns and cylinder volume variations are investigated correspondingly. Further, three-dimensional numerical model of the ORP engine is established, enabling analysis of in-cylinder combustion characteristics, engine performance, as well as nitrogen oxides (NOx) emissions. The results indicate that the volume variations of all cylinders follow a consistent pattern under the power output by Shaft 3. A pair of opposed cylinders features longer durations of intake and expansion strokes, and shorter durations of compression and exhaust strokes, the other pair of combustion chambers is the opposite under the power output by Shaft 1. The engine demonstrates a charging efficiency of 95.36 %, with corresponding indicated thermal efficiency and indicated power output of 38.08 % and 40.9 kW, respectively. The two adjacent cylinders present significantly different operation processes in the case of the power output by Shaft 1, achieving charging efficiencies of 94.29 % and 89.11 %, respectively, with the indicated thermal efficiency of 34.49 % and 31.60 %. The corresponding minimum indicated specific fuel consumption under the two power output modes are 211.7 g/kW∙h and 243.7 g/kW∙h, respectively, accompanied by NOx emission factors of 25.3 g/kW∙h and 17.6 g/kW∙h. With the given range of ignition timing, a trade-off relationship exists between the engine performance and NOx emissions. The power density of the case of power output by Shaft 3 is superior to that of Shaft 1.

Taking a Tailored Approach to Material Design: A Mechanistic Study of the Selective Localization of Phase-Separated Graphene Microdomains.

To achieve multifunctional properties using nanocomposites, selectively locating nanofillers in specific areas by tailoring a mixture of two immiscible polymers has been widely investigated. Forming a phase-separated structure from entirely miscible molecules is rarely reported, and the related mechanisms to govern the formation of assemblies from molecules have not been fully resolved. In this work, a novel method and the underlying mechanism to fabricate self-assembling, bicontinuous, biphasic structures with localized domains made up of amine-functionalized graphene nanoplatelets are presented, involving the tailoring of compositions in a liquid processable multicomponent epoxy blend. Kinetics studies were carried out to investigate the differences in reactivity of various epoxy-hardener pairs. Molecular dynamics simulations and in situ optical photothermal infrared spectroscopy measurements revealed the trajectories of different components during the early stages of polymerization, supporting the migration (phase behavior) of each component during the curing process. Confirmed by the phase structure and the correlated chemical maps down to the submicrometer level, it is believed that the bicontinuous phase separation is driven by the change of the miscibility between various building blocks forming during polymerization, leading to the formation of nanofiller domains. The proposed morphology evolution mechanism is based on combining solubility parameter calculations with kinetics studies, and preliminary experiments are performed to validate the applicability of the mechanism of selectively locating nanofillers in the phase-separated structure. This provides a simple yet sophisticated engineering model and a roadmap to a mechanism for fabricating phase-separated structures with nanofiller domains in nanocomposite films.

Thermal Systems Modeling and Control of Flex-Fuel Combustion Engines for Real-Time Applications

The modeling of the engine as a thermal system, in addition to enhancing the understanding of local and global energy flows, can be used in conjunction with thermodynamic control to reduce fuel consumption and emissions. This article presents some considerations on thermal system modeling in order to use the model in a flex-fuel gasoline-ethanol engine without compromising real-time response. To avoid calculating the power loss to the cylinder wall complex and computationally expensive, a different methodology was employed. Due to the enthalpy-based approach used, it was necessary to model both the gas temperature in the intake manifold and the exhaust manifold, aiming for simplicity once again to maintain real-time response unaffected.

Long-term source apportionment of PM2.5 across the contiguous United States (2000-2019) using a multilinear engine model

Identifying PM2.5 sources is crucial for effective air quality management and public health. This research used the Multilinear Engine (ME-2) model to analyze PM2.5 from 515 EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites across the U.S. from 2000 to 2019. The U.S. was divided into nine regions for detailed analysis. A total of seven source types (tracers) were resolved across the country: (1) Soil/Dust (Si, Al, Ca and Fe); (2) Vehicle emissions (EC, OC, Cu and Zn); (3) Biomass/wood burning (K); (4) Heavy oil/coal combustion (Ni, V, Cl and As); (5) Secondary sulfate (SO42-); (6) Secondary nitrate (NO3-) and (7) Sea salt (Mg, Na, Cl and SO42-). Furthermore, we extracted and calculated secondary organic aerosols (SOA) based on the secondary sulfate and nitrate factors. Notably, significant reductions in secondary sulfate, nitrate, and heavy oil/coal combustion emissions reflect recent cuts in fossil-fueled power sector emissions. A decline in SOA suggests effective mitigation of their formation conditions or precursors. Despite these improvements, vehicle emissions and biomass burning show no significant decrease, highlighting the need for focused control on these persistent pollution sources for future air quality management.

Predicting compressor mass flow rate using various machine learning approaches

Abstract A major focus of the present study is to construct high-fidelity models for predicting corrected mass flow rates based on the collected compressor map data. Both traditional machine learning research and modern deep learning techniques have been employed to obtain well-fitted regression models of compressor mass flow rate. As traditional machine learning methods, Multiple Linear Regression and Random Forest, are conducted on compressor maps for prediction of corrected mass flow rate. The time series-based deep learning models are able to capture the overall trend of a given input for specific map data. Therefore, a time series-based deep learning technique, namely Gated Recurrent Unit has been employed to improve regression results. Besides, the prediction capabilities of the models, results also show that the proposed models can be used for the development of dynamic aero-thermal mathematical models of gas turbine engines and mass flow rate models created for dynamic compressors in other disciplines.

A novel approach for the lifetime prediction and structural health monitoring of concrete sewer systems exposed to biogenic sulphide corrosion

A novel, combined experimental-modelling approach is presented for the estimation of the corrosion depth, load bearing capacity and lifetime of unreinforced concrete sewer pipes exposed to prolonged biogenic sulphide corrosion. The biogenic sulphide corrosion process is mimicked through two types of representative chemical experiments, namely: (i) long-term experiments performed under moderately controlled pH conditions, where dry-cast concrete cube samples are exposed to monthly refreshed sulphuric acid solutions with initial pH values of 3, 2 and 1 for a period of 12 months, and (ii) short-term experiments carried out under highly controlled pH conditions, in which dry-cast concrete disk samples are subjected to sulphuric acid solutions with almost constant pH values of 2, 1 and 0.5 for a period of two months. By applying X-ray diffraction, optical microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy analyses, insight into the micro-scale morphology and elemental composition of the corrosion profile is obtained. In addition, the corrosion front and depth characteristics are measured by conducting phenolphthalein tests and analyses of surface colour and texture of macro-scale samples. From the experimental results, the time course of the corrosion depth is calibrated with a model for sulphate attack under a constant pH level. The model formulation is combined with detailed finite element method results from the literature to predict the long-term load bearing capacity of a concrete sewer pipe. The model is subsequently generalised for sulphate corrosion under a varying pH level. When combined with the installation of pH measuring devices on the inside of sewer pipes, the current engineering model may serve as an excellent practical tool for continuously monitoring the structural health and predicting the lifetime of in-situ sewer systems subject to sulphate attack under random acidity fluctuations.

Design and Characterization of the Engineering Model of the Spectrometer Onboard LuSEE‐Night

AbstractThe Lunar Surface Electromagnetics Explorer—Night, LuSEE‐Night, is a low‐frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio‐quiet farside of the Moon. The LuSEE‐Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA's Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5−50 MHz band. The engineering model (EM) of the four‐channel spectrometer has been developed. The EM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the EM meets all the requirements for LuSEE‐Night. Three mitigation techniques have been implemented and verified to suppress self‐induced electromagnetic interference. The flight model of the spectrometer is currently being developed and is scheduled to be shipped to the integration site in early 2024.

Developing and correction methods for a one-dimensional axial flow compressor model

This paper establishes a one-dimensional model of an axial flow compressor using the Average Mid-Diameter Method and employs polynomials to adjust the off-design stagger angle and off-design loss models. Furthermore, a model stepwise correction strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed. The accuracy of the corrected one-dimensional axial flow compressor model was verified and integrated into the overall engine model for validation. Simulation results indicate that the maximum error in calculating the characteristics of the established one-dimensional axial flow compressor model does not exceed 6%, and the maximum error in simulating the overall engine model remains below 4%. These findings validate the effectiveness of modeling and correction methods for a one-dimensional axial flow compressor model targeting aero-engine component-level model.

Analysis of the interactive effects of scavenge parameters on spark ignition two-stroke diesel engines

The scavenging efficiency of a two-stroke diesel engine affects the formation of the mixture in the engine cylinder, which in turn affects the engine’s performance. A one-dimensional simulation model of a two-stroke diesel engine is established using GT-POWER, and the model with original experimental data is calibrated as boundary conditions. Firstly, the influence of crankcase compression ratio on engine performance of the two-stroke engine was investigated to obtain an appropriate crankcase compression ratio; Then, based on the optimization index of scavenging efficiency, the optimal combination of engine air port structure and timing is obtained through orthogonal optimization calculation method. To achieve higher scavenging efficiency, when the phase of the air port remains unchanged, a scavenging port width ratio of 1.0 and an exhaust port width ratio of 0.8 can achieve a scavenging efficiency of 97.2%; When the air port size remains unchanged, the engine scavenging efficiency reaches 96.44% when the scavenging port timing is 123° CA and the exhaust port timing is 89° CA.

A model-based control strategy for turbo-shaft engine

This paper takes the turbo-shaft engine (the power source of the helicopter) as the research object, and mainly conducts the simulation of its related control law and control structure. The working principle of the turbo-shaft engine is briefly introduced initially, and then the component-level model of the turbo-shaft engine is established. This paper focuses on the design of the control law upon the turbo-shaft engine, and verifies the designed control law through the results of simulation by MATLAB/Simulink. Finally, the relevant data collected from 240KW hybrid system is taken into the consideration to verify the control algorithm. The results show that the control accuracy of the corresponding control algorithm in this paper is high (accuracy≤5%).