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Density is an important indicator for evaluating diesel fuel quality that directly affects the injection timing and injection rule of the engine, and also has a significant impact on the spray broken particle size, the spray penetration distance, the spray cone angle, and so forth, which in turn affects the combustion process and pollutant emission of the compression-ignition engine. Therefore, it is important to accurately predict the diesel fuel mixture density in industrial and compression-ignition engines. However, the mathematical models for predicting the density of diesel fuel mixture with changed temperature are relatively lacking and less accurate, especially for ternary diesel fuel mixtures with different physicochemical properties. This paper proposes a mathematical model including binary and ternary diesel mixtures under changed fuel volume fraction and temperature, and published data were used for verification. The data verification results show that: for the density prediction of binary diesel fuel mixtures at constant temperature, the average relative deviation (ARD) is 0.0245%, the RMS error (RMSE) is 0.000344, and the correlation coefficient (R) is 0.9993. For the density prediction of binary diesel fuel mixtures at changed temperature, the ARD is 0.0609%, the RMSE is 0.000695, and R is 0.9980. For the density prediction of ternary diesel fuel mixtures at constant temperature, the ARD is lower than 0.0571%, the RMSE is lower than 0.000610, and R is higher than 0.9861. For the density prediction of ternary diesel fuel mixtures at changed temperature, the ARD is 0.0484%, the RMSE is 0.000513, and R is 0.9996. The diesel mixed fuel density prediction model proposed in this paper has good accuracy and calculation convenience, and provides important reference value for measuring or designing the density of diesel mixed fuel in the field of compression-ignition engines. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

This article presents the authors' considerations regarding the possibilities of developing fuel equipment for modern compression ignition engines used in special and non-road vehicles. The paper discusses the process of fuel combustion and atomization in the chamber of a piston combustion engine. The paper then presents the concept of modifying the atomizer of a modern fuel injector for operation using hydrogen-containing fuels of plant origin. The authors present a review of tests performed using an engine dynamometer on a modern engine with a Common Rail system running on biofuel. The CI engine operated with standard and modified fuel injectors. During the tests, the external ecological characteristics of the engine were analyzed as a function of rotational speed; the values of injection doses at individual rotational speeds and their effects on the characteristics were read from the current parameters, and the pressure and temperature in the engine's combustion chamber were measured. The research results show that implementing the changes proposed by the authors of this work is a good direction for the development of compression ignition engines. [ABSTRACT FROM AUTHOR]

AbstractThe unique mission of the helicopter compared to the fixed-wing aircraft introduces challenges to the aerodynamic stability of the turboshaft engine. Thus, the assessment of the availability of stability margin for special operating conditions in the envelope of this equipment is required to guarantee flight safety. In this paper, a rapid nonlinear complete engine method is developed to investigate the effect of helicopter ground-effect operation and high-altitude hovering on aerodynamic stability by considering factors including inlet distortion and component matching. Research indicated that the most dangerous point of stability margin in a unique section of the flight envelope is high altitude hovering, with about a 16.7% reduction in available stability margin compared to standard ground test conditions. This paper also investigates the effects of typical helicopter ground effects on a turboshaft engine and shows that the stability margin will be reduced by a maximum of 1.43% in hovering if the influence of ground effects in the flight envelope is not considered in the design. [ABSTRACT FROM AUTHOR]

This paper introduces a feedforward control method for physical systems that can be described with linear parameter-varying (LPV) models. The proposed feedforward controller structure is consequently derived from a generic LPV representation and is shown to be identifiable directly from noisy measurement data. The identified structure is advantageous for feedforward control, as using a simple least squares algorithm allows to parameterise basis functions representing the required input trajectory to follow a given output trajectory. Also, with the proposed regularisation, the input trajectory remains bounded even when the physical system exhibits non-minimum phase behaviour. Additionally, the proposed controller structure does not possess states but only considers the inputs and outputs signals and their derivatives, leading to a unique physical interpretation of each controller's parameter. Multiple feedforward controllers identified at various operating points can therefore be directly merged to create a parameter-varying controller. A nonlinear and locally non-minimum phase system is considered in this study, i.e. an engine air path, to evaluate the performances of the proposed feedforward strategy. The controller parameters are first identified from noisy measurement data, and then the proposed feedforward controller is implemented with a feedback controller to track the exhaust pressure and $ \mathrm {NO}_{\mathrm {x}} $ NO x concentration. Using a detailed physical simulation of the engine air path, the proposed feedforward strategy showed encouraging output tracking performances compared to state-of-the-art control methods. The presented feedforward method is shown to be straightforward to identify and calibrate while guaranteeing a contained computational complexity and being applicable to many physical systems thanks to its modularity. [ABSTRACT FROM AUTHOR]

In an internal combustion engine, interactions of fuel droplets and heated walls can significantly affect the combustion process and engine performance. The formation and characteristics of secondary droplets from drop-wall interactions are functions of various factors such as fuel properties, impact velocity, ambient conditions, and wall temperature. Understanding the impact behavior is important to optimize the distribution of the fuel-air mixture for efficient and clean combustion and to develop a comprehensive spray-wall interaction model. In this study, three-dimensional smoothed particle hydrodynamics (SPH) simulations are performed to investigate the interactions of fuel droplets with a heated wall at atmospheric and elevated pressures over a range of Weber numbers (We). The SPH model is validated using available experimental data. Secondary atomization is characterized by using size distributions for different fuels. The resulting droplets vary in size, where secondary droplets are mostly below 7 µm in diameter. Following these cases, this paper qualitatively describes the impact process and proposes empirical correlation relating the mean secondary droplet size to ambient pressure in the film-boiling regime. Postimpingement vaporization characteristics are also analyzed and compared for fuels with drastically different vapor pressures. [ABSTRACT FROM AUTHOR]

This paper presents a numerical investigation of the effect of wing integration on the aerodynamic behavior of a typical large civil aero-engine exhaust system at wind-milling flow conditions. The work is based on the dual stream jet propulsion (DSJP) test rig, as will be tested within the transonic wind tunnel (TWT) located at the aircraft research association (ARA) in the UK. The DSJP rig was designed to measure the impact of the installed pressure field due to the effect of the wing on the aerodynamic performance of separate-jet exhausts. The rig is equipped with the dual separate flow reference nozzle (DSFRN), installed under a swept wing. Computational fluid dynamic simulations were carried out for representative ranges of fan and core nozzle pressure ratios (CNPR) for "engine-out" wind-milling scenarios at end of runway (EOR) takeoff, diversion, and cruise conditions. Analyses were done for both isolated and installed configurations to quantify the impact of the installed pressure field on the fan and core nozzle discharge coefficients. The impact of fan and core nozzle pressure ratios, as well as freestream Mach number and high-lift surfaces on the installed suppression effect, was also evaluated. It is shown that the installed pressure field can reduce the fan nozzle discharge coefficient by up to 16%, relative to the isolated configuration for EOR wind-milling conditions. The results were used to inform the design and setup of the experimental activity which is planned for 2023. [ABSTRACT FROM AUTHOR]

This paper provides a framework based on machine learning approach in I4.0 environment to predict the remaining useful life of an aviation engine. For illustration purpose, an industrial case study is presented which applies machine learning algorithms to analyze the data collected using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) simulation which includes run-to-degradation data for a turbofan engine. The results obtained from the study validate the proposed framework to identify prominent features and perform sequential analysis on unstructured data for predicting the remaining useful life of an aviation engine. Six machine learning models are applied to the dataset containing four subsets: FD001, FD002, FD003 and FD004 in C-MAPSS dataset each working on different degradation conditions for turbofan engine. For FD001, random forest had the lowest RMSE (11.59), and for FD002, FD003 and FD004, the lowest RMSE was given by LGBM classifier (12.78, 7.95 and 11.04), respectively. From the findings, it is observed that LGBM performs better with higher AUC 89% and lowest RMSE. The proposed framework can be applied to a wide range of failure prediction applications. Regardless of the underlying physics, ML-based data-driven methodologies can be used to analyze a wide range of systems. [ABSTRACT FROM AUTHOR]

Engine as the core component of mechanical equipment, its operating state directly affects whether the equipment can operate normally. Predicting the engine remaining useful life (RUL) can monitor the health of the engine in real time and formulate a timely and reasonable maintenance plan. Aiming at the engine monitoring data with various and long time span, we propose a direct prediction method of engine RUL based on particle swarm optimization (PSO) optimized multi-layer Long Short-Term Memory (LSTM) in this paper. Firstly, the monitoring data that can well reflect the engine degradation trend is screened out, and the samples are constructed through a sliding time window. Then, a multi-layer LSTM model is constructed to mine the deep-seated features of the samples for predicting the engine RUL. Finally, the hyperparameters of the multi-layer LSTM model are optimized automatically by the PSO algorithm to optimize the performance of the model. The effectiveness of this method is verified by NASA data set. RMSE, MAE and the scoring function are used as evaluation indexes. RMSE and score of the prediction results are 12.35 and 284.1, respectively. It has higher prediction accuracy compared with traditional deep learning and machine learning methods. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy. [ABSTRACT FROM AUTHOR]

Pulse Detonation Engine has been a point of interest in the propulsion industry for some time and the interest has been rising due to its better output and results. But any system can perform its task efficiently based on the efficiency of its control system. In this paper we have presented a comparative study between multiple control systems- one based on Bluetooth technology, the other based on infrared sensor technology and one based on wired electrical system. For the wireless system, the signal received from either of the media is passed to the various sensors and systems connected through an Arduino board that further controls the solenoid valves and ignition system. Primarily, a control system circuit is developed using Arduino board and different sensors to connect the fuel supply and ignition system. In the next stage, Bluetooth sensors are connected using an android app and then an infrared sensor based system is integrated with the Arduino to control the engine and the performance of the two systems are compared. Whereas for wired system, every sub system is controlled through optical wires including the solenoid valves, the injection system and the sensors. [ABSTRACT FROM AUTHOR]

Turboshaft engines are different from each other due to manufacturing and installation tolerances. Hence, it is difficult to draw out the physical model from the average component maps and design points to represent the performance of the individual engine. The available test-bed data is usually less than the number of correction coefficients to update the maps, and it is the underdetermined state optimization issue. In this paper, we propose a novel reduced-order optimization, namely PSO-EKF algorithm, combined with prior state estimation for non-linear model correction of the turboshaft engine. This method combines the advantages of PSO and extended Kalman filter (EKF). PSO-EKF method converts partial parameters optimized by PSO into parameters directly solved by EKF. The order of optimization space is reduced. Using the correction coefficient function, a stability improvement strategy is designed to ensure the stability of the optimization process. Compared with the GA and PSO algorithms, experimental verification shows that the method has a faster convergence speed and smaller model error than the general PSO. After the performance map is updated by this method, the error of outputs of the individual model is within 1.4 %. [ABSTRACT FROM AUTHOR]

New generations of spark-ignition engines include exhaust gas recirculation (EGR) to improve the engine efficiency. Depending on the design of the EGR routing, some differences in the total amount of recirculated gases that reach each cylinder can be induced. This affects the air-to-fuel ratio on each cylinder due to the combination of the different temperature and composition of the gases at the intake valve closure. As a consequence, significant deviations in the combustion process and the subsequent composition upstream the three-way catalyst can be reached. This paper explores these effects on catalyst performance and tailpipe emissions, individualizing the behavior for each regulated species. The study was performed in a four-cylinder naturally aspirated engine with Atkinson cycle and a close-coupled three-way catalyst. The most significant deterioration in conversion efficiency appeared for the nitrogen oxides, directly linked to the EGR dispersion level. In the case of carbon monoxide (CO) emissions, no significant impact was observed except at high average EGR rates, where one or more of the cylinders exceeded the EGR tolerance for that speed and load. Based on these results, a strategy where the fuel injector command is adapted to correct the air-to-fuel ratio deviations induced by the EGR was developed and implemented. [ABSTRACT FROM AUTHOR]

With escalating environmental standards and the pursuit of energy efficiency, Gasoline Direct Injection (GDI) technology has become crucial for enhancing engine performance and reducing emissions. As a vital component of the engine, the design of the piston ring significantly influences the thermodynamic performance of the engine. This paper systematically studies the thermodynamic impact of piston ring gaps in GDI engines. Initially, through the analysis of heat transfer and piston strength associated with piston ring gaps, the effects on engine thermal efficiency and structural stability are explored. Subsequently, by integrating three different thermal analysis boundary conditions and mechanical load conditions, we assess how these variables affect the piston's thermal state and engine performance. Existing studies, often relying on simplified models or empirical formulas, tend to overlook the complex thermodynamic and mechanical interactions under actual operating conditions, resulting in inadequate predictive accuracy. This research introduces more complex boundary condition simulations, aiming to provide a more precise method for predicting piston performance, thus offering theoretical support and practical guidance for the optimization of GDI engine design. This study not only fills the research gap regarding piston ring gaps but also provides new perspectives and data support for engine design and optimization, contributing to the advancement of efficient, low-emission engine technologies. [ABSTRACT FROM AUTHOR]

This paper presents a Linear Joule Engine Generator (LJEG) powered by ammonia and hydrogen co-combustion to tackle decarbonisation in the electrification of transport propulsion systems. A dynamic model of the LJEG, which integrates mechanics, thermodynamics, and electromagnetics sub-models, as well as detailed combustion chemistry analysis for emissions, is presented. The dynamic model is integrated and validated, and the LJEG performance is optimised for improved performance and reduced emissions. At optimal conditions, the engine could generate 1.96 kWe at a thermal efficiency of 34.3% and an electrical efficiency of 91%. It is found that the electromagnetic force of the linear alternator and heat addition from the external combustor and engine valve timing have the most significant influences on performance, whereas the piston stroke has a lesser impact. The impacts of hydrogen ratio, oxygen concentration, inlet pressure, and equivalence ratio of ammonia-air on nitric oxide (NO) formation and reduction are revealed using a detailed chemical kinetic analysis. Results indicated that rich combustion and elevated pressure are beneficial for NO reduction. The rate of production analysis indicates that the equivalence ratio significantly changes the relative contribution among the critical NO formation and reduction reaction pathways. [ABSTRACT FROM AUTHOR]

Wind propulsion systems (WPS) for commercial ships can be a key ingredient to achieving the IMO green targets. Most WPS installations will operate in conjunction with propellers and marine engines in a hybrid mode, which will affect their performance. The present paper presents the development of a generic, fast, and easy tool to predict the propeller and engine performance variation, along with the cost, as a function of the wind power installed in two operation conditions: fixed ship speed and constant shaft speed. Specific focus is directed toward showing generic trends and trade-offs that inform economic decision-making. To this end, a key feature of the presented work is the ability to assess the cost–benefit of both controllable pitch propellers and fixed pitch propellers (CPPs and FPPs). This provides advice on when, in terms of WPS installation size, it is worthwhile to install which kind of propeller. CPPs are found to be more suitable for newly built wind-powered ships (>70% wind power), while a conventional FPP is satisfactory for wind-assisted ships (<70% wind power) and retrofitted installations. The results for a 91,373 GT bulk carrier showed that a WPS unloads the propeller and the engine, which leads to an increase in the propulsive efficiency and a detrimental rise of the engine specific fuel oil consumption. However, propeller gains are found to be greater than engine losses, which result in extra savings. Thus, not only does a WPS save fuel and corresponding pollutant emissions, but it also increases the entire propulsive efficiency. [ABSTRACT FROM AUTHOR]

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task. [ABSTRACT FROM AUTHOR]

Efficient safeguarding of the security of interconnected devices, which are often resource-constrained, can be achieved through collective remote attestation schemes. However, in existing schemes, the attestation keys are independent of the device configuration, leading to increased requirements for the trusted computing base. This paper introduces a symmetrical aggregate trust attestation that is compatible with devices adhering to the device identifier composition engine framework. The proposed scheme simplifies the trusted computing base requirements by generating an attestation key that is derived from the device configuration. Moreover, the scheme employs distributed aggregate message authentication codes to reduce both the communication volume within the device network and the size of the attestation report, thereby enhancing the aggregation efficiency. In addition, the scheme incorporates interactive authentication to accurately identify compromised devices. [ABSTRACT FROM AUTHOR]

This paper presents the exploration and optimization of a hybridized opposed piston (OP) engine. In this work, the exhaust crankshaft lead (ECL) is introduced as a controllable parameter in the hybridized OP engine enabled by eliminating the conventional geartrain linking the two crankshafts of an OP engine. This allows for variation in the effective compression and expansion ratio of the engine, along with scavenging performance. This novel control actuator as well as the adjustable speed and load setpoint in a series hybrid OP engine powertrain architecture necessitates an intensive calibration effort to realize any possible efficiency improvements. However, the OP engine within this series hybrid powertrain does not need to operate in highly transient conditions, but rather its operating point is fixed or slowly varying. This property permits the use of online calibration techniques. After manually sweeping speed and ECL values at two power setpoints, the use of an extremum-seeking type intercycle optimization algorithm to optimize the operating setpoint is validated, showing that near optimal speed and ECL setpoints can be selected despite the relatively flat operating map of the OP engine. [ABSTRACT FROM AUTHOR]

Architecture-Driven Modernization (ADM) is a special kind of reengineering that employs models along the process. The main ADM metamodel is the Knowledge Discovery Metamodel (KDM), which is a platform-independent metamodel able to represent several views of a system. Although a lot of research is currently focused on the reverse engineering phase of ADM, little has been devoted to the forward engineering one, mainly on the generation of Platform-Specific Models (PSMs) from KDM. The forward engineering phase is essential because it belongs to the end of the horseshoe model, completing the reengineering process. Besides, the lack of research and the absence of tooling support in this phase hinder the industrial adoption of ADM. Therefore, in this paper, we present a process for creating Transformation Engines (TEs) capable of transforming KDM instances in a chosen PSM. We highlight two main contributions in this work. The first is a process that software engineers can follow for building TEs capable of generating PSM instances (e.g., Java model, Python model, etc.) from KDM instances. Having that on their hands, modernization engineers can then use generators for generating language-specific source code from the PSM. The second is delivering a specific TE called RUTE-K2J, which is able to generate Java models from KDM models. The transformation rules of RUTE-K2J have been tested considering sets of common code structures that normally appear when modernizing systems. The test cases have shown that in this version of RUTE the transformation rules are able to correctly generate 92% of the source code submitted to the transformation. [ABSTRACT FROM AUTHOR]

Historical cars are an important part of the cultural heritage of the last 150 years. Their preservation in technical museums raises the question of how to preserve their primary functionality, namely their mobility. This implies being able to reactivate and to maintain their thermal engines, which are the source of their motion. However, the diversity and complexity of these engines generally require the presence of highly qualified personnel as well as detailed condition reports to assist the conservators. This study proposes to use acoustic emission techniques to facilitate these conservation procedures by objectifying the evaluation of the state of the engines and by providing systematic quantitative indicators for their health monitoring. To illustrate the implementation and the potentialities of this approach, different tests have been carried out at the National Automobile Museum of Mulhouse, on a Renault Type AG1. A dedicated experimental setup and the associated measurement protocol are presented in this paper. The derived results show the ability of this method to detect specific types of engine malfunctions, both during bench test and in situ measurement conditions. A critical discussion is finally proposed to highlight the feasibility and the possibilities of such laboratory techniques in the context of conservation assistance. [ABSTRACT FROM AUTHOR]

A lumped-parameter dynamic performance model for a single-spool turbojet engine is presented in this paper. This model can handle pre and poststall transients under forward and reverse-flow conditions. The inter-component volume technique is employed instead of the standard matching technique to be able to handle high-frequency transients and reverse-flow conditions. Inspired by Greitzer's lumped-parameter surge model, momentum (duct) and volume elements are placed within the flow path to handle surge dynamics. Compressor and turbine maps are extended to low-flow and reverse-flow regions using a combination of the guidelines presented by Kurzke, the cubic axisymmetric characteristics of Moore and Greitzer, and a quadratic function guess for in-stall characteristics. Combustor efficiency, stability limits, and delay are taken from the literature. Poststall behavior of the model is validated using the data available in the literature for a Rolls-Royce Viper engine. A good match is observed with a correct prediction of poststall behaviors, which transition from surge after locked stall to multiple surge cycles around 80% speed and multiple surge cycles to surge after flameout around 95% speed. The effects of different modeling choices and modeling parameters on the obtained results are discussed. The produced model can be calibrated for a specific engine with surge tests, and it can be used for hard-to-test scenarios like surge after shaft breakage. Different surge/stall-causing events, such as fuel spiking, in-bleeding, and shaft breakage, are simulated to see the capabilities of the model. [ABSTRACT FROM AUTHOR]

As a key component of high‐performance switches and routers, the packet forwarding engine (PFE) is mainly responsible for selecting the appropriate output port for tens of thousands of packets within an extremely short time frame. However, the performance of PFE is determined by the selected group membership algorithm. This paper puts forth a hybrid strategy–caching scalar‐pair and vectors routing and forwarding (CSVRF), consisting of virtual output port bitmap caching (VOPBC) and fractional‐N SVRF to address major multicast forwarding issues such as scalability by using content addressable memory. In CSVRF, a virtual output port bitmap cache is introduced, which includes the most popular combinations of output port bitmap and divides the big scalar‐pair into N sub‐groups to achieve parallel compute and the reusability of less bit‐length prime. The results demonstrate that the memory space and the forwarding latency are effectively reduced compared with previous work. In space efficiency, it only required 10% memory space compared with the original SVRF/fractional‐N SVRF, decreased 10% memory usage compared with pure VOPBC and nearly improved 1 to 4 orders of magnitude of packet processing time compared with the original SVRF and the fractional‐N SVRF respectively. [ABSTRACT FROM AUTHOR]

Most hydraulic excavators use passive power control to adjust the engine speed according to the load torque to achieve power matching, which has very limited effectiveness in energy saving. In this paper, an active power optimization control of speed closed-loop engine is proposed to improve its tracking performance for the dynamic energy-saving operating points. The inertia movement of the engine during variable speed control is analyzed to determine the kinetic energy requirements for different start and end operating points. An RBF-based neural network controller is designed based on power optimization to actively compensate the strong hysteresis of the engine speed relative to the optimal torque. The simulation and the experimental results show that the proposed control method has a much faster response for the energy-saving operating points, which reduces the energy consumption by 9.28% and 5.56% without adding any energy storage devices to the hydraulic excavator. [ABSTRACT FROM AUTHOR]

• The novel preignition event (PEMC) was explored on HDI engines. • PEMC event occurred in the early/mid compression stroke and with the peak pressure at TDC. • PEMC events worsened NOx emissions, BP, and BTE in most cases. • Delaying injection timing beyond 180° CA BTDC could totally prevent PEMC events. • The established RF prediction model has excellent robustness and reliability. The potential use of hydrogen in automotive internal combustion engines could aid in the energy transition and contribute to achieving carbon neutrality. This paper has investigated the impact of operating parameters boundary for a novel preignition event on a 2.0L hydrogen direct injection (HDI) engine to address the challenge of abnormal combustion limiting the application of HDI engines. The identified characteristics of the event and its potential harm to the engine has been summarized. Experiments have shown that the novel preignition event predominantly occurred mainly during 170−80°CA BTDC, hence the name PEMC (preignition in the early/mid compression stroke) event, and caused a surge in cylinder pressure. In 77.2 % of the selected cases, the PEMC events resulted in increased NOx emissions. In 93.1 % and 91.5 % of the cases, there was a decrease in brake power and brake thermal efficiency, respectively. Unlike the low speed, high load preignition of gasoline engines, PEMC events were more likely to occur in the torque range of 70–100 Nm and could be monitored in a lean mixture environment with λ = 1.5–3.3. PEMC events were found in the interval of 180−320° CA BTDC at injection timing and were most intense at 240−250° CA BTDC. To prevent PEMC events completely, it is recommended to delay the injection timing beyond 180° CA BTDC. The paper has employed a model based on experimental data and Random Forest algorithms to predict PEMC events. The model demonstrated an accuracy of 98.76 % and 97.69 % for the training and test data, respectively. It has been effective in predicting the occurrence of PEMC events under various operating conditions. The paper has offered an alternative solution for the novel preignition in HDI engines. [ABSTRACT FROM AUTHOR]

To meet the requirements of the update for the supersonic landing and take-off (LTO) cycle standard, a methodology for studying the LTO supersonic standard based on pollutant emission characteristics is proposed in this paper to analyze the thrust and time of the LTO. In this study, three distinct supersonic engine models were developed based on subsonic engine cores. Subsequently, the pollutant emission calculation models were developed to calculate the pollutant emission index (I e) of supersonic engines and analyze the pollutant emission characteristics. Findings confirmed the presence of a correlation between standard formulations and pollutant emissions. Finally, considering that the current LTO standard leads to excessive noise during the climb phase and inefficient combustion during the taxi/ground idle phase, the LTO supersonic standard was analyzed for the trade-off, which resulted in the more applicable and fairer standard being formulated. The results indicate that the LTO supersonic standard formulated in this paper proves to be more reasonable than the current LTO standard and accurately represents the pollutant emission characteristics of distinct supersonic engines during both the climb and taxi/ground idle phases. Consequently, this study's results have guiding significance for formulating the future LTO supersonic standard and predicting pollutant emissions from supersonic engines. • Outdated supersonic landing and take-off cycle standards are updated. • A trade-off analysis of the supersonic standard is developed. • More comprehensive supersonic engine models and supersonic emission calculation methods are developed. • The updated supersonic climb standard more closely approximates the emissions from the noise-reducing climb trajectory. • The updated supersonic taxi/ground idle standard is better able to meet the combustion efficiency requirements. [ABSTRACT FROM AUTHOR]

The layout design of ship engine room belongs to the multi-objective optimization design problem in confined space. This paper proposes an intelligent three-dimensional (3D) layout optimization design method of ship engine room based on the combination of knowledge-based engineering (KBE) and intelligent optimization algorithm. Firstly, the initial 3D layout scheme of engine room equipment is automatically obtained by the KBE method including knowledge acquisition, knowledge representation and knowledge reasoning. Then the multi-objective optimization of ship engine room layout is performed by the simulated annealing algorithm (SAA) to acquire the optimal design scheme. The classification rules of ship engine room are established to improve the efficiency of knowledge acquisition. The rule of adding the virtual area is proposed to control the equipment spacing. The parametric assembly is used to complete knowledge reasoning. Three expert verification rules including heeling moment, interference inspection and escape time are established for the generated layout scheme. Finally, a case study result verifies the feasibility and effectiveness of the proposed method. The visualization and automation of the 3D layout for ship engine room are successfully realized. • This paper proposes an intelligent 3D layout optimization design method for ship engine room based on KBE. • An optimization model for ship engine room is established, -with increased virtual areas to control equipment spacing. • Three optimization sub-objectives are established, combined with simulated annealing algorithm (SAA) to solve. [ABSTRACT FROM AUTHOR]

• Assessment of long-term testing in the selection of renewable oxygenated fuels for engines. • Evaluating fuel performance over extended durations, ensuring engine compatibility. • Holistic review covers the long-term effects of various oxygenated fuels on engine deposits, wear, and lubricating oil. • Comprehensive assessment of fuel performance with both fixed-bed laboratory engines and field/road vehicle engine testing. Long-term testing is crucial for evaluating renewable and non-carbon fuels for engines, ensuring compatibility and assessing their performance for extended periods. This is especially pertinent as researchers explore the addition of higher ethanol, hydrogen and ammonia content in gasoline as potential fuel additives. The paper extensively reviews the long-term effects of various fuels on engine deposits, wear, and lubricating oil. Studies indicate that biofuel blends can pose challenges in the long run, leading to increased deposits, engine wear, and lubricating oil degradation. However, some studies highlight the favorable impact of biofuel blends, due to their oxygenated nature. For instance, deposit formation in diesel-fuelled constant-speed engines was significantly lower than in JO5 and JO10-fuelled engines (512 h duration). Similarly, KB20-fuelled engines exhibited approximately 15.3 % higher deposit formation than diesel-fuelled engines (274 h duration). The oxygen content and superior lubrication properties of biodiesel blended fuels help in better combustion process and reduce engine deposits. The deposit formation on the fuel-injector affects the fuel pattern and engine performance. The performance and combustion characteristics degrade and emissions increase with time. The application of biodiesel observed to enhance corrosive behavior due to inherent oxygen, leads to increased wear and friction in engine components and fuel pump plunger. The formation of metal debris increases and lubrication oil degrades with respect engine operation time. This study provides insights into the long-term impact of various renewable biofuels and their blends on engine performance and discusses future prospects on engine tribological characteristics. [ABSTRACT FROM AUTHOR]

The Joule cycle Linear Engine Generator (LEG) is a promising power generation technology with the potential to achieve zero carbon emissions. However, the LEG expander valve actuation system presents unique challenges due to its lack of a traditional crankshaft, the need for swift valve lift and reversal, and variable lift. This paper presents a Linear Variable Valve Actuation (LVVA) system for a LEG prototype. The LVVA system is powered by voice coil motors. Rigorous experimental investigations were conducted to analyze crucial performance factors, including energy consumption, force balance, energy flow distribution, and the relationship between valve lift duration and energy consumption. The results show that the LVVA system can achieve the desired valve lift and timing, as well as very small variations in LEG performance compared to the model using an ideal lift curve. The LVVA accounts for approximately 3.59 % of the LEG power output. The energy consumption of 1.607 J per valve stroke provides a slight advantage over traditional actuation systems. The obtained optimal lift curves were used to refine the LEG model. The influence of valve lift curves on LEG performance was evaluated which reveals rapid valve openings and relatively short duration contributing to improved LEG performance. [Display omitted] • Valve actuation validation contributes to a 1D Linear Engine model. • The valve actuation system consumes about 1.607 J per valve lift cycle. • Valve openings durations improves energy consumption of valves and engine performance. • The valve actuation energy demands 3.59 % of Linear Engine's power output. [ABSTRACT FROM AUTHOR]

Compared with the conventional turbofan engine, the adaptive cycle engine (ACE) has more extensive flight envelope and the more types and number of adjustable components. While the performance of each mission profile is improved adaptively, the controller design of profile switching process will face strong flight condition disturbance and internal uncertainty. The traditional control system's single loop controller and discrete switching method of control parameters are to some extent difficult to meet the adaptive and robustness requirements of ACE. Thus, this paper proposed a multi-variable decoupling anti-disturbance controller for ACE based on the results of flight envelope partitioning with similar distances, embedded a state-dependent switching law. By analyzing the impact of inlet disturbances, internal actuator actuation, and random degradation of component performance on different switching laws, the adjust time and overshoot of the controller with state-dependent switching law proposed in this paper are reduced by an average of about 11.91 % and about 9.98 %, and the average thrust linearity and robustness has increased by about 0.096 % and 20.02 %. Finally, the reliability and feasibility of the control system were verified based on a hydraulic driven semi-physical experimental platform, providing reference for more complex and dynamic engine operation control in the future. • Consider external and internal disturbances during the ACE mission profile switching. • A multivariable ADRC controller with state-dependent switching law for ACE is proposed. • The controller robustness was analyzed through stochastic perturbation simulation. • The system reliability was verified based on RVABI semi-physical experimental platform. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

Rotating detonation engines (RDEs) are pressure-gain combustion devices which allow for a compact and mechanically simple engine design with improved thermodynamic efficiency compared to traditional deflagration devices. However, achieving these gains in practical devices that are operated with non-premixed reactants requires overcoming non-idealities in the mixing and combustion process. We investigate these non-ideal effects in the Air Force Research Laboratory's rotating detonation rocket engine using large-eddy simulation. The simulation results are compared against experimental measurements, showing overall reasonable agreement for critical performance metrics of thrust, specific impulse, and chamber pressure. A wavenumber-domain filtering method is presented to separate the clockwise-rotating waves and counter-clockwise waves, which enables the unambiguous identification of secondary weak waves and their interaction with detonation waves. The analysis of the flow field and response of the injectors shows that these interactions lead to precessing regions of higher pressure, characterized by less parasitic combustion, higher heat release rate, and higher injector blockage. Furthermore, the thrust contributed by the pressure gain combustion process during these interactions is significantly increased, which leads to a modulation of the overall thrust output of the engine. Novelty and significance statement This study analyzes non-ideal effects inherent to rotating detonation rocket engines (RDRE), which prevent achieving theoretically higher thermodynamic efficiencies compared to traditional deflagration devices. This manuscript has three major novel contributions: First, we assess the predictive accuracy of our large-eddy simulation approach in predicting the combustion performance of an RDRE at high-mass flow conditions, which is a benchmark case of the Model Validation for Propulsion workshop. As such, it contributes to the literature in identifying capabilities and deficiencies of current simulation tools in predicting such complex combustor configurations. Second, we extend the method of Bennewitz et al. [AIAA Paper 2018-4688, 2018] and introduce a wavenumber domain filtering method as an efficient approach for separating clockwise and counter-clockwise rotating features in RDREs. This analysis tool is significantly simpler than other methods found in the literature, and enables a detailed analysis of detonation and secondary combustion modes. Third, this study contributes new knowledge about the role of secondary waves in RDREs. Specifically, the effects of these waves on the flow field and injector response are analyzed, showing that these effects are related to the overall thrust of the engine, which is the first time to the authors' knowledge. [ABSTRACT FROM AUTHOR]

Convolution is a fundamental and computationally expensive primitive and finds ubiquitous in deep neural networks (DNNs). The evolving DNNs have spurred the emergence of numerous accelerators and they successfully achieve high throughput. However, for DNN inference with small batch sizes, the computational resources of the accelerators are often under-utilized, and the overhead of offloading is significant. Compared to accelerators, the CPU can better meet fast response requirements of inference, flexibly handle various models, and is suitable for various scenarios (from edge to data center). Therefore, CPU remains an attractive platform for DNN inference, despite the sub-optimal performance, and resource efficiency. In this paper, we propose CUTE, a scalable CPU-centric and ultra-utilized tensor engine for convolutions. It co-designs data flow and hardware architecture to leverage the data reuse and parallelism of convolutions. CUTE is composed of several small tensor elements (TEs) and two-level buffers. It employs a decoupled access-execution architecture and greedy strategy to feed data to TEs, enabling it to achieve ultra utilization and great scalability. CUTE is tightly coupled with the CPU to minimize offloading latency, thereby providing efficient convolution computing capabilities for the system. Experimental results show that under the same bandwidth, CUTE achieves an average performance improvement of 3.8x compared with the CPU AVX512 unit and 1.6x compared with the CPU AMX unit. Besides, CUTE achieves a speedup of 7.0x and 3.9x over Nvidia V100 GPU and Eyeriss accelerator respectively, due to higher utilization of computing units. [ABSTRACT FROM AUTHOR]

Today's internal combustion engines are subject to strict regulations of pollutant emissions and to ambitious fuel consumption reduction targets. For direct-injection compression-ignition engines, multiple injections offer additional degrees of freedom that help to achieve these goals. Thus far, the calibration of multiple injections has been based on data-driven approaches using design of experiments. However, an ever increasing number of actuators causes the calibration of the engine to require much more time. In this paper, we propose a model-based calibration method for multiple injections. The model, which is based on first principles, is identified by a low number of measurements and extrapolates well to various operating points. An optimal control problem which includes that model is then formulated to calculate the efficiency-optimal injector inputs while respecting constraints on cylinder pressure, pressure gradient, engine-out temperature, and engine-out NOx emissions. The optimal control problem is implemented on a rapid prototyping control system and is solved with the nonlinear optimal control framework acados. Due to the simple zero-dimensional formulation of the model and the fast convergence of the solver, the method allows for a fast calibration of two injections for an engine at steady-state operation. The method is used at various engine operating points to find various trade-offs of relevant engine-out quantities. An experimental validation shows that the calibration time is reduced significantly compared to an approach based on a design of experiments. When compared to a full-factorial design of experiment, a reduction in measurements of up to 60% is possible. • We propose a model-based calibration method for multiple injections in a CI engine. • We use nonlinear optimal control to calculate efficiency-optimal solutions. • New solutions are computed within 50 ms on a rapid prototyping hardware. • The new method is validated on an engine test bench. • A significant reduction in calibration time is achieved with the new method. [ABSTRACT FROM AUTHOR]

• A MPNN based graph network is proposed to learn the rules of the physical system behind the data. • The pre-trained model has the ability of forward prediction by considering the predicted value as input. • The crack propagation prediction under loading condition is realized by introducing edge updating module. In this paper, the message passage neural networks (MPNN) based graph networks is proposed for learning and prediction of boundary loading problem in solid mechanics. As a learnable physics engine, the proposed model is designed to learn from historical data to understand the rules of the physical system behind the data, and then to make predictions. By learning the historical displacement field sampling data of 2D intact plate, the proposed model successfully realizes its continuous displacement field prediction. On this basis, the trained model also achieves a good prediction in a plate containing holes under the same loading condition but without training, which verifies the generalization ability of the proposed model. Finally, the crack propagation can be realized by learning the historical data of the plate with prefabricated cracks subjected to tension, which shows the excellent predict ability and exciting prospect of the proposed model. [ABSTRACT FROM AUTHOR]

In recent years, hardware sorters have been an attracted topic for researchers. Since hardware sorters play a crucial role in embedded systems, several attempts have been made to efficiently design and implement these sorters. Previous state-of-the-art hardware sorters are not suitable for embedded edge computing devices because they (1) consume high power, (2) occupy high area, (3) work for limited data-width numbers, (4) require many memory resources, and (5) finally, their architecture is not scalable with the number of input records. This paper proposes a hardware sorter for edge devices with limited hardware resources. The proposed hardware sorter, called Edge-Sorter, processes 4 bits of input records at each clock cycle. Edge-Sorter utilizes the unary processing in its main processing core. Edge-Sorter has valuable attributes compared to previous state-of-the-art techniques, including low power consumption, low area occupation, sorting numbers without storing their indices, sorting numbers with arbitrary data-width, and scalable with the number of input records. The proposed approach is evaluated and compared with previous state-of-the-art techniques with two different implementation and synthesis environments: Xilinx Vivado FPGA-based and Synopsys Design Compiler 45-nm ASIC-based. The Synthesis results of both environments indicate that both Edge-Sorter techniques reduces area and power consumption on average by 80% and 90%, respectively compared to previous techniques. [ABSTRACT FROM AUTHOR]

The ejector mode in rocket-based combined-cycle (RBCC) engines involves complex mixing and combustion processes between the primary and secondary streams and second fuel, which poses a challenge to numerical simulation. This paper introduces an efficient model to predict RBCC engine performance in ejector mode. Given the slow growth rate of compressible mixing layers with strong compressibility effects and high chemical reactivity of immediate species within the primary stream, the combustion intensity is primarily governed by the mixing process. The model converts residual chemical energy within the primary stream into suitable mass flow rates of H 2 and CO and simplifies the intricate chemical kinetics into single-step models. The model was tested on hydrogen- and kerosene-fueled RBCC engine experiments involving diffusion and afterburning (DAB) and simultaneous mixing and combustion cycles. Simulation results align well with experimental data regarding wall pressure distributions, entrainment ratio, and axial thrust. The model suggests intense combustion occurs in both the mixer and afterburner, even in the DAB cycle, which notably reduces the entrainment ratio. Additionally, early combustion within the mixing process incurs extra Rayleigh loss and impairs pressurization performance. The secondary fuel exhibits a jet-wake stabilized combustion mode under a high-temperature and low Mach number environment. In conclusion, the proposed model provides an efficient way of predicting RBCC engine performance in the ejector mode. [ABSTRACT FROM AUTHOR]

No matter in traditional hydraulic mechanical or newly numerical controlling system, pressure differential valve (PDV) always plays an important role in main fuel oil system and thrust augmentation fuel system. A throughout study on PDV is required to gain a more precise control and effective design for aero engine. Therefore, this paper presents a PDV and establishes the mathematical model to analyze its working characteristics, which shows that PDV performance is greatly affected by return oil inlet orifice and spring stiffness. The simulation and experiment are carried out to analyze the effect of PDV performance on system outlet volume flow rate and their results agree well with a maximum error of approximately 5 %. According to the study, it is found that there is a maximum increase of 9.99 % in system volume flow rate with pump rotating velocity from 300 to 900 rpm, but a maximum decrease of 3.4 % with outlet load pressure from 0.5 to 1.6 MPa. In addition, calculated curve slope reduces with return oil inlet orifice number increase and spring stiffness decrease, which means that PDV shows a faster response to working condition change and a better dynamic characteristic in controlling process. It is excepted that the research can be helpful in a further understanding of aero engine controlling system and a meaningful instruction on system optimization as well as fuel efficiency improvement. • A constant pressure differential valve and its controlling characteristics for aero engine is presented. • A maximum error of approximately 5% between simulation and experiment is gained. • System volume flow rate increases with pump rotating velocity increase but outlet load pressure decrease. • Large number of return oil inlet orifice and small spring stiffness is suggested for well PDV controlling quality. [ABSTRACT FROM AUTHOR]