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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]

Bird strikes pose one of the most significant threats to aviation safety, often leading to substantial loss of life and economic damage. Many bird strike incidents involve multiple birds. However, in previous bird strike studies, the problem of multiple bird strikes has often been neglected. In this paper, the bird slicing process of a rotating engine fan is examined, and a probability model is introduced to assess the risk of multiple impacts on the fan blades. In addition, this paper utilized an implicit–explicit calculation method. The parameters of blade root stress, tip displacement, plastic deformation, and energy were selected to investigate the effects of the time interval and strike position of a bird strike on the dynamic response of and damage to the blades. The results indicated that the position of bird strikes has a more pronounced effect on blade damage compared to the time interval between impacts. Damage to a blade is most severe when the blade root is struck multiple times. Multiple bird strikes may not always lead to a significant increase in maximum blade tip displacement, and may even have a dampening effect. [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]

The paper presents an analysis of the dynamic parameters of a compact class passenger car powered by LPG/DME blends. The presented results are part of the research cycle of this vehicle, the purpose of which was to check the possibility of using DME (dimethyl ether) as an additive in the fuel mixture with LPG. In the presented part of the experimental research, the acceleration times of the vehicle under specific conditions were measured. On the basis of the obtained results, the relations between the average acceleration in the tested speed ranges, the fuel composition, and the degree of engine load were developed. The results of the analysis indicate that in the examined range of changes in the DME share in the fuel, comparable or higher acceleration values were obtained for all engine load levels. This confirms the usefulness of DME as a fuel component used to power SI engines. [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]

Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness. [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]

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]

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition. [ABSTRACT FROM AUTHOR]

This paper presents an experimental and numerical evaluation of the pre-chamber induced HCCI combustion concept (PC-HCCI) in terms of engine performance, emissions, and controllability. In this concept, a spark-initiated combustion in the pre-chamber is utilized to trigger the kinetically controlled combustion of an ultra-lean mixture in the main combustion chamber. The experimental measurements were performed on a single-cylinder engine with a custom-made active pre-chamber. A high compression ratio of 17.5 was used, which limits the maximum achievable engine load due to high knocking tendency but enables both standard PCSI combustion (flame propagation) at very high dilution levels and HCCI combustion at reasonable intake temperatures. The analysis of combustion characteristics and the resulting performance is performed at indicated mean effective pressures (IMEPs) of 3.5 and 3.0 bars, and three different intake temperatures of 80 °C, 90 °C, and 100 °C. The variation in engine load was achieved by adjusting the excess air ratio in the main chamber. On each combination of intake temperature and engine load, a spark sweep and an injected PC fuel mass sweep were performed to obtain the highest indicated efficiency while satisfying the restrictions in terms of combustion stability and knock intensity. It was shown that, unlike in a conventional HCCI engine, the combustion phasing can be directly and reliably controlled by adjusting either spark timing or the reactivity of the pre-chamber mixture, ensuring adequate combustion stability and eliminating potential misfires. A similar indicated efficiency as with conventional HCCI combustion was obtained, while the NOx emissions, although slightly elevated, are still insignificant. Compared to PCSI combustion at the same engine load, a 4-percentage-point increase in indicated efficiency and two times lower NOx emissions were achieved. Compared to the most efficient PCSI operating point, it was 1 percentage point lower, indicating that efficiency was achieved, but the specific NOx emissions are reduced by approximately 70%. Most importantly, very similar performance was obtained with significant variations in intake temperature, proving the reliability and adaptability of this combustion concept. [ABSTRACT FROM AUTHOR]

The outcomes of research regarding pyrolysis oils obtained from waste sources (WPO) used to power a compression-ignition engine have been presented in this paper. Oils obtained in an industrial process based on polypropylene (PPO), polystyrene (PSO) and used car tires (TPO) were used. Prior to conducting engine tests, a in-depth examination of the tested fuels parameters was undertaken. An advanced single-cylinder research engine utilizing split fuel injection technique was used for the tests. Emission analysis was performed using multi-compound FTIR analytical system. The WPO were blended with diesel fuel in proportions of 20%, on the mass basis and tested at middle engine load and variable EGR rates. Tests have shown that modern combustion systems compliant with the Tier 4/Stage IV standard with multi-pulse injection can handle fuels with a WPO content of 20% without the need for recalibration. The addition of PPO did not significantly affect the emission, while mixing with PSO resulted in elevated levels of hydrocarbon and carbon monoxide emissions. Regarding to the mixture with TPO, increased levels of particulate matter, sulfur oxides, aromatic compounds and formic acid were observed. [ABSTRACT FROM AUTHOR]

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 ( I T T M ) to track engine deterioration to determine the extent of an in-service engine life. The proposed model was used to assess the I T T M 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 I T T M 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. [ABSTRACT FROM AUTHOR]

The advent of Generative Artificial Intelligence is opening essential questions about whether and when AI will replace human abilities in accomplishing everyday tasks. This issue is particularly true in the domain of software development, where generative AI seems to have strong skills in solving coding problems and generating software source code. In this paper, an empirical evaluation of AI-generated source code is performed: three complex coding problems (selected from the exams for the Java Programming course at the University of Insubria) are prompted to three different Large Language Model (LLM) Engines, and the generated code is evaluated in its correctness and quality by means of human-implemented test suites and quality metrics. The experimentation shows that the three evaluated LLM engines are able to solve the three exams but with the constant supervision of software experts in performing these tasks. Currently, LLM engines need human-expert support to produce running code that is of good quality. [ABSTRACT FROM AUTHOR]

For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed. [ABSTRACT FROM AUTHOR]

Data from the steady-state operation of gas turbine engines are used in gas path diagnostic procedures. A method to identify steady-state operation is thus required. This paper initially explains and demonstrates the factors that cause a deviation in engine health when transient data are used for diagnosis and shows that there is a threshold in the slope of time traces, below which the variation in engine health parameters is acceptable. A methodology for deriving a criterion for steady-state operation based on actual flight data is then presented. The slope of the exhaust gas temperature variation with time and the size of its time-series window, from which this slope is determined, are the required parameters that must be specified when applying this criterion. It is found that the values of these parameters must be selected so that a sufficient number of steady-state points are available without compromising the accuracy of the diagnostic procedure. [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]

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]

This paper introduces a Python framework for developing Deep Reinforcement Learning (DRL) in an open-source Godot game engine to tackle sim-to-real research. A framework was designed to communicate and interface with the Godot game engine to perform the DRL. With the Godot game engine, users will be able to set up their environment while defining the constraints, motion, interactive objects, and actions to be performed. The framework interfaces with the Godot game engine to perform defined actions. It can be further extended to perform domain randomization and enhance overall learning by increasing the complexity of the environment. Unlike other proprietary physics or game engines, Godot provides extensive developmental freedom under an open-source licence. By incorporating Godot's built-in powerful node-based environment system, flexible user interface, and the proposed Python framework, developers can extend its features to develop deep learning applications. Research performed on Sim2Real using this framework has provided great insight into the factors that affect the gap in reality. It also demonstrated the effectiveness of this framework in Sim2Real applications and research. [ABSTRACT FROM AUTHOR]

In recent years, the use of fixed-wing Unmanned Aerial Vehicles (UAVs) has expanded, and the use of fixed-wing UAVs is expected to expand due to their usefulness for long-range operations. Different from manned aircraft, no provision is required regarding climb angle at takeoff for fixed-wing UAVs. Therefore, fixed-wing UAVs can take off by taking advantage of their performance. In addition, propeller engines are the propulsion device currently used by most fixed-wing UAVs. However, the thrust force generated by a propeller engine decreases as its airspeed increases. In such circumstances, this paper describes how to derive a maximum rate of climb in which the characteristics of the propeller engine are taken into account, with the aim of reducing takeoff time by maximizing the rate of climb during takeoff. The derivation uses optimization problems with a dependency of the thrust force on the airspeed. After the derivation of the maximum rate of climb, we first checked whether the maximum rate of climb obtained for the mass system was feasible for takeoff at the rate of climb by using a 6-DOF flight simulation, and then confirmed its validity through flight experiments. [ABSTRACT FROM AUTHOR]

The present work aims at showing the didactic potential of the novel Zweinhalb Störche: Roman einer Kindheit in Siebenbürgen, written in 2008 by the German-Romanian author Claudiu M. Florian, for multilingual learning in GFL classes. The paper focuses on the possible use of corpus-based learning tasks and consists of two parts: the first will introduce the main features of multilingual didactics and multilingual competence and the possible teaching approaches of inter- and transcultural literature through the use of corpora; the second will focus on the use of the novel to promote multilingual competence within the GFL classroom. In particular, specific tasks will be suggested to achieve a didactic surplus through the use of corpus compilation and analysis tools such as Sketch Engine. [ABSTRACT FROM AUTHOR]

This study explores ground-breaking methods for improving engine efficiency by combining cutting-edge materials, theoretical frameworks, and alternative energy paradigms. The paper primarily offers a cohesive framework, built from our variational method which combines thermal and entropic engines. We investigate the fabrication of hydrophobic and other functionally specific surfaces using nanomaterials and sophisticated surface engineering techniques that efficiently utilize entropy gradient forces. Additionally, this publication explores the fields of quantum-based propulsion systems and information-burning engines, creating a connecting link between theoretical foundations and real-world technical implementations. The study emphasizes the multifaceted character of engine research and its crucial role in shaping a future in which sustainability and efficiency are intimately connected. [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]

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]

In response to challenges in traditional offline experiments in optical physics, such as high trial-and-error costs, expensive equipment, limited design parameters, and restricted explanations of instrument structures and principles, this paper proposes a design method for an optical physics virtual simulation system based on Unreal Engine 5. The method employs a generic design architecture to partition system units and utilizes technologies like pixel streaming, rendering engines, and physics engines to implement the design of the virtual simulation system. This system covers the entire experimental process, including video learning, instrument adjustment, phenomenon observation, data measurement and recording, and grade inquiry. The proposed design method enhances the immersive experience of experiments, reduces the hardware performance requirements for users, and allows access and utilization across multiple platforms. It efficiently facilitates physics teaching through simulated experiments, thereby effectively improving the quality of experimental teaching. Finally, through a comparison with existing virtual simulation systems, it is demonstrated that the system can save approximately 94.76% of the average CPU usage and provide better immersion and user experience advantages. [ABSTRACT FROM AUTHOR]

With the rapid advancement of manufacturing science and technology, the associated measurement technology has also been transformed. Traditional manual and single-instrument detection modes are unable to meet the requirements of fast beat, high precision, and online measurement in intelligent manufacturing. As connecting rods are typical and key components for automobile engines, their precision performance highly impacts the ultimate quality of engine assemblies. The online measurement of connecting rods was studied in this paper. According to the structural characteristics and parameter requirements of the connecting rods, an online multi-station measurement platform was designed and developed to measure, mark, and classify the measurements of the connecting rods in an automatic assembly line. Among these measurements, two significant parameters, the weight and the barycenter, were focused on in our work. A multi-point balance method was developed and applied to obtain the weights of the big and small ends as well as the position of the barycenter. An approach for weight sensor calibration and measurement data processing was also proposed. Finally, automatic and online measurement platforms were built for experimental verification. The results show that the measurement beats of the weight and the barycenter can reach 3 s/piece. The measured data were compared with a high-precision balance, and the average error of the connecting rod's weight was 0.27%. The on-site application verification further proved the effectiveness and efficiency of our proposed measurement methods and machine, which enables fast and high-precision online measurement of connecting rods. [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]

Abstract Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

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]

Engine mounts serve three primary purposes: (1) to support the weight of the engine, (2) to lessen the transmitted engine disturbance forces to the vehicle structure/chassis or airplane fuselage, and (3) to limit the engine motion brought on by shock excitations. The engine mount's stiffness must be high to control large engine motions and low to control chassis or vehicle body vibration. When hydraulic engine mounts are used, a device called a decoupler creates the dual stiffness requirement. However, numerous investigations have shown that the decoupler has the potential to rotate within its cage bound and become stuck or sink and obstruct fluid flow between the fluid chambers due to a density mismatch between the decoupler and the working fluid. In addition, most hydraulic engine mounts with a decoupler no longer act as vibration isolators but as hydraulic dampers. This study suggests a new amplitude-sensitive hydraulic engine mount design without a decoupler, where the vibration isolation of the engine mount is retained and there is a 75% reduction in the peak frequency, which further enhances the engine mount's capabilities in comparison to the current hydraulic engine mounts with a decoupler. The new design concept and its mathematical model and simulation results will be presented. [ABSTRACT FROM AUTHOR]

Durability and reliability have been studied for decades through intensive trial-error experimentation. However, there are numerous fields of application where the costs associated with this approach are not acceptable. In lubricated machines with severe dynamic loads, such as high-power-density engines, simulation tools offer clear advantages over intensive testing. Prototypes and multiple scenarios can be cost-effectively simulated to assess different lubricants and engine configurations. The work presented here details the study of wear based on a validated elastohydrodynamic (EHD) simulation model of the connecting rod journal bearing. This model accounts for elastic deformation through a connecting rod finite element model (FEM). In addition, multiple lubricant rheological and tribological dependences, determined by specific experimental tests, are applied in the model through their interaction with the simulation software. Correspondingly, a novel wear algorithm is proposed to predict wear depth over time evolution along a proposed wear cycle based on the typical working ranges of high-performance engines. A final assessment is presented to compare 4 different ultralow-viscosity lubricants in their protective performance under severe conditions. The results show the evolution of the wear load and wear depth over the wear cycle. This evaluation is key to describing a lubricant selection procedure for high-power-density engines. [ABSTRACT FROM AUTHOR]

The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies. [ABSTRACT FROM AUTHOR]

This study addresses the vital role of engine dampers in reducing vibrations and enhancing ride comfort in quad bikes. Utilizing ANSYS modal analysis and CATIA V5 design, the research aims to optimize damper design by assessing natural frequencies and potential resonance in the cage structure and engine. Through extensive analysis, the study evaluates vibrations transmitted from the engine to the chassis, crucial for understanding and improving overall vehicle performance. Neoprene rubber emerges as the optimal material for vibration dampers, offering superior density, elasticity, stiffness, and damping coefficient. Comparative analysis reveals that Design 3 of the dampers significantly outperforms Design 2 and Design 1, showcasing a remarkable 62% reduction in vibration. However, the study underscores the influence of various factors, such as application specificity and vibration characteristics, on damper effectiveness. Thus, while Design 3 may excel under certain conditions, careful consideration of diverse a variable is essential for optimal damper selection and performance across different engine types and operational contexts. [ABSTRACT FROM AUTHOR]

A comprehensive thermodynamic model of the marine diesel engine in combination with the operating cost assessment is used in the decision-making process regarding the selection of the most favorable slow steaming speed. The influence of the number of cylinders and sailing speed on exhaust emissions, fuel consumption and operating costs is analyzed for the case of a containership sailing on a Trans-Pacific route. The engine simulation model was used for the calculation of engine fuel consumption, NOX and soot emissions. The operating costs and annual income were calculated through a fuel consumption correlation. The benefit of slow steaming is shown through the comparison of calculated data with the data calculated for the six-cylinder engine and the design speed of 23 knots. The highest reduction of 67.2% in CO2 and 93.3% in NOX emissions is achieved with the seven-cylinder engine at 15 knots, but the six-cylinder engine yields the highest increase in income per route of 6.2%. To comply with the proposed regulations for GHG emissions, the sailing speed should be reduced by at least 26%, which results in a decrease in the annual income by 24% compared to the design speed. [ABSTRACT FROM AUTHOR]

Ammonia can be considered a relevant compound in the future energy sector, playing a significant role as an energy carrier, storage, or carbon-free fuel. However, the production of this molecule has a high energy demand, and the use of natural gas, which is not free of controversy due to the accidental leakage into the atmosphere produced during extraction and the fact that it is a nonrenewable source, contributes to increasing greenhouse gas emissions. Reducing the process's energy demand and carbon footprint will be essential to making ammonia a clear alternative for a carbon-free economy. Given the vast research in ammonia production and handling, this gas seems to be the logical step forward in the evolution of the energy sector. However, the current uncertainty in the global market requires cautiousness in decision making. Several factors may impact economic growth and human welfare, thus needing a careful assessment before making any transcendental decisions that could affect worldwide energy prices and raw material availability. [ABSTRACT FROM AUTHOR]

A pulsed detonation chamber (PDC) equipped with Hartmann–Sprenger resonators has been designed and tested for both Hydrogen/air and Hydrogen/Oxygen mixtures. A full-factorial experimental campaign employing four factors with four levels each has been carried out for both mixtures. Instantaneous static pressure has been measured at two locations on the exhaust pipe of the PDC, and the signal has been processed to extract the average and maximum cycle pressures and the operating frequency of the spark plug. The PDC has been shown to be able to reach sustained detonation cycles over a length below 200 mm, measured from the spark plug to the first pressure sensor. The optimal regimes for both air and Oxygen operation have been determined, and the influence of the four factors on the responses is discussed. [ABSTRACT FROM AUTHOR]

The manufacturing geometrical variability in axial compressors is a stochastic source of uncertainty, implying that the real geometry differs from the nominal design. This causes the real geometry to lose the ideal axial symmetry. Considering the aerofoils of a stator vane, the geometrical variability affects the flow traversing it. This impacts the downstream rotor, especially when considering the aeroelastic excitation forces. Optical surface scans coupled with a parametrisation method allow for acquiring the information relative to the real aerofoils geometries. The measured data are included in a multi-passage and multi-stage CFD setup to represent the mistuned flow. In particular, low excitation harmonics on the rotor vane are introduced due to the geometrical deviations of the upstream stator. The introduced low engine orders, as well as their amplitude, depend on the stator geometries and their order. A method is proposed to represent the phenomena in a reduced CFD domain, limiting the size and number of solutions required to probabilistically describe the rotor excitation forces. The resulting rotor excitation forces are reconstructed as a superposition of disturbances due to individual stator aerofoils geometries. This indicates that the problem is linear in the combination of disturbances from single passages. [ABSTRACT FROM AUTHOR]

Internal combustion engines have been a major contributor to air pollution. Replacing these engines with electric propulsion systems presents significant challenges due to different countries' needs and limitations. An active, purely mechanical solution to the problem of irregular torque production in an alternative internal combustion engine is proposed. This solution uses an actuator built on a camshaft and a spring, which stores and returns energy during the engine operating cycle, allowing torque production to be normalized, avoiding heavy flywheels. Designed for control throughout the engine's duty cycle, this system incorporates a cam profile and a spring mechanism. The spring captures energy during the expansion stroke, which is then released to the engine during the intake and compression strokes. Simple, lightweight, and efficient, this system ensures smoother and more consistent engine operations. It presents a viable alternative to the heavy and problematic dual-mass flywheels that were introduced in the 1980s and are still in use. This innovative approach could significantly enhance the performance and reliability of alternative internal combustion engines without notable energy losses. [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]

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies. [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]

Reactivity-controlled compression ignition (RCCI) is a promising new combustion technology for marine applications. It has offered the potential to achieve low NOx emissions and high thermal efficiency, which are both important considerations for marine engines. However, the performance of RCCI engines is sensitive to a number of factors, including the start of injection. This study used computational fluid dynamics (CFD) to investigate the effects of start of ignition (SOI) on the performance of a marine RCCI engine. The CFD model was validated against experimental data, and the results showed that the SOI has a significant impact on the combustion process. In particular, the SOI affected the distribution of fuel and air in the combustion chamber, which in turn affected the rate of heat release and the formation of pollutants. Ten different SOIs were implemented on a validated closed-loop CFD model from 96 to 42 CAD bTDC (crank angle degree before top dead center) at six-degree intervals. A chemical kinetic mechanism of 54 species and 269 reactions tuned and used for simulation of in-cylinder combustion. The results show that in early injection, high-reactivity fuel was distributed close to the liner. This distribution was around the center of late injection angles. A homogeneity study was carried out to investigate the local equivalence ratio. It showed a more homogenous mixture in early injection until 66 CAD bTDC, after which point, earlier injection timing had no effect on homogeneity. Maximum indicated mean effective pressure (IMEP) was achieved at SOI 48 CAD bTDC, and minimum amounts of THC (total hydrocarbons) and NOx were observed with middle injection timing angles around 66 CAD bTDC. [ABSTRACT FROM AUTHOR]

Ammonia is a very promising alternative fuel for internal combustion engines, but there are some disadvantages, such as difficulty in ignition and slow combustion rate when ammonia is used alone. Aiming to address the problem of ammonia combustion difficulty, measures are proposed to improve ammonia combustion by blending hydrogen. A one-dimensional turbocharged ammonia-hydrogen engine simulation model was established, and the combustion model was corrected and verified. Using the verified one-dimensional model, the effects of different ratios of hydrogen to ammonia, different rotational speeds and loads on the combustion performance are investigated. The results show that the ignition delay and combustion duration is shortened with the increase of the hydrogen blending ratio. The appropriate amount of hydrogen blending can improve the brake's thermal efficiency. With the increase in engine speed, increasing the proportion of hydrogen blending is necessary to ensure reliable ignition. In conclusion, the ammonia-hydrogen fuel engine has good combustion performance, but it is necessary to choose the appropriate hydrogen blending ratio according to the engine's operating conditions and requirements. [ABSTRACT FROM AUTHOR]

The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines' optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times. [ABSTRACT FROM AUTHOR]

This study presents two concepts for integrating a wave rotor combustor (WRC) into a baseline engine: the wave rotor pressure-gain cycle (WRPGC) and the wave rotor work cycle (WRWC). Performance parameters were calculated under different thermodynamic cycles, and a comparative analysis of the thermodynamic cycles was conducted, considering both the ideal- and actual-loss conditions. Furthermore, the impact of the WRC precompression ratio, turbine inlet temperature, and fixed peak cycle temperature on the thermodynamic-cycle performance was investigated. The results indicate that embedding a WRC into a baseline engine with a compressor pressure ratio higher than 24.0 does not lead to an improvement in the thermal efficiency. However, under a baseline engine pressure ratio of 3.6, the actual-loss WRC cycle achieves efficiency improvements of 40.5% and 49.5% in the WRPGC and WRWC, respectively, compared to the baseline engine cycle. Increasing the wave rotor precompression ratio or the turbine inlet temperature ratio results in greater performance improvements for the WRWC compared to the WRPGC. When the peak cycle temperature of the wave rotor is fixed, there exists a narrow pressure ratio range wherein the WRPGC outperforms the WRWC. Therefore, the WRPGC is more suitable for embedment in baseline engines with lower pressure ratios. [ABSTRACT FROM AUTHOR]

The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively. [ABSTRACT FROM AUTHOR]

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]