Engine Test Bench Research Articles

Primary Particulate Matter and Aerosol Emissions from Biodiesel Engines During Idling in Plateau Environments of China

Diesel vehicles are recognized as significant mobile sources of particulate matter emissions. As a renewable and environmentally friendly alternative to conventional fossil diesel, biodiesel offers the benefit of reducing greenhouse gas emissions. However, existing research on biodiesel emissions primarily focuses on primary emissions, with a limited understanding of their impact on secondary organic aerosol (SOA) formation. In this study, a diesel engine test bench was employed under idle conditions using three commonly used biodiesel blends. Exhaust emissions were directly introduced into the HAP-SWFU chamber, a quartz glass smog chamber designed to characterize both primary emissions and SOA formation during the photochemical oxidation process. The black carbon and primary organic aerosol (POA) emission factors for the three biodiesel blends under idle conditions ranged from 0.31 to 0.58 g kg−1 fuel and 0.99 to 1.06 g kg−1 fuel, respectively. The particle size of exhaust particulates peaked between 20 and 30 nm, and nucleation-idle conditions were found to be the dominating mode. The SOA production factor was between 0.92 and 1.15 g kg−1 fuel, and the SOA/POA ratio ranged from 1.35 to 2.37, with an average of 1.86. This study concludes that the POA emission factor for biodiesel under idle conditions is comparable to values reported in previous studies on pure diesel exhaust, with the maximum SOA production factor reduced by 38%.

Multi-sensor adaptive fusion and convolutional neural network-based acoustic emission diagnosis for initial damage of the engine

Abstract Aiming at the problems of traditional fault diagnosis means that are difficult to identify initial damage, as well as the poor reliability and fault tolerance with a single sensor, an acoustic emission (AE) diagnosis method for initial damage of the engine based on multi-sensor adaptive fusion and convolutional neural network (CNN) is proposed. Firstly, under the premise of utilizing parametric analysis to characterize the multi-sensor AE signals, the feature parameter entropy is used to determine the primary and secondary relationships between multi-sensor signals, and then the AE feature parameter matrix is formed by adaptive fusion. Secondly, CNN is employed to mine and learn the fault feature combinations from the AE feature parameter matrix by multi-layer fusion to realize the identification and diagnosis for initial damage of the engine. Finally, the proposed method is validated on the engine test bench designed for initial damage identification and is compared with conventional methods in terms of diagnostic performance. The results demonstrate that the proposed method can achieve an identification accuracy of 98.83% for initial damage, and has advantages in various aspects such as TAMSE, K, F1mic, and F1mac, which explicitly provides a theoretical and methodological basis for identifying initial faults comprehensively and accurately. This research not only enriches the theory and methods in the field of structural health monitoring, but also provides strong technical support for engine health management, which is expected to play a key role in the maintenance and guarantee of aviation engines in the future.

Optimizing of selective catalytic reduction urea injection and NOx conversion analysis of diesel engine based on higher-order physical model and sequential quadratic programming algorithm

In order to maximize the NOx conversion rate and minimize NH3 slip in the diesel selective catalytic reduction (SCR) system, first, the high-order SCR model was simplified and solved using the trapezoidal method and second-order backward difference formula, while the chemical reaction parameters were optimized using the Gauss-Newton method. The results of the engine bench test show that the mean absolute errors of NH3 and NOx concentrations under steady-state operating conditions were 2.67 ppm and 1.72 ppm, respectively, and 5.46 ppm and 42.50 ppm under severe operating conditions. Second, urea injection rate is optimized based on a high-order model combined with the sequential quadratic programming (SQP) algorithm. The change in NOx conversion rate at different temperatures and air speeds is analyzed, and the results show that the NOx conversion is best at 650 K and 29,000 h⁻1. Third, the experiment validation show that the optimal NH3/NOx ratio is 0.69 at 554 K and 54,200 h⁻1; 0.8; and 1.37 at 746 K and 116,700 h⁻1. Finally, this paper simulates and analyzes the SCR emission characteristics at different temperatures and air speeds when the upstream NOx concentration is 1,000 ppm.

ПЛАНИРОВАНИЕ ЭКСПЕРИМЕНТА ДЛЯ ОПРЕДЕЛЕНИЯ ЗАВИСИМОСТИ ИЗМЕНЕНИЯ ЩЕЛОЧНОГО ЧИСЛА МОТОРНОГО МАСЛА ОТ ЭКСПЛУАТАЦИОННЫХ ФАКТОРОВ В ПРЕДЕЛАХ ОДНОГО СРОКА ЗАМЕНЫ

Timely replacement of oil in engines of modern agricultural machinery is economically feasible, since it allows to fully exhaust the resource of engine oil and ensure the safety of the internal combustion engine. One of the ways to determine the moment of engine oil replacement with sufficient accuracy is to use a digital twin, for the creation of which data on the influence of operational factors on oil degradation obtained experimentally are required. Features of conducting experimental studies during engine bench tests are analyzed in the paper. An algorithm for planning an experiment based on probabilistic methods has been developed. It is shown that a bench experiment to collect data for building a digital twin can be carried out within one period of engine oil replacement, it should be planned in several stages and consist of several series of experiments, since the moment of reaching the critical value of the base number is not known in advance, and when planning the main experiment covering 250 engine hours in two series of 10 experiments, the duration of each experiment is determined from the condition of equality of the error in determining the change in the base number. The duration of the first series of experiments was 62.7 engine hours, and the second — 187.3 engine hours. The developed algorithm allowed to build a plan of a bench experiment consisting of four series of experiments with sequential uniform filling of the factor space without repeating the engine operating modes. The resulting experimental plan allows to determine the data with the planned accuracy, minimizes the number of experiments and uniformly fills the factor space at any duration of the experiment.

Optimization study of diesel engine emission prediction based on neural network model cluster

This study aims to reduce the testing volume and cost of engine bench tests. By combining neural network models and ensemble learning algorithms, a model cluster prediction method is proposed to predict engine emissions. The core of this method is to use a large number of neural network models for prediction, taking the average of the prediction results as the final prediction result, thereby reducing prediction errors and improving accuracy. The findings reveal that this cluster-based approach significantly outperforms a single optimal model in forecasting steady-state emissions of NOx, HC, and CO in diesel engines. Notably, the performance of the model cluster is highly dependent on both the quality and the number of constituent sub-models, with enhanced predictive capabilities observed when higher-quality sub-models are included. Additionally, the study identifies a stabilization in predictive accuracy as the number of models increases, particularly within the range of 50 to 100 models, and achieving robust stability beyond 100 models. The research also underscores the importance of the training dataset size on the effectiveness of neural network modeling. A reduction in the training dataset from 28% to 18% leads to a decline in the coefficient of determination ( R2) for NOx emissions prediction in a single neural network model from 0.6954 to 0.6539, a 5.97% decrease. For the model cluster method, the R2 similarly drops from 0.8633 to 0.8154, a reduction of 5.55%. Notably, the neural network model suffers from distortion in predicting the peak position of NOx emissions. After supplementing specific operating condition training data in a targeted manner, the model training amount was increased from 18% to 25%. The model cluster method showed a good improvement in the prediction of NOx emissions, with the prediction coefficient increasing from 0.8154 to 0.8997, with an improvement rate of 10.34%. Through planning of training data, the study demonstrates that employing just 25% of experimental data for model clustering can effectively predict the engine emission trends for the remaining 75% of data in the target conditions. This approach offers a substantial reduction in experimental requirements while maintaining high prediction accuracy.

Experimental and simulation study on lean combustion characteristics of passive pre-combustion chamber ignition boosted GDI engine

In order to verify the potential application of passive pre-combustion chamber technology in commercial GDI engines, the efficiency characteristics, combustion characteristics (CA50, Knock Index and COV) and emission characteristics of a pre-combustion chamber ignition engine in a boosted environment were studied by three-stage injection strategy to form a lean burn environment (λ = 1.3). A 3D simulation model of pre-combustion chamber ignition engine is established. Combined with the engine bench test results, the process of intake, mixture formation, combustion and emission generation under different working conditions is simulated by simulation method. The development process of TKE (turbulent kinetic energy), air-fuel ratio, temperature and combustion emissions in the main/pre-combustor is studied. It is found that under high load conditions in boosted pre-combustion chamber engine, the average knock index can be controlled between 2 and 3.4 bar, and the COV can be controlled between 3% and 3.5%. The indicated thermal efficiency reaches over 40% within the load range of IMEP from 10 to 14 bar. When the load increases, NOx and soot emissions show a downward trend, the HC and CO emissions slight increase. The pre-combustion chamber engine exhibits a tumble intake pattern around the cylinder axis perpendicular to the cylinder axis. The TKE reaches its maximum value 20 CAD before the top dead center and then rapidly decreases. The use of a three-injection strategy can form a uniform stratified mixture at the ignition moment. The fuel air equivalence ratio in the ignition area of the pre-combustion chamber is 0.8–0.9, slightly higher than the average fuel air equivalence ratio of 0.75.

МЕТОДИ ВИЗНАЧЕННЯ ЕКСПЛУАТАЦІЙНИХ ПОКАЗНИКІВ МАШИН ТРАНСПОРТНОГО БУДІВНИЦТВА

The article presents the main methods of determining the main operational indicators of transport construction machines, which are their fuel efficiency and environmental friendliness. Considered international standards such as STAGE and TIER set emission limits for off-road equipment, including transport construction machinery. In Ukraine, the SOU 42.1-37641918-094:2017 standard is used to determine the fuel efficiency of road machines and mechanisms, which determines the consumption norms of fuel and lubricants. The calculations are based on the specific fuel consumption at the nominal power of the engine and the integral coefficient, which takes into account the average operating conditions of the machine during the work shift. Typical engine load charts were analyzed to study the efficiency and environmental friendliness of special machines used in transport construction and other industries. They reflect the dependence of the torque change on the duty cycle time. Another research method involves testing using the test cycles described in ISO 8178. The procedure involves the use of several test cycles on an engine test bench. These cycles vary depending on the engine class and the type of equipment being tested. Each cycle is a sequence of stable or transient modes of operation of the engine with different load factors. In cases where laboratory testing is not possible or suitable, ISO 8178-2 provides measurements in real operating conditions using a portable emission measurement system (PEMS). This allows you to receive data directly during the normal operation of the car. Test cycles are key tools for evaluating the fuel efficiency and environmental performance of construction vehicles. They allow you to simulate different modes of operation and obtain accurate data on the operational properties of the equipment. However, given the unique characteristics and operating conditions of different types of machines, there is a need to develop specialized test cycles that best match the actual conditions of use of the equipment. Keywords: test cycle, performance indicators, consumption norms of fuel and lubricants, fuel efficiency, environmental safety, toxicity norms, engine load chart, portable system for measuring harmful emissions, transport construction machines.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.

Development of Miniaturised Fibre-Optic Laser Doppler Velocimetry for Opaque Liquid: Measurement of the Velocity Profile in the Engine Oil Flow of a Lubrication System

This study developed a fibre-optic laser Doppler velocimetry sensor for use in opaque, high-temperature, and high-pressure fluid flows by inserting the fibre perpendicular to the main flow. The tip of the optical fibre was obliquely polished and chemically etched using a buffered hydrofluoric acid solution, and a reflective mirror was deposited on the surface of the oblique fibre tip. Based on the results of the verification test using the rotating annular open channel, the fabrication conditions of the fibre tip were optimized for measuring the lubricating oil flow. The flow velocity profiles in the engine’s oil flow of the lubrication system during engine bench testing were measured. These velocity profiles were influenced by variations in the measurement position, oil temperature, and engine speed. The measurement accuracy of this sensor was compared with the volumetric flow rate obtained by cross-sectional area integration of the flow velocity profile, as measured using a Coriolis flowmeter, and the difference was within 1%. By combining computational simulation for flow and optical attenuation and particle scattering in light transmission through a working fluid, this fibre-optic sensor achieved a measurement volume of 200 microns in length and 200 microns in width at a distance of 900–1000 microns from the sensor.

Updating a Didactical Piston Engine Test Bench, from Analogue Instrumentation to Digital

Updating a Didactical Piston Engine Test Bench, from Analogue Instrumentation to Digital

Real-World Emission Characteristics of Diesel Pallet Trucks under Varying Loads: Using the Example of China

Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOX), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO2, CO, and NOX increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks.

Study on the synergistic control of nitrogenous emissions and greenhouse gas of ammonia/diesel dual direct injection two-stroke engine

This study developed a numerical model for an ammonia/diesel dual direct-injection two-stroke engine and validated it based on engine bench test results. The engine combustion and emission characteristics were discussed under the cases of different injection parameters, intake temperatures, and exhaust gas re-circulation (EGR) rates. The findings indicate that altering the injection angle of ammonia and diesel sprays impacts the in-cylinder swirl and the ignition timing of the ammonia spray, consequently affecting the heat release phase of ammonia combustion. A suitable arrangement spray plumes can effectively reduce the emissions of unburned ammonia and greenhouse gas (GHG), while increase the emissions of NOx. On the other hand, higher intake temperatures can increase the average in-cylinder temperature, reducing unburned ammonia, N2O and GHG emissions. However, it also results in greater NO emissions, which can affect fuel economy negatively. Furthermore, the use of EGR technology can reduce the combustion temperature, thereby lowering the emission of nitrogen-based pollutants, but it also reduces ammonia combustion efficiency. By simultaneously applying the three aforementioned technologies within a certain range of conditions, it is possible to effectively synergize and control nitrogenous emissions and GHG while maintaining high thermal efficiency of ammonia/diesel dual direct injection engines.

Impact of Mid-to-Low-Ash, Low-Viscosity Lubricants on Aftertreatment Systems after 210,000-Kilometer Real-World Road Endurance Trials

Engine lubricants globally face the challenge of meeting the demands of new engine technologies while enhancing energy efficiency and reducing emissions. Lubricants must enhance their performance and sustainability, improve reliability in complex and harsh environments, and minimize environmental impact and health risks. This study explores the influence of two different formulations of low viscosity lubricants, tested through actual road endurance trials, on a hybrid vehicle’s aftertreatment system performance and overall emission levels. The study includes 120,000 km of endurance testing in four different challenging environments in China, as well as 90,000 km of endurance testing in a typical urban and highway driving cycle in a large city. Results indicate that emissions from the test vehicles during the 120,000 km and 210,000 km durable Worldwide harmonized Light vehicle Test Cycles (WLTCs) meet China’s Stage 6 light-duty vehicle emission standards, with the 210,000 km Real Driving Emission test (RDE) results also conforming to these standards. Relative to fresh TWC, the light-off temperature increased by a mere 60 °C, and the oxygen storage capacity declined by around 19% following endurance testing. Additionally, the GPF exhibited satisfactory performance after 210,000 km of endurance testing, showing lower backpressure values compared to the fresh-coated samples, with no notable ash buildup observed in the substrate. Drawing on the outcomes of actual road endurance testing, this study illustrates that employing low-to-mid-ash-content, low-viscosity lubricants is both compatible and reliable for aftertreatment systems in present or advanced hybrid technologies. Premium lubricants facilitate vehicles in sustaining compliant and stable emission performance, even amid harsh environments and complex operating conditions. Furthermore, the tested lubricants effectively inhibit excessive aging of the aftertreatment system over prolonged mileage. Moreover, this study discusses the feasibility of rapid aging evaluation methods for aftertreatment systems based on engine test benches, juxtaposed with actual road endurance testing. These findings and conclusions offer crucial references and guidance for enhancing lubricant performance and sustainability. Subsequent studies can delve deeper into the correlation between lubricant performance and environmental impact, alongside optimization strategies for lubricants across various vehicle models and usage scenarios.

A novel method for aero-engine time-series forecasting based on multi-resolution transformer

Time-series forecasting is widely studied in the data-driven component-level modeling, fault diagnosis, and performance prediction of aero-engines. The operational data of aero-engines have the characteristics of complexity and nonlinearity. This paper proposes a multi-resolution transformer (MRT) model for the non-steady state process of aero-engines. This transformer-based model can learn temporal patterns of different scales by performing multi-resolution down-sampling and patch-based tokeniczation on the input time-series. On this basis, a two-stage multi-resolution transformer (TSMRT) framework is proposed for the entire processes time-series modeling of aero-engines. The TSMRT framework employs Augmented Dickey-Fuller (ADF) test to classify aero-engine operational data into steady state processes and non-steady state processes, and models the two processes separately using time–frequency feature neural network (TFNN) model and MRT model. The time-series forecasting performance of the MRT model is validated on three publicly available benchmark datasets and an ablation study is conducted on turbofan engine test bench datasets. The results indicate that the TSMRT framework demonstrates superior forecasting performance across steady state, non-steady state, and entire processes. This framework stands out as a novel method for component-level modeling of aero-engines.

Experimental Investigation on the Effect of Heating Oil and Tyre Pyrolysis Oil Combustion in an Evaporative Combustion Chamber

This research aims to delve into the intricacies of combustion processes, specifically focusing on heating oil and a blend of heating oil with Tire Pyrolysis Oil (TPO) in a self-developed evaporative combustion chamber featuring steam injection. The primary objective is to scrutinize the impact of steam injection on the combustion dynamics. Conducting a series of tests, the investigation involved the meticulous manipulation of stoichiometric ratios while introducing ambient air through gravity fuel flow. Subsequent iterations of these tests incorporated the introduction of steam into the ambient air stream. The examination encompassed the combustion of both heating oil and the TPO blend within the combustion chamber. The evaluation criteria comprised an in-depth analysis of flame characteristics, temperature distribution within the combustion chamber, and the quantification of emissions such as particulate matter (PM), nitrogen oxides (NOx), carbon dioxide (CO2), carbon monoxide (CO), and water vapor (H2O). Throughout the experimentation phase, commercially available diesel fuel served as the primary fuel source. To facilitate the tests, the combustion chamber under scrutiny was seamlessly integrated into an AVL engine test bench system. Essential parameters, including fuel consumption, were meticulously gauged using an AVL 735 fuel flow meter, while fuel temperature was monitored using the AVL 745 fuel temperature conditioning system. The intake air, a crucial element in the combustion process, was quantified with precision using an AVL Flowsonix sensor. Emission measurements were conducted meticulously using state-of-the-art equipment, with gaseous emissions analyzed using an AVL FTIR AMA i60 exhaust gas analyzer. Simultaneously, soot emissions were quantified through employment of an AVL Micro Soot sensor. This comprehensive approach not only delves into the fundamental aspects of combustion but also extends its reach to the exploration of innovative techniques, such as steam injection, to enhance combustion efficiency and reduce emissions. The integration of advanced measurement tools ensures a robust and thorough analysis of the combustion process and its environmental implications.

Particle filter performance of soot-loaded diesel particulate filter and the effect of its regeneration on the particle number and size distribution

The DPF regeneration is crucial for reducing backpressure, minimizing negative impacts on engine power and fuel economy, but the changes in particulate emission characteristics it brings are worth studying. In this study, the influence of DPFs with different soot loadings on exhaust back pressure and particle emission characteristics, as well as the particle number emission, particle size distribution, and thermal field distribution characteristics during regeneration was investigated based on engine bench test. Results reveal that the exhaust back pressure increases linearly with the engine speed, as well as the soot loading. The increase of DPF soot loading leads to a considerable increase in the reduction efficiency of particle number (PN) and particulate matter emissions, especially for the accumulation mode particles. Moreover, the performance in reducing nucleation mode particles is markedly better than that of accumulation mode ones, and this effect is greatly influenced by the engine operating conditions. At the beginning of DPF regeneration, the PN concentration increases sharply, especially the nucleation mode particles. After regeneration, the PN emissions decrease by two orders of magnitude. During regeneration, the particle size distribution exhibits a unimodal distribution, with the peak value increasing considerably and shifting toward smaller particle sizes. After regeneration, the particle size of the PN peak value slightly decreases and markedly shifts toward larger sizes, the accumulation mode particle concentration increases by 7.2 times, and the nucleation mode particle concentration increases by 2.6 times. Before the DPF regeneration, the axial temperature gradually decreases along the direction of the airflow, and the radial temperature gradually decreases from the center to the edge. During regeneration, the highest temperature occurs in the center of the DPF cross-section, whereas the temperature peak at the outlet interface occurs at the middle position of the DPF. The findings of this paper can provide scientific references to formulation and optimization of DPF regeneration strategies.

Innovative engine test bench set-up for testing of exhaust gas aftertreatment and detailed gas species analysis for CNG-SI-operation

For an efficient reduction of methane slip, a precise understanding of exhaust gas after treatment under real conditions is essential. Since it is not possible to produce catalytic converters in near-series geometry on a laboratory scale, it is necessary to resort to significantly smaller sample catalysts. Therefore, an engine test bench was designed to ensure real operating conditions for such samples with the help of space velocity and temperature control. A comparison between the actual and reference values of the space velocity results in a small deviation of 0.1% on average. Furthermore, the pressure conditions at the catalyst have been measured showing a propagation of pressure oscillations from the engine outlet which in combination with the space velocity regulation show that real conditions could be applied to the catalyst sample. Subsequently, the exhaust gas concentrations were monitored with a Fourier transform infrared spectrometer. The catalyst material used is PdO on Al2O3, common for methane oxidation. The measurements show that the CH4 conversion is higher under lean conditions, but is below complete conversion. In a final comparison between purely stoichiometric operation and dithering, the course of the CH4 conversion rate over the test period is examined more closely. In addition to sampling pre- and post-catalyst, the exhaust gas composition is measured spatially resolved within a catalyst channel using special measurement technology. In the temporal course of the CH4 emissions, a stabilizing effect due to the change of the operating mode can be seen, showing that dithering seems to prevent further deactivation.

Promoted catalytic property of Cu/SSZ-13 by introducing a minority of Mn for NO removal from diesel engine exhaust

The Cu-exchanged SSZ-13 with the small-pore chabazite framework is considered as a highly efficient catalyst for selective catalytic reduction of NO with NH3 (NH3-SCR). In order to further improve the catalytic property, a series of Mn ion-assisted Cu/SSZ-13 powder catalysts were prepared by co-exchange method and stepwise exchange method. It is found that the NH3-SCR activity, N2 selectivity, hydrothermal stability and sulfur resistance of Cu/SSZ-13 are promoted by introducing a minority of Mn (0.15%–0.23%(mass)) through co-exchange method. Characterization results revel that the Cu,Mn co-exchange enables the higher amounts of Cu2+ active sites, the abundant medium strong and strong acid, the optimized ratio of lewis acid to Brønsted acid, etc., which are required for a good NH3-SCR catalytic property over broad temperature range and under harsh working environment. Moreover, a monolithic catalyst was prepared by impregnating a cordierite ceramic support into the coating slurry containing the optimized CuMn/SSZ-13 powder. The diesel engine bench tests show that Cu,Mn co-exchange gives the monolith catalyst a better catalytic property than commercial catalysts. This work provides an important guidance for the rational design of secondary-ion-assisted zeolites applied in NH3-SCR.

Coupling analysis of cylinder block for two-stroke aviation piston engine

The aluminum alloy block is a component of aircraft piston engine, and it is prone to fatigue cracks when working in a thermal mechanical coupling state for a long time. Establish a GT-POWER simulation model for a certain type of engine, verify the accuracy of the model, obtain boundary parameters such as temperature and pressure of the engine block under harsh operating conditions through the model, and divide the cylinder wall into gradients based on the engine operating conditions to obtain the surface heat transfer coefficient of the block, and then obtain the temperature field distribution of the engine body. The coupling analysis of the cylinder burst pressure and temperature field of the engine block under harsh working conditions showed that the maximum stress of the engine block was 292.55 MPa and the maximum deformation was 0.39 mm, with thermal load being the main factor causing deformation. Conduct a complete engine bench test, and under the 1000 h bench durability test, there are no cracks on the engine block, indicating that the design and analysis meet the requirements.

Effect of Variable-Nozzle-Turbocharger-Coupled Exhaust Gas Recirculation on Natural Gas Engine Emissions and Collaborative Optimization

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH4 increased by 3.7%, on average.