Search Results (33)

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

Ammonia is a carbon-free fuel with strong potential for emission reduction. However, its high auto-ignition temperature and low reactivity lead to poor ignitability and unstable combustion. In contrast, coal-to-liquid (CTL) fuel offers high cetane number, low sulfur content, and low aromaticity, making it a clean fuel with excellent ignition performance. Blending CTL with ammonia can effectively compensate for ammonia’s combustion limitations, offering a promising pathway toward low-carbon clean combustion. This study explores the effects of post-injection strategies on combustion and emission characteristics of a CTL–ammonia dual-fuel engine under different levels of ammonia energy fractions (AEFs). Results show that post-injection significantly improves combustion and emission performance by expanding ammonia’s the favorable reactivity range of ammonia and enhancing NH₃ oxidation, particularly under moderate AEF conditions (5–10%) where ammonia and CTL demonstrate strong synergy. For emissions, moderate post-injection notably reduces CO at low AEFs, while NO_X emissions consistently decrease with increasing post-injection quantity, with greater suppression observed at higher AEFs. Soot emissions are also effectively reduced under post-injection conditions. Although total hydrocarbon (THC) emissions increase due to ammonia’s low reactivity, post-injection mitigates this accumulation trend to some extent, demonstrating overall co-benefits for emission control. Comprehensive evaluation indicates that the combination of 5–10% AEF, 8–12 mg post-injection quantity, and post-injection timing of 10–15 °CA achieves the most favorable balance of combustion efficiency, emissions reduction, and reaction stability, confirming the potential of the CTL–ammonia dual-fuel system for clean and efficient combustion. Full article

Ignition delay time (IDT) is a critical parameter for evaluating the autoignition characteristics of aviation fuels. However, its accurate prediction remains challenging due to the complex coupling of temperature, pressure, and compositional factors, resulting in a high-dimensional and nonlinear problem. To address this challenge for the complex aviation kerosene RP-3, this study proposes a multi-stage hybrid optimization framework based on a five-input, one-output BP neural network. The framework—referred to as CGD-ABC-BP—integrates randomized initialization, conjugate gradient descent (CGD), the artificial bee colony (ABC) algorithm, and L2 regularization to enhance convergence stability and model robustness. The dataset includes 700 experimental and simulated samples, covering a wide range of thermodynamic conditions: 624–1700 K, 0.5–20 bar, and equivalence ratios φ = 0.5 − 2.0. To improve training efficiency, the temperature feature was linearized using a 1000/T transformation. Based on 30 independent resampling trials, the CGD-ABC-BP model with a three-hidden-layer structure of [21 17 19] achieved strong performance on internal test data: R² = 0.994 ± 0.001, MAE = 0.04 ± 0.015, MAPE = 1.4 ± 0.05%, and RMSE = 0.07 ± 0.01. These results consistently outperformed the baseline model that lacked ABC optimization. On an entirely independent external test set comprising 70 low-pressure shock tube samples, the model still exhibited strong generalization capability, achieving R² = 0.976 and MAPE = 2.18%, thereby confirming its robustness across datasets with different sources. Furthermore, permutation importance and local gradient sensitivity analysis reveal that the model can reliably identify and rank key controlling factors—such as temperature, diluent fraction, and oxidizer mole fraction—across low-temperature, NTC, and high-temperature regimes. The observed trends align well with established findings in the chemical kinetics literature. In conclusion, the proposed CGD-ABC-BP framework offers a highly accurate and interpretable data-driven approach for modeling IDT in complex aviation fuels, and it shows promising potential for practical engineering deployment. Full article

Methanol can be synthesized using green electricity and carbon dioxide, making it a green, carbon-neutral fuel with significant potential for widespread application in engines. However, due to its low ignition energy and high laminar flame speed, methanol is susceptible to hotspot-induced pre-ignition and even knocking under high-temperature, high-load engine conditions, posing challenges to engine performance and reliability. This paper systematically reviews the manifestations and mechanisms of pre-ignition and knocking in methanol engines. Pre-ignition can be sustained or sporadic. Sustained pre-ignition is caused by overheating of structural components, while sporadic pre-ignition is often linked to oil droplets entering the combustion chamber from the piston crevice. Residual exhaust gas trapped within the spark plug can also initiate pre-ignition. Knocking, characterized by pressure oscillations, arises from the auto-ignition of hotspots in the end-gas or, potentially, from deflagration-to-detonation transition, although the latter requires further experimental validation. Factors influencing pre-ignition and knocking, including engine oil, in-cylinder deposits, structural hotspots, and the reactivity of the air–fuel mixture, are also analyzed. Based on these factors, the paper concludes that the primary approach to suppressing pre-ignition and knocking in methanol engines is controlling the formation of pre-ignition sources and reducing the reactivity of the air–fuel mixture. Furthermore, it addresses existing issues and limitations in current research, such as combustion testing techniques, numerical simulation accuracy, and the mechanisms of methanol–oil interaction, and offers related recommendations. Full article

The generation of ozone by dielectric barrier discharge (DBD) is widely used for water and wastewater treatment, the control of catalytic reactions, and surface treatment. Recently, a need for compact, effective, and economical ozone and reactive oxygen–nitrogen species (RONS) generators for medical, biological, and agricultural applications has been observed. In this study, a novel hybrid DBD (HDBD) reactor fulfilling such requirements is presented. Its structured high-voltage (HV) electrode allows for the ignition of both the surface and volume microdischarges contributing to plasma generation. A Peltier module cooling of the dielectric barrier, made of alumina, allows for the efficient control of plasma chemistry. The typical electrical power consumption of this device is below 30 W. The operation frequency of the DBD driver oscillating in the auto-resonance mode is from 20 to 40 kHz. The specific energy input (SEI) of the reactor was controlled by the DBD driver input voltage in the range from 10.5 to 18.0 V, the Peltier current from 0 to 4.5 A, the duty cycle of the pulse-width modulated (PWM) power varied from 0 to 100%, and the gas flow from 0.5 to 10 SLM. The operation with oxygen, synthetic air, and compressed dry air (CDA) was characterized. The ultraviolet light (UV) absorption technique was implemented for the measurement of the ozone concentration. The higher harmonics of the discharge current observed in the frequency range of 5 to 50 MHz were used for monitoring the discharge net power. Full article

To meet climate targets, a global shift away from fossil fuels is essential. For sectors where electrification is impractical, it is crucial to find sustainable energy carriers. Renewable methanol is widely considered a promising fuel for powering heavy-duty applications like shipping, freight transport, agriculture, and industrial machines due to its various sustainable production methods. While current technological efforts focus mainly on dual-fuel engines in shipping, future progress hinges on single-fuel solutions using renewable methanol to achieve net-zero goals in the heavy-duty sector. This review examines the research status of technologies enabling methanol as the sole fuel for heavy-duty applications. Three main categories emerged from the literature: spark-ignition, compression-ignition, and pre-chamber systems. Each concept’s operational principles and characteristics regarding efficiency, stability, and emissions were analyzed. Spark-ignition concepts are a proven and cost-effective solution with high maturity. However, they face limitations due to knock issues, restricting power output with larger bore sizes. Compression-ignition concepts inherently do not suffer from end-gas autoignition, but encounter challenges related to ignitability due to the low cetane number of methanol. Nonetheless, various methods for achieving autoignition of methanol exist. To obtain stable combustion at all load points, a combination of techniques will be required. Pre-chamber technology, despite its lower maturity, holds promise for extending the knock limit and enhancing efficiency by acting as a distributed ignition source. Furthermore, mixing-controlled pre-chamber concepts show potential for eliminating knock and the associated size and power limitations. The review concludes by comparing each technology and identifying research gaps for future work. Full article

Gasoline engines employing the spatially distributed auto-ignition combustion mode, known as controlled auto-ignition (CAI), are a prospective technology for significantly improving engine efficiency and reducing emissions. This review paper provides an overview of developments in various gasoline CAI technologies and discusses their attendant strengths and weaknesses. Hybrid propulsion systems powered by high-efficiency gasoline CAI engines can provide a low-carbon pathway for mobility sector decarbonisation. Therefore, this paper focuses on the challenges and opportunities of CAI implementation, especially for electrified powertrains. Different control actuators that can extend the CAI operating range are discussed, and opportunities for synergistic operation between thermal and electric components of hybridised powertrains are identified. Such synergies can remove impediments in the way of CAI system adoption and can, thus, support CAI adoption and maximise efficiency gains from its implementation. The prospects of supporting CAI combustion for different powertrain electrification levels, hybrid architectures, engine size, and energy management systems are discussed. Load levelling offered by electrified powertrains through CAI-favouring energy management strategies has the potential to substantially relax the operating point requirements for CAI-based thermal propulsion units and to remove the need for expensive actuators. The highly flexible spark-assisted partially premixed compression ignition hybrid mode (SACI-PPCI) emerges as a promising CAI strategy for conventional powertrains, and the moderately flexible spark-assisted compression ignition (SACI) configuration can be a cost-effective thermal propulsion mode for electrified powertrains. Full article

Homogeneous charge compression ignition is considered a promising solution to face the increasing regulations imposed by the legislator in the transport sector, thanks to pollutant and CO₂ emissions reduction. In this work, a quasi-dimensional multi-zone HCCI model integrated with 1D commercial software is developed and validated. It is based on the control mass Lagrangian approach and computes the mixture chemistry evolution through offline tabulation of chemical kinetics (tabulated kinetic of ignition). Thus, the simulation can predict mixture auto-ignition with reduced computational effort and high accuracy. Multi-zone schematization mimics the typical thermal stratification of HCCI engines, controlling the combustion evolution. The model is coupled to sub-models for pollutant emissions estimation. Initially, the tabulated chemistry approach is validated against a chemical kinetics solver applied to a constant-volume homogeneous reactor, considering various fuel blends. The model is then used to simulate the operations of four engines using different fuels (hydrogen, methane, n-heptane, and n-heptane/toluene/ethanol blend), under various boundary conditions. The model predictivity is demonstrated against pressure traces, heat release rate, and noxious emissions. The numerical results showed to adequately agree with measured counterparts (average relative error of 1.3% on in-cylinder pressure peak, average absolute error of 0.95 CAD on pressure peak angle, average relative error of 8.4% on uHCs emissions, absolute error below 1 ppm on NOx emissions) only adapting the thermal stratification to the engines under study. The methodology proved to be a reliable tool to investigate the operation of an HCCI engine, applicable in the development of new engine architecture. Full article

Spinel ferrites are widely investigated for their widespread applications in high-frequency and energy storage devices. This work focuses on enhancing the magnetic and dielectric properties of Ni_0.25Cu_0.25Zn_0.50 ferrite series through non-thermal microwave plasma exposure under low-pressure conditions. A series of Ni_0.25Cu_0.25Zn_0.50 ferrites was produced using a facile sol–gel auto-ignition approach. The post-synthesis plasma treatment was given in a low-pressure chamber by sustaining oxygen plasma with a microwave source. The structural formation of control and plasma-modified ferrites was investigated through X-ray diffraction analysis, which confirmed the formation of the fcc cubical structure of all samples. The plasma treatment did not affect crystallize size but significantly altered the surface porosity. The surface porosity increased after plasma treatment and average crystallite size was measured as about ~49.13 nm. Morphological studies confirmed changes in surface morphology and reduction in particle size on plasma exposure. The saturation magnetization of plasma-exposed ferrites was roughly 65% higher than the control. The saturation magnetization, remnant magnetization, and coercivity of plasma-exposed ferrites were calculated as 74.46 emu/g, 26.35 emu/g, and 1040 Oe, respectively. Dielectric characteristics revealed a better response of plasma-exposed ferrites to electromagnetic waves than control. These findings suggest that the plasma-exposed ferrites are good candidates for constructing high-frequency devices. Full article

The evaporation, autoignition and micro-explosion characteristics of RP-3 kerosene droplets under sub-atmospheric pressure (0.2–1.0 bar) and elevated temperature (473–1023 K) were experimentally investigated using high-speed camera technology. The results showed that the droplet evaporation rate increased monotonically with increasing temperature and pressure under 573–873 K and 0.2–1.0 bar. The decrease of temperature and pressure was obviously detrimental to the successful autoignition of droplets and increased the ignition delay time. Autoignitions at 0.2 bar were very difficult and required an ambient temperature of at least 973 K, which was about 150 K higher than the minimum ignition temperature at 1.0 bar. Sub-atmospheric pressure environment significantly inhibits the formation of soot particle clusters during the autoignition of droplet. Reducing pressure was also discovered to reduce the likelihood of micro-explosions at 673, 773 and 823 K but increase the bubble growth rate and droplet breakage intensity. Strong micro-explosions with droplet breakage time close to 1 ms were observed at 0.6 bar and 773/823 K, showing the characteristic of bubble inertia control growth. Full article

The spark ignition engine is a complex multi-domain system that contains many variables to be controlled and managed with the aim of attending to performance requirements. The traditional method and workflow of the engine calibration comprise measure and calibration through the design of an experimental process that demands high time and costs on bench testing. For the growing use of virtualization through artificial neural networks for physical systems at the component and system level, we came up with a likely efficiency adoption of the same approach for the case of engine calibration that could bring much better cost reduction and efficiency. Therefore, we developed a workflow integrated into the development cycle that allows us to model an engine black-box model based on an auto-generated feedfoward Artificial Neural Network without needing the human expertise required by a hand-crafted process. The model’s structure and parameters are determined and optimized by a genetic algorithm. The proposed method was used to create an ANN model for injection parameters calibration purposes. The experimental results indicated that the method could reduce the time and costs of bench testing. Full article

The global automotive industry is undergoing a significant transition as battery electric vehicles enter the market and diesel sales decline. It is widely recognized that internal combustion engines (ICE) will be needed for transport for years to come; however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach for achieving the demanding efficiency and emissions targets. A key technology enabler for GCI is partially-premixed, compression ignition (PPCI) combustion, which involves two high-pressure, late fuel injections during the compression stroke. Both NOx and smoke emissions are greatly reduced relative to diesel, and this reduces the aftertreatment (AT) requirements significantly. For robust low-load and cold operation, a two-step valvetrain system is used for exhaust rebreathing (RB). Exhaust rebreathing involves the reinduction of hot exhaust gases into the cylinder during a second exhaust lift event during the intake stroke to help promote autoignition. The amount of exhaust rebreathing is controlled by exhaust backpressure, created by the vanes on the variable nozzle turbine (VNT) turbocharger. Because of the higher cycle temperatures during rebreathing, exhaust HC and CO may be significantly reduced, while combustion robustness and stability also improve. Importantly, exhaust rebreathing significantly increases exhaust temperatures in order to maintain active catalysis in the AT system for ultra-low tailpipe emissions. To achieve these benefits, it is important to optimize the rebreathe valve lift profile and develop an RB ON→OFF (mode switch) strategy that is easy to implement and control, without engine torque fluctuation. In this study, an engine model was developed using GT-Suite to conduct steady-state and transient engine simulations of the rebreathing process, followed by engine tests. The investigation was conducted in four parts. In part 1, various rebreathe lift profiles were simulated. The system performance was evaluated based on in-cylinder temperature, exhaust temperature, and pumping work. The results were compared with alternative variable valve actuation (VVA) strategies such as early exhaust valve closing (EEVC), negative valve overlap (NVO), positive valve overlap (PVO). In part 2, steady-state simulations were conducted to determine an appropriate engine load range for mode switching (exhaust rebreathing ON/OFF and vice-versa). The limits for both in-cylinder temperature and exhaust gas temperature, as well as the external exhaust gas recirculation (EGR) delivery potential were set as the criteria for load selection. In part 3, transient simulations were conducted to evaluate various mode switch strategies. For RB OFF, the cooled external EGR was utilized with the goal to maintain exhaust gas dilution during mode switches for low NOx emissions. The most promising mode-switch strategies produced negligible torque fluctuation during the mode switch. Finally, in part 4, engine tests were conducted, using the developed RB valve lift profile, at various low-load operating conditions. The mode switch experiments correlated well with the simulation results. The tests demonstrated the simplicity and robustness of the exhaust rebreathing approach. A robust engine response, low CNL, high exhaust gas temperature, and low engine out emissions were achieved in the low load region. Full article

The spark ignition (SI)-controlled auto-ignition (CAI) hybrid combustion is characterized by early flame propagation combustion and subsequent auto-ignition combustion. The application of combined SI–CAI hybrid combustion can be used to effectively extend the operating range of CAI combustion and achieve smooth transitions between SI and CAI combustion modes. However, SI–CAI hybrid combustion can produce significant cycle-to-cycle variations (CCV). In order to better understand the sources of CCV and minimize its occurrence, the large eddy simulation (LES) and Reynolds-averaged Navier–Stokes (RANS) approaches were employed in this study to model and understand the cyclic phenomenon of SI–CAI hybrid combustion. Both the multi-cycle LES and RANS simulations were analyzed against the experimental measurements in a single cylinder engine at 1500 rpm and a 5.43 bar average indicated the mean effective pressure (IMEP). The detailed analysis of the in-cylinder pressure traces, IMEP, in-cylinder peak pressure (PP), peak pressure rise rate (PPRR) and the crank angles with fuel mass burned fraction at 10%, 50%, 90% and mode transition was performed. The results indicate that overall, the adopted LES simulations could effectively predict the cyclic variations in the hybrid combustion observed in the experiments, while the RANS simulations failed to reproduce the cyclic characteristics at the chosen engine operating conditions. Based on the LES results, the correlation and visualization studies indicate that the cyclic variations in the local velocity around the spark plug lead to the variations in the early flame propagation, which in turn produce temperature fluctuations among the cycles and result in greater variations in the subsequent auto-ignition combustion events. Full article

The auto-ignition caused by oblique shocks was investigated experimentally in a supersonic flow regime, with the incoming flow at a Mach number of 2.5. The transient characteristics of the auto-ignition caused by shock evolvements were recorded with a schlieren photography system, and the initial flame kernel generation and subsequent propagation were recorded using a high-speed camera. The fuel mixing characteristics were captured using NPLS (nanoparticle-based planar laser scattering method). This work aimed to reveal the flame spread mechanism in a supersonic flow regime. The effects of airflow total temperature, fuel injection pressure, and cavity length in the process of auto-ignition and on the auto-ignitable boundary were investigated and analyzed. From this work, it was found that the initial occurrence of auto-ignition is first induced by oblique shocks and then propagated upstream to the recirculation region, to establish a sustained flame. The auto-ignition performance can be improved by increasing the injection pressure and airflow total temperature. In addition, a cavity with a long length has benefits in controlling the flame spread from the induced state to a sustained state. The low-speed recirculating region created in the cavity is beneficial for the flame spread, which has the function of flame-holding and prevents the flame from being blown away. Full article

In this study, we developed a new reacting flow solver based on OpenFOAM (OF) and Cantera, with the capabilities of (i) dealing with detailed species transport and chemistry, (ii) integration using a well-balanced splitting scheme, and (iii) two advanced computational diagnostic methods. First of all, a flaw of the original OF chemistry model to deal with pressure-dependent reactions is fixed. This solver then couples Cantera with OF so that the robust chemistry reader, chemical reaction rate calculations, ordinary differential equations (ODEs) solver, and species transport properties handled by Cantera can be accessed by OF. In this way, two transport models (mixture-averaged and constant Lewis number models) are implemented in the coupled solver. Finally, both the Strang splitting scheme and a well-balanced splitting scheme are implemented in this solver. The newly added features are then assessed and validated via a series of auto-ignition tests, a perfectly stirred reactor, a 1D unstretched laminar premixed flame, a 2D counter-flow laminar diffusion flame, and a 3D turbulent partially premixed flame (Sandia Flame D). It is shown that the well-balanced property is crucial for splitting schemes to accurately capture the ignition and extinction events. To facilitate the understanding on combustion modes and complex chemistry in large scale simulations, two computational diagnostic methods (conservative chemical explosive mode analysis, CCEMA, and global pathway analysis, GPA) are subsequently implemented in the current framework and used to study Sandia Flame D for the first time. It is shown that these two diagnostic methods can extract the flame structure, combustion modes, and controlling global reaction pathways from the simulation data. Full article