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Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Abstract A detailed investigation was carried out on the performance, combustion, and emissions of a single-cylinder direct injection hydrogen spark ignition (SI) engine with either a side-mounted direct injection (SDI) or a centrally installed direct injection (CDI) injector. The first part of the study analyzed the performance and emissions characteristics of CDI and SDI engine operations with different injection timings and pressures. This was followed by comparing the engine’s performance and emissions of the CDI and SDI operations at different engine speeds and relative air-to-fuel ratios (lambda) with the optimized injection pressure and timings. Furthermore, the performance and emission attributes of the hydrogen engine with the CDI and SDI setups were conducted at a fixed λ value of 2.75 across a broad spectrum of engine loads. The study’s main outcome demonstrates that both direct injection systems produced near-zero CO2, CO, and HC emissions.

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Abstract Increasing engine efficiency is essential to reducing emissions, which is a priority for automakers. Unconventional modes such as boosted and highly dilute operation have the potential to increase engine efficiency but suffer from stability concerns and cyclic variability. To aid engineers in designing ignition systems that reduce cyclic variability in such engine operation modes, reliable and accurate spark-ignition models are necessary. In this article, a Lagrangian–Eulerian spark-ignition (LESI) model is used to simulate electrical discharge, spark channel elongation, and ignition in inert or reacting crossflow within a combustion vessel, at different pressures, flow speeds, and dilution rates. First the model formulation is briefly revisited. Then, the experimental and simulations setups are presented.

Abstract In this article, an improved brake cooling simulation method is introduced. By this method, the vehicle parameters, such as weight, height of the center of gravity, wheelbase, and the like can be included to calculate the braking thermal load under different operating conditions. The effect of the brake kinetic energy regeneration (BKER) on the braking thermal load can also be calculated by this method. The calculated braking thermal load is then input to a coupled 3D simulation model to conduct flow and thermal simulation to calculate brake disc temperature. It is demonstrated that by this simulation method, the difference between the brake disc temperatures obtained from simulation and vehicle test can be controlled below 5%.

Abstract Improving thermal efficiency of an internal combustion engine is one of the most cost-effective ways to reduce life cycle-based CO2 emissions for transportation. Lean burn technology has the potential to reach high thermal efficiency if simultaneous low NOx, HC, and CO emissions can be achieved. Low NOx can be realized by ultra-lean (λ ≥ 2) spark-ignited combustion; however, the HC and CO emissions can increase due to slow flame propagation and high combustion variability. In this work, we introduce a new combustion concept called turbulent jet-controlled compression ignition, which utilizes multiple turbulent jets to ignite the mixture and subsequently triggers end gas autoignition. As a result, the ultra-lean combustion is further improved with reduced late-cycle combustion duration and enhanced HC and CO oxidation. A low-cost passive prechamber is innovatively fueled using a DI injector in the main combustion chamber through spray-guided stratification.

Abstract Composite ceramic brake discs are made of ceramic material reinforced with carbon fibers and offer exceptional advantages that translate directly into higher vehicle performance. In the case of an electric vehicle, it could increase the range of the vehicle, and in the case of conventional internal combustion engine vehicles, it means lower fuel consumption (and consequently lower CO2 emissions). These discs are typically characterized by complex internal geometries, further complicated by the presence of drilling holes on both friction surfaces. To estimate the aerothermal performance of these discs, and for the thermal management of the vehicle, a reliable model for predicting the air flowing across the disc channels is needed. In this study, a real carbon-ceramic brake disc with drilling holes was investigated in a dedicated test rig simulating the wheel corner flow conditions experimentally using the particle image velocimetry technique and numerically.

Abstract This study proposes a machine learning tabulation (MLT) method that employs deep neural networks (DNNs) to predict ignition delay and knock propensity in spark ignition (SI) engines. The commonly used Arrhenius model and Livengood–Wu integral for fast knock prediction are not accurate enough to account for residual gas species and may require adjustments or modifications to account for specific engine characteristics. Detailed kinetics modeling is computationally expensive, so the MLT approach is introduced to solve these issues. The MLT method uses precalculated thermochemical states of the mixture that are clustered based on a combustion progress variable. Hundreds of DNNs are trained with the stochastic Levenberg–Marquardt (SLM) optimization algorithm, reducing training time and memory requirements for large-scale problems. MLT has high interpolation accuracy, eliminates the need for table storage, and reduces memory requirements by three orders of magnitude.

Abstract One of the key problems of battery electric vehicles is the risk of severe range reduction in winter conditions. Technologies such as heat pump systems can help to mitigate such effects, but finding adequate heat sources for the heat pump sometimes can be a problem, too. In cold ambient conditions below −10°C and for a cold-soaked vehicle this can become a limiting factor. Storing waste heat or excess cold when it is generated and releasing it to the vehicle thermal management system later can reduce peak thermal requirements to more manageable average levels. In related architectures it is not always necessary to replace existing electric heaters or conventional air-conditioning systems. Sometimes it is more efficient to keep them and support them, instead. Accordingly, we show, how latent heat storage can be used to increase the efficiency of existing, well-established heating and cooling technologies without replacing them.

Abstract The numerical study presented in this article is based on an automotive diesel engine (2.8 L, 4-cylinder, turbocharged), considering a NG–H2 blend with 30 vol% of H2, ignited by multiple diesel fuel injections. The 3D-CFD investigation aims at improving BTE, CO, and UHC emissions at low load, by means of an optimization of the diesel fuel injection strategy and of the in-cylinder turbulence (swirl ratio, SR). The operating condition is 3000 rpm – BMEP = 2 bar, corresponding to about 25% of the maximum load of a gen-set engine, able to deliver up to 83 kW at 3000 rpm (rated speed). The reference diesel fuel injection strategy, adopted in all the previous numerical and experimental studies, is a three-shot mode. The numerical optimization carried out in this study consisted in finding the optimal number of injections per cycle, as well as the best timing of each injection and the fuel mass split among the injections.

Abstract Limited fossil fuel resources and carbonaceous greenhouse gas emissions are two major problems the world faces today. Alternative fuels can effectively power internal combustion engines to address these issues. Methanol can be an alternative to conventional fuels, particularly to displace gasoline in spark ignition engines. The physicochemical properties of methanol are significantly different than baseline gasoline and fuel mixture-aim lambda; hence methanol-fueled engines require modifications in the fuel injection parameters. This study optimized the fuel injection quantity, spark timing, and air–fuel ratio for M85 (85% v/v methanol + 15% v/v gasoline) fueling of a port fuel-injected single-cylinder 500 cc motorcycle test engine. Comparative engine performance, combustion, and emissions analyses were performed for M85 and baseline gasoline.

Abstract Methanol is emerging as an alternate internal combustion engine fuel. It is getting attention in countries such as China and India as an emerging transport fuel. Using methanol in spark ignition engines is easier and more economical than in compression ignition engines via the blending approach. M85 (85% v/v methanol and 15% v/v gasoline) is one of the preferred blends with the highest methanol concentration. However, its physicochemical properties significantly differ from gasoline, leading to challenges in operating existing vehicles. This experimental study addresses the challenges such as cold-start operation and poor throttle response of M85-fueled motorcycle using a port fuel injection engine. In this study, M85-fueled motorcycle prototype is developed with superior performance, similar/better drivability, and lower emissions than a gasoline-fueled port-fuel-injected motorcycle.

Abstract Turbocharged spark-ignition (SI) engines, owing to frequent engine knocking events, utilize retarded spark timing that causes combustion inefficiency, and high turbine inlet temperature (Trb-In T) levels. Fuel enrichment is implemented at high power levels to prevent excessive Trb-In T levels, resulting in an additional fueling penalty and higher CO emissions. In current times, fuel-enrichment reductions are of high strategic importance for engine manufacturers to meet the imminent emissions regulations. To that end, the authors investigated the divided exhaust period (DEP) concept in a 2.2 L turbocharged SI engine with a geometric compression ratio of 14 by decoupling blowdown (BD) and scavenge (SC) events during the exhaust process. Using a validated 1D engine model, the authors first analyzed the DEP concept in terms of pumping mean effective pressure (PMEP) and engine knocking (KI) reduction.

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Abstract This study investigates the behavior of pre-chamber knock in comparison to traditional spark ignition engine knock, using a modified constant-volume gasoline engine with an optically accessible piston. The aim is to provide a deeper understanding of pre-chamber knock combustion and its potential for mitigating knock. Five passive pre-chambers with different nozzle diameters, volumes, and nozzle numbers were tested, and nitrogen dilution was varied from 0% to 10%. The stochastic nature of knock behavior necessitates the use of statistical methods, leading to the proposal of a high-frequency band-pass filter (37–43 kHz) as an alternative pre-chamber knock metric. Pre-chamber knock combustion was found to exhibit fewer strong knock cycles compared to SI engines, indicating its potential for mitigating knock intensity. High-speed images revealed pre-chamber knock primarily occurs near the liner, where end-gas knock is typically exhibited.

Abstract Electrical connection plays an important role in not only direct heat transfer, but also the transmission of electric energy and the transformation of electrothermal effect in the parallel battery modules. The thermal propagation simulation research model was established based on the equivalent circuit and thermal runaway experimental research of a module formed by four parallel cells, which superimposes the discharge process and corresponding electrothermal effect in the process of thermal runaway and thermal propagation, and pays attention to the SoC (state of charge) state and corresponding thermal runaway energy release changes after cell discharged. Thermal runaway and propagation characteristics of parallel and non-parallel battery modules were analyzed and results showed that without considering the energy exchange between the system and the environment, the parallel battery module will accelerate the process of thermal propagation.

Abstract Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system.

Abstract For spacecraft with high power consumption, it is reasonable to build the thermal control system based on a two-phase mechanically pumped loop. The heat-controlled accumulator is a key element of the two-phase mechanically pumped loop, which allows for the control of pressure in the loop and maintains the required level of coolant boiling temperature or cavitation margin at the pump inlet. There can be two critical modes of loop operation where the ability to control pressure will be lost. The first critical mode occurs when the accumulator fills with liquid at high heat loads. The second critical mode occurs when the accumulator is at low heat loads and partial loss of coolant, for example, due to the leak caused by micrometeorite breakdown. Both modes are caused by insufficient accumulator volume or working fluid charge.

Abstract The objective of this study was to compare head, neck, and chest injury risks between front and rear-seated Hybrid III 50th-percentile male anthropomorphic test devices (ATDs) during matched frontal impacts. Seven vehicles were converted to rear seat test bucks (two sedans, three mid-size SUVs, one subcompact SUV, and one minivan) and then used to perform sled testing with vehicle-specific frontal NCAP acceleration pulses and a rear seated (i.e., second row) Hybrid III 50th male ATD. Matched front seat Hybrid III 50th male ATD data were obtained from the NHTSA Vehicle Crash Test Database for each vehicle. HIC15, Nij, maximum chest acceleration, and maximum chest deflection were compared between the front and rear seat tests, as well as between vehicles with conventional and advanced three-point belt restraint systems in the rear seat. Additionally, a modified version of the NCAP frontal star rating was calculated for the front and rear seat tests.

Abstract Due to the incoming phase out of fossil fuels from the market in order to reduce the carbon footprint of the automotive sector, hydrogen-fueled engines are candidate mid-term solution. Thanks to its properties, hydrogen promotes flames that poorly suffer from the quenching effects toward the engine walls. Thus, emphasis must be posed on the heat-up of the oil layer that wets the cylinder liner in hydrogen-fueled engines. It is known that motor oils are complex mixtures of a number of mainly heavy hydrocarbons (HCs); however, their composition is not known a priori. Simulation tools that can support the early development steps of those engines must be provided with oil composition and properties at operation-like conditions. The authors propose a statistical inference-based optimization approach for identifying oil surrogate multicomponent mixtures. The algorithm is implemented in Python and relies on the Bayesian optimization technique.