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We propose a novel Split Ring Resonator (SRR) metamaterial capable of achieving a total (or complete) bandgap in the material’s band structure, thereby reflecting airborne and structure-borne noise in a targeted frequency range. Electric Vehicles (EVs) experience tonal excitation arising from switching frequencies associated with motors and inverters, which can significantly affect occupant perception of vehicle quality. Recently proposed metamaterial designs reflect airborne noise and structure-borne transverse waves over a band of frequencies, but do not address structure-borne longitudinal waves in the same band. To achieve isolation of acoustic, transverse, and longitudinal elastic waves associated with tonal frequencies, we propose a metamaterial super cell with transverse and longitudinal resonant frequencies falling in a total bandgap. We calculate the resonant frequencies and corresponding mode shapes using finite element (FE) modal analysis.

Climate change mitigation is the main challenge for the automotive industry, as the government issues legislation to combat CO2 emissions. In addition to electrification and battery electric vehicles, using low-carbon and zero-carbon fuels in Internal Combustion (IC) engines can also be an effective way to reach net zero-carbon transport. This study investigated and compared the combustion characteristics, performance and emissions of a highly boosted spark ignition (SI) engine fuelled with EU VI 95 RON E10 gasoline and blends of second-generation bio-gasoline with different ethanol contents of 5% (E5), 10% (E10), and 20% (E20). The single-cylinder SI engine was equipped with a centrally mounted high-pressure injector and supplied externally boosted air. Engine experiments were conducted at 2000 RPM and 3000 RPM with low and high load operations.

An intelligent connected vehicle (ICV) swarm system that includes N vehicles is considered. Based on the special properties of potential functions, a kinematic model describing the swarm performances is proposed, which allows all vehicles to enclose the tracking target and show both tracking and formation characteristics. Treating the performances as the desired constraints, the analytical form of constraint forces can be obtained inspired by the Udwadia-Kalaba approaches. A special approach of uncertainty decomposition to deal with uncertain interferences is proposed, and a switching-type robust control method is addressed for each vehicle agent in the swarm system. The features and validity of the addressed control are demonstrated in the numerical simulations.

The automobile industry is under intense pressure to reduce carbon dioxide (CO2) emissions of vehicles. There is also increasing pressure to reduce the other tail-pipe emissions from vehicles to combat air pollution. Electric powertrains offer great potential for eliminating tailpipe CO2 and all other tailpipe emissions. However, current battery technology and recharging infrastructure still present limitations for some applications, where a continuous high-power demand is required. Furthermore, not all markets have the infrastructure to support a sizeable electric fleet and until the grid energy generation mix is of a sufficiently low carbon intensity, then significant vehicle life-cycle CO2 savings could not be realized by the Battery Electric Vehicles. This investigation examines the effects of combustion, efficiencies, and emissions of two alcohol fuels that could help to significantly reduce CO2 in both tailpipe and the whole life cycle.

An appropriate energy management strategy can further reduce the fuel consumption of P2 hybrid electric vehicles (HEV) with simple hybrid configuration and low cost. The rule-based real-time energy management strategy dominates the energy management strategies utilized in commercial HEVs, due to its robustness and low computational loads. However, its performance is sensitive to the setting of parameters and control actions. To further improve the fuel economy of a P2 HEV, the energy management strategy of the HEV has been re-designed based on the globally optimal control theory. An optimization strategy model based on the longitudinal dynamics of the vehicle and Bellman’s dynamic programming algorithm was established in this research and an optimal power split in the dual power sources including an internal combustion engine (ICE) and an electric machine at a given driving cycle was used as a benchmark for the development of the rule-based energy management strategy.

Pumping Mean Effective Pressure (PMEP) is the main factor limiting the improvement of thermal efficiency in a spark-ignition (SI) engine under low load. One of the ways to reduce the pumping loss under low load is to use Cylinder DeActivation (CDA). The CDA aims at reducing the firing density (FD) of the SI engine under low load operation and increasing the mass of air-fuel mixture within one cycle in one cylinder to reduce the throttling effect and further reducing the PMEP. The multi-stroke cycles can also reduce the firing density of the SI engine after some certain reasonable design, which is feasible to improve the thermal efficiency of the engine under low load in theory. The research was carried out on a calibrated four-cylinder SI engine simulation platform. The thermal efficiency improvements of the 6-stroke cycle and 8-stroke cycle to the engine performance were studied compared with the traditional 4-stroke cycle under low load conditions.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

Two-scale command shaping is a recently proposed feedforward control method aimed at mitigating undesirable vibrations in nonlinear systems. The TSCS strategy uses a scale separation to cancel oscillations arising from nonlinear behavior of the system, and command shaping of the remaining linear problem. One promising application of TSCS is in reducing engine restart and shutdown vibrations found in conventional and in hybrid electric vehicle powertrains equipped with start-stop features. The efficacy of the TSCS during internal combustion engine restart has been demonstrated theoretically and experimentally in the authors’ prior works. The present article presents simulation results and describes the verified experimental apparatus used to study TSCS as applied to the ICE shutdown case. The apparatus represents a typical HEV powertrain and consists of a 1.03 L three-cylinder diesel ICE coupled to a permanent magnet alternating current electric machine through a spur gear coupling.

Low combustion efficiency and high hydrocarbon emissions at low loads are key issues of natural gas and diesel (NG-diesel) dual fuel engines. For better engine performance, two diesel-spray-orientated (DSO) bowls were developed based on the existing diesel injector of a heavy-duty diesel engine with the purpose of placing more combustible natural gas/air mixture around the diesel spray jets. A protrusion-ring was designed at the rim of the piston bowl to enhance the in-cylinder flame propagation. Numerical simulations were conducted for a whole engine cycle at engine speed of 1200 r/min and indicated mean effective pressure (IMEP) of 0.6 MPa. Extended coherent flame model 3 zones (ECFM-3Z) combustion model with built-in soot emissions model was employed. Simulation results of the original piston bowl agreed well with the experimental data, including in-cylinder pressure and heat released rate (HRR), as well as soot and methane emissions.

With the advent of autonomous vehicles, the human driver’s attention will slowly be relinquished from the driving task. It will allow drivers to participate in more non-driving related activities, such as engaging with information and entertainment systems. However, the automated driving system would need to notify the driver of upcoming points-of-interest on the road when the driver’s attention is focused on their screen rather than on the road or driving display. In this paper, we investigated whether providing directional alerts for an upcoming point-of-interest (POI) in or around the user’s active screen can augment their ability in relocating their visual attention to the POI on the road when traveling in a vehicle with Conditional Driving Automation. A user study (N = 15) was conducted to compare solutions for alerts that presented themselves in the participants’ central and peripheral field of view.

Since, ice accretion can significantly degrade the performance and the stability of an airborne vehicle, it is imperative to be able to model it accurately. While ice accretion studies have been performed on airplane wings and helicopter blades in abundance, there are few that attempt to model the process on more complex geometries such as fuselages. This paper proposes a methodology that extends an existing in-house Extended Messinger solver to complex geometries by introducing the capability to work with unstructured grids and carry out spatial surface streamwise marching. For the work presented here commercial solvers such as STAR-CCM+ and ANSYS Fluent are used for the flow field and droplet dispersed phase computations. The ice accretion is carried out using an in-house icing solver called GT-ICE. The predictions by GT-ICE are compared to available experimental data, or to predictions by other solvers such as LEWICE and STAR-CCM+.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.

Vertical takeoff and landing vehicle platforms with many small rotors are gaining importance for small UAVs as well as distributed electric propulsion for larger vehicles. To predict vehicle performance, it must be possible to gauge interaction effects. These rotors operate in the less-known regime of low Reynolds number, with different blade geometry. As a first step, two identical commercial UAV rotors from a flight test program are studied in isolation, experimentally and computationally. Load measurements were performed in Georgia Tech’s 2.13 m × 2.74 m wind tunnel. Simulations were done using the RotCFD solver which uses a Navier-Stokes wake computation along with rotor-disc loads calculation using low-Reynolds number blade section data. It is found that in hover, small rotors available in the market vary noticeably in performance at low rotor speeds, the data converging at higher RPM and Reynolds number.

Engine downsizing has established itself as one of the most successful strategies to reduce fuel consumption and pollutant emissions in the automotive field. To this regard, a major role is played by turbocharging, which allows an increase in engine power density, so reducing engine size and weight. However, the need for turbocharging imposes some issues to be solved. In the attempt of mitigating turbo lag and poor low-end torque, many solutions have been presented in the open literature so far, such as: low inertia turbine wheels and variable geometry turbines; or even more complex concepts such as twin turbo and electrically assisted turbochargers. None of them appears as definitive, though. As a possible way of reducing turbine rotor inertia, and so the turbo lag, also the change of turbine layout has been investigated, and it revealed itself to be a viable option, leading to the use of mixed-flow turbines.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

In decade years, Spark Ignition-Controlled Auto Ignition (SI-CAI) hybrid combustion, also called Spark Assisted Compression Ignition (SACI) has shown its high-efficiency and low emissions advantages. However, high dilution causes the problem of unstable initial ignition and flame propagation, which leads to high cyclic variation of heat release and IMEP. The instability of SI-CAI hybrid combustion limits its dilution degree and its ability to improve the thermal efficiency. In order to solve instability problems and expand the dilution boundary of hybrid combustion, micro flame ignition (MFI) strategy is applied in gasoline hybrid combustion engines. Small amount of Dimethyl Ether (DME) chosen as the ignition fuel is injected into cylinder to form micro flame kernel, which can stabilize the ignition combustion process.

Poppet-valve two-stroke gasoline engines can increase the specific power of their four-stroke counterparts with the same displacement and hence decrease fuel consumption. However, knock may occur at high loads. Therefore, the combustion with stratified lean mixture was proposed to decrease knock tendency and improve combustion stability in a poppet-valve two-stroke direct injection gasoline engine. The effect of intake pressure and split injection on fuel distribution, combustion and knock intensity in lean mixture conditions at high loads was simulated with a three-dimensional computational fluid dynamic software. Simulation results show that with the increase of intake pressure, the average fuel-air equivalent ratio in the cylinder decreases when the second injection ratio was fixed at 70% at a given amount of fuel in a cycle.

In order to improve understanding of the primary atomization process for diesel-like sprays, a collaborative experimental and computational study was focused on the near-nozzle spray structure for the Engine Combustion Network (ECN) Spray D single-hole injector. These results were presented at the 5th Workshop of the ECN in Detroit, Michigan. Application of x-ray diagnostics to the Spray D standard cold condition enabled quantification of distributions of mass, phase interfacial area, and droplet size in the near-nozzle region from 0.1 to 14 mm from the nozzle exit. Using these data, several modeling frameworks, from Lagrangian-Eulerian to Eulerian-Eulerian and from Reynolds-Averaged Navier-Stokes (RANS) to Direct Numerical Simulation (DNS), were assessed in their ability to capture and explain experimentally observed spray details. Due to its computational efficiency, the Lagrangian-Eulerian approach was able to provide spray predictions across a broad range of conditions.

This presentation is an assessment of the turbulence-stress scale-similarity in an IC engine, which is used for modeling subgrid dissipation in LES. Residual stresses and Leonard stresses were computed after applying progressively smaller spatial filters to measured and simulated velocity distributions. The velocity was measured in the TCC-II engine using planar and stereo PIV taken in three different planes and with three different spatial resolutions, thus yielding two and three velocity components, respectively. Comparisons are made between the stresses computed from the measured velocity and stress computed from the LES resolved-scale velocity from an LES simulation. The results present the degree of similarity between the residual stresses and the Leonard stresses at adjacent scales. The specified filters are systematically reduced in size to the resolution limits of the measurements and simulation.

The outward-opening piezoelectric injector can achieve stable fuel/air mixture distribution and multiple injections in a single cycle, having attracted great attentions in direct injection gasoline engines. In order to realise accurate predictions of the gasoline spray with the outward-opening piezoelectric injector, the computational fluid dynamic (CFD) simulations of the gasoline spray with different droplet breakup models were performed in the commercial CFD software STAR-CD and validated by the corresponding measurements. The injection pressure was fixed at 180 bar, while two different backpressures (1 and 10 bar) were used to evaluate the robustness of the breakup models. The effects of the mesh quality, simulation timestep, breakup model parameters were investigated to clarify the overall performance of different breakup model in modeling the gasoline sprays.