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Optical motion capture (OMC) is a relatively new experimental tool used in many branches of science and engineering. Despite OMC’s widespread use, literature and practical procedures on the quantification of error and uncertainty in OMC systems for rigid bodies are currently underdeveloped. However, in most studies involving error and uncertainty quantification, the OMC volumes are relatively small (maximum length of 2m in any dimension) and involve expensive experimental apparatuses. Therefore, a cost-effective procedure to quantify the positional errors and uncertainties present in a large volume OMC system is presented. The procedure utilizes the kinematics of a wooden block traveling through air to predict errors and uncertainties in the OMC system by only collecting trajectory data.

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

Performance of a natural gas two-stroke engine incorporated in a 1-kW free-piston oscillating Linear Engine Alternator (LEA) - a household electricity generator - was investigated under different resonant frequencies for pre-design phase purposes. To increase the robustness, power density, and thermal efficiencies, the crank mechanism in free-piston LEA is omitted and all moving parts of the generator operate at a fixed resonant frequency. Flexure springs are the main source of the LEA’s stiffness and the mass-spring dynamics dominates the engine’s speed. The trade-off between the engine’s performance, mass-spring system limits, and power and efficiency targets versus the LEA speed is very crucial and demands a careful investigation specifically at the concept design stages to find the optimum design parameters and operating conditions. CFD modeling was performed to analyze the effects of resonant frequency on the engine’s gas exchange behavior.

Methods to quantify the energy losses within linear motion devices that included flexural springs as the main suspension component were investigated. The methods were applied to a two-stroke free-piston linear engine alternator (LEA) as a case study that incorporated flexure springs to add stiffness to the mass-spring system. Use of flexure springs is an enabling mechanism for improving the efficiency and lifespan in linear applications e.g. linear engines and generators, cryocoolers, and linear Stirling engines. The energy loss due to vibrations and windage effects of flexure springs in a free piston LEA was investigated to quantify possible energy losses. A transient finite element solver was used to determine the effects of higher modes of vibration frequencies of the flexure arms at an operational frequency of 65 Hz. Also, a computational fluid dynamics (CFD) solver was used to determine the effects of drag force on the moving surfaces of flexures at high frequencies.

3D CFD spark-ignition IC engine simulations are extremely complex for the regular user. Truly-predictive CFD simulations for the turbulent flame combustion that solve fully coupled transport/chemistry equations may require large computational capabilities unavailable to regular CFD users. A solution is to use a simpler phenomenological model such as the G-equation that decouples transport/chemistry result. Such simulation can still provide acceptable and faster results at the expense of predictive capabilities. While the G-equation is well understood within the experienced modeling community, the goal of this paper is to document some of them for a novice or less experienced CFD user who may not be aware that phenomenological models of turbulent flame combustion usually require heavy tuning and calibration from the user to mimic experimental observations.

Computational fluid dynamics (CFD) model has been widely applied in internal combustion (IC) engine research. The integration of chemical kinetic model with CFD provides an opportunity for researchers to investigate the detailed chemical reactions for better understanding the combustion process of IC engines. However, the simulation using CFD has generally focused on the examination of primary parameters, such as temperature and species distributions. The detailed investigation on chemical reactions is limited. This paper presents the development of a post-processing tool capable of calculating the rate of production (ROP) of interested species with the known temperature, pressure, and concentration of each species in each cell simulated using CONVERGE-SAGE CFD model.

Recent free piston engine research reported in the literature has included development efforts for single and dual cylinder devices through both simulation and prototype operation. A single cylinder, spring opposed, oscillating linear engine and alternator (OLEA) is a suitable architecture for application as a steady state generator. Such a device could be tuned and optimized for peak efficiency and nominal power at unthrottled operation. One of the significant challenges facing researchers is startup of the engine. It could be achieved by operating the alternator in a motoring mode according to the natural system resonant frequency, effectively bouncing the translator between the spring and cylinder, increasing stroke until sufficient compression is reached to allow introduction of fuel and initiation of combustion. To study the natural resonance of the OLEA, a numeric model has been built to simulate multiple cycles of operation.

The Wankel engine for Unmanned Aerial Vehicle (UAV) applications delivers advantages vs. piston engines of simplicity, smoothness, compactness and high power-to-weight ratio. The use of computational fluid dynamic (CFD) and computer aided engineering (CAE) tools may permit to address the major downfalls of these engines, namely the slow and incomplete combustion due to the low temperatures and the rotating combustion chambers. The paper proposes the results of CAD/CFD/CAE modelling of a Wankel engine featuring tangential jet ignition to produce faster and more complete combustion.

Unmanned Aerial Vehicles (UAV) require simple and reliable engines of high power to weight ratio. Wankel and two stroke engines offer many advantages over four stroke engines. A two stroke engines featuring crank case scavenging, precise oiling, direct injection and jet ignition is analyzed here by using CAD, CFD and CAE tools. Results of simulations of engine performances are shown in details. The CFD analysis is used to study fuel injection, mixing and combustion. The CAE model then returns the engine performances over the full range of loads and speeds with the combustion parameters given as an input. The use of asymmetric rather than symmetric port timing and supercharging scavenging is finally suggested as the best avenue to further improve power density and fuel conversion efficiency.

Scale-resolving turbulence modeling for engine flow simulation has constantly increased its popularity in the last decade. In contrast to classical RANS modeling, LES-like approaches are able to resolve a larger number of unsteady flow features. In principle, this capability allows to accurately predict some of the key parameters involved in the development and optimization of modern engines such as cycle-to-cycle variations in a DI engine. However, since multiple simulated engine cycles are required to extract reliable flow statistics, the spatial and temporal resolution requirements of pure LES still represent a severe limit for its wider application on realistic engine geometries. In this context, Hybrid URANS-LES methodologies can therefore become a potentially attractive option. In fact, their task is to preserve the turbulence scale-resolving in the flow core regions but at a significantly lower computational cost compared to standard LES.

Diesel particulate filters (DPFs) are recognized as the most efficient technology for particulate matter (PM) reduction, with filtration efficiencies in excess of 90%. Design guidelines for DPFs typically are: high removal efficiency, low pressure drop, high durability and capacity to resist high temperature excursions during regeneration events. The collected mass inside the trap needs to be periodically oxidized to regenerate the DPF. Thus, an in-depth understanding of filtration and regeneration mechanisms, together with the ability of predicting actual DPF conditions, could play a key role in optimizing the duration and number of regeneration events in case of active DPFs. Thus, the correct estimation of soot loading during operation is imperative for effectively controlling the whole engine-DPF assembly and simultaneously avoidingany system failure due to a malfunctioning DPF. A viable way to solve this problem is to use DPF models.

Dual primary full-flow dilution tunnels represent an integral part of a heavy-duty transportable emissions measurement laboratory designed and constructed to comply with US Code of Federal Regulations (CFR) 40 Part 1065 requirements. Few data exist to characterize the evolution of particulate matter (PM) in full scale dilution tunnels, particularly at very low PM mass levels. Size distributions of ultra-fine particles in diesel exhaust from a naturally aspirated, 2.4 liter, 40 kW ISUZU C240 diesel engine equipped with a diesel particulate filter (DPF) were studied in one set of standard primary and secondary dilution tunnels with varied dilution ratios. Particle size distribution data, during steady-state engine operation, were collected using a Cambustion DMS500 Fast Particulate Spectrometer. Measurements were made at four positions that spanned the tunnel cross section after the mixing orifice plate for the primary dilution tunnel and at the outlet of the secondary dilution tunnel.

To meet the ignition system needs of large bore high pressure lean burn natural gas engines a laser diode side pumped passively Q-switched laser igniter was designed and tested. The laser was designed to produce the optical intensities needed to initiate ignition in a lean burn high brake mean effective pressure (BMEP) engine. The experimentation explored a variety of optical and electrical input parameters that when combined produced a robust spark in air. The results show peak power levels exceeding 2 MW and peak focal intensities above 400 GW/cm2. Future research avenues and current progress with the initial prototype are presented and discussed.

Evermore demanding market and legislative pressures require stationary lean burn natural gas engines to operate at higher efficiencies and reduced levels of emissions. Higher in-cylinder pressures and leaner air/fuel ratios are required in order to meet these demands. The performance and durability of spark plug ignition systems suffer as a result of the increase in spark energy required to maintain suitable engine operation under these conditions. Advancing the state of the art of ignition systems for these engines is critical to meeting increased performance requirements. Laser-spark ignition has shown potential to improve engine performance and ignition system durability to levels required meet or exceed projected requirements. This paper discusses testing which extends previous efforts [1] to include constant fueling knock, misfire, thermal efficiency, and NOx emissions mapping of a single cylinder lean burn natural gas engine.

The downwash of the prop-rotor blades of the Bell/Boeing V-22 “Osprey” in hover mode creates an undesirable negative lift on the wing of the aircraft. This downforce can be reduced through a number of methods. Neglecting all other effects, such as power requirements, this research investigated the feasibility of using circulation control, through blowing slots on the leading and trailing edge of the airfoil to reduce the wake profile under the wing. A model was built at West Virginia University (WVU) and tested in a Closed Loop Wind Tunnel. The airfoil was placed normal to the airflow using the tunnel air to simulate the vertical component of the downwash experienced in hover mode. The standard hover mode flap angle of 67 degrees was used throughout the testing covered in this paper. All of these tests were conducted at a free stream velocity of 59 fps, and the baseline downforce on the model was measured to be 5.45 lbs.

Two and three-dimensional test cases were simulated using a detailed kinetic mechanism for di-methyl ether to represent methane combustion. A piston-bowl assembly for the compression and expansion strokes with combustion has been simulated at 1500 RPM. A fine grid was used for the 2-D simulations and a rather coarse grid was used for the 3-D calculations together with a k-ε subgrid-scale turbulence model and a partially stirred reactor model with three time scales. Ignition was simulated artificially by increasing the temperature at one point inside the cylinder. The results of these simulations were compared with experimental results. The simulation involved an engine with a homogeneous charge of methane as fuel. Results indicate that pressure fluctuations were captured some time after the ignition started, which indicates knock conditions.

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.

The KIVA family of codes (Amsden et al., 1989,1993,1997) are being used by many researchers for internal combustion engine simulation. For these codes to continue to be useful, improvements need to be made to make them more efficient. One of the most CPU intensive parts of these codes is the pressure solver. Improvement in the convergence of the pressure solver could have a significant effect on the performance of the overall code. This paper presents the theory behind the matrix solution procedure utilized by KIVA. A different approach to preconditioning is then presented. When implemented, it is shown that the overall CPU time required to perform a simulation is reduced by up to 20% for pressure dominated three-dimensional simulations. This is accomplished without an increase in memory allocation.