A report is presented on the power performance of deep submicron AlGaN/GaN high electron mobility transistors grown by ammonia molecular beam epitaxy. At 10 GHz, 70% power-added-efficiency (PAE) and 3 W/mm power density were demonstrated at a drain bias of 20 V. At 30 GHz, 31% PAE and 6.5 W/mm power density were achieved at a drain bias of 40 V.

In-cylinder flow fields and their temporal evolution have strong effect on the combustion dynamics of internal combustion engines. Proper orthogonal decomposition is a statistical tool to analyze these flow fields by decomposing them into flow patterns (known as proper orthogonal decomposition modes) and corresponding coefficients with their contribution to the ensemble flow kinetic energy successively maximized. However, neither of the two prevailing proper orthogonal decomposition approaches satisfactorily describes the temporal behavior of the flow fields. The phase-dependent proper orthogonal decomposition approach is limited to analyzing spatial flow structures at a certain engine phase. The phase-invariant proper orthogonal decomposition approach attempts to account for both spatial and temporal variations, but at the expense of diminished statistical and physical significance. In this article, we seek to understand the temporal behavior of tumble flow fields by analyzing the evolution of low-order phase-dependent proper orthogonal decomposition modes over multiple crank angles. The concept of relevance index is first generalized to enable comparison between two vectorial fields of different sizes. This metric is then used to quantify the directional similarities between the two lowest proper orthogonal decomposition modes obtained at sequential crank angles. The mode shapes are observed to evolve gradually and naturally over most crank angles, but change significantly at certain crank angles during intake. The results indicate that each of the low-order modes features strong velocity fluctuations in different regions of the tumble plane, and different numbers of modes are needed to represent the dominant features of tumble flow at different engine phases. Based on this understanding, we propose to use the partial sum of those proper orthogonal decomposition modes and their coefficients to form a low-order approximation model of the in-cylinder tumble flow, in order to reduce flow field complexity and noise while retaining its major spatial and temporal features.

In this article, different manifold reduction techniques are implemented for the post-processing of Particle Image Velocimetry (PIV) images from a Spark Ignition Direct Injection (SIDI) engine. The methods are proposed to help make a more objective comparison between Reynolds-averaged Navier-Stokes (RANS) simulations and PIV experiments when Cycle-to-Cycle Variations (CCV) are present in the flow field. The two different methods used here are based on Singular Value Decomposition (SVD) principles where Proper Orthogonal Decomposition (POD) and Kernel Principal Component Analysis (KPCA) are used for representing linear and non-linear manifold reduction techniques. To the authors’ best knowledge, this is the first time a non-linear manifold reduction technique, such as KPCA, has ever been used in the study of in-cylinder flow fields. Both qualitative and quantitative studies are given to show the capability of each method in validating the simulation and incorporating CCV for each engine cycle. Traditional Relevance Index (RI) and two other previously developed novel indexes: the Weighted Relevance Index (WRI) and the Weighted Magnitude Index (WMI), are used for the quantitative study. The results indicate that both POD and KPCA show improvements in capturing the main flow field features compared to ensemble-averaged PIV experimental data and single cycle experimental flow fields while capturing CCV. Both methods present similar quantitative accuracy when using the three indexes. However, challenges were highlighted in the POD method for the selection of the number of POD modes needed for a representative reconstruction. When the flow field region presents a Gaussian distribution, the KPCA method is seen to provide a more objective numerical process as the reconstructed flow field will see convergence with an increasing number of modes due to its usage of Gaussian properties. No additional criterion is needed to determine how to reconstruct the main flow field feature. Using KPCA can, therefore, reduce the amount of analysis needed in the process of extracting the main flow field while incorporating CCV.

Computational fluid dynamics (CFD) simulations of the in-cylinder flow field are widely used in the design of internal combustion engines (ICEs) and must be validated against experimental measurements to enable a robust predictive capability. Such validation is complicated by the presence of both large-scale cycle-to-cycle variations and small-scale turbulent fluctuations in experimental measurements of in-cylinder flow fields. Reynolds averaged Navier-Stokes (RANS) simulations provide overall flow structures with acceptable accuracy and affordable computational cost for widespread industrial applications. Due to the nature of averaging physical parameters in RANS, its validation against experimental results obtained by particle image velocimetry (PIV) requires consideration of how best to average or filter the measured turbulent flows. In this paper, PIV measurements on the cross-tumble plane were recorded every five crank angle degrees for $300$ cycles during the intake process of a motored, optically accessible spark ignition direct injection (SIDI) engine. Several methods including ensemble averaging, speed-based averaging and low-order proper orthogonal decomposition (POD) reconstruction were applied to remove the fluctuations from experimental PIV vector fields and thus enable comparison to RANS simulations. Quantitative comparison metrics were used to evaluate the performances of each method in representing the intake jet. Recommendations are made on how to provide a fair validation between measured data and simulation results in highly fluctuating flow fields such as the engine intake jet.

Ammonia, a zero carbon energy vector, is under consideration for decarbonising marine and energy storage applications due to its high mass-based energy density compared to many alternatives. In addition, there is widespread existing supply and transportation infrastructure due to ammonia’s use as a fertiliser. When injected in its liquid form, however, ammonia behaves quite differently to traditional fuels due to its high saturation pressure and enthalpy of vaporisation, amongst other things. This means that fundamental data on ammonia sprays need to be collected in order to understand ammonia spray behaviour and calibrate models of ammonia sprays needed for design in the virtual world. Previous work on ammonia sprays has mostly focused on spray morphology at a macroscopic level (such as liquid penetration length). However, there are fewer studies of ammonia sprays at a microscopic level. In this study, liquid ammonia was injected into a constant-volume chamber using a direct injector at two injection pressures (100 bar and 150 bar) and a range of ambient pressures from 3–13 bar. This range of ambient conditions spans regimes from flash-boiling to non-flash-boiling, thereby enabling systematic investigation of the transition between these regimes. A laser diffraction technique was used for measuring the droplet sizes of the spray at different locations (in a cylindrical volume with a diameter of 10 mm) within the spray plume at 10 kHz, and the nominal droplet sizes were quantified by the Sauter Mean Diameter (SMD). These SMD values provided, at a microscopic level, an insight of the atomisation of the spray as it left the nozzle and penetrated into an environment with different densities. It was found that the tested injector leads to a breakup dominant spray behaviour with liquid ammonia and hence the SMD values decrease as ambient pressure increases. In addition, the droplets are generally smaller at the outer edge of the spray plume compared to the inner part and both the injection pressure and injection duration have a strong effect on the droplet sizes.