Large eddy simulation of an ignition front in a heavy duty partially premixed combustion engine

In partially premixed combustion engines high octane number fuels are injected into the cylinder during the late part of the compression cycle, giving the fuel and oxidizer enough time to mix into a desirable stratified mixture. If ignited by auto-ignition such a gas composition can react in a combustion mode dominated by ignition wave propagation. 3D-CFD modeling of such a combustion mode is challenging as the rate of fuel consumption can be dependent on both mixing history and turbulence acting on the reaction wave. This paper presents a large eddy simulation (LES) study of the effects of stratification in scalar concentration (enthalpy and reactant mass fraction) due to large scale turbulence on the propagation of reaction waves in PPC combustion engines. The studied case is a closed cycle simulation of a single cylinder of a Scania D13 engine running PRF81 (81% iso-octane and 19% n-heptane). Two injection timings are investigated; start of injection at -17 CAD aTDC and -30 CAD aTDC. One-equation transported turbulence sub-grid closure is used for the unresolved momentum and scalar fluxes and the fuel spray is modelled using a Lagrangian particle tracking (LPT) approach. Initial flow conditions (prior to intake valve closing) are generated using a scale forcing method with a prescribed large-scale swirl mean flow motion. Fuel reactivity is modeled using finite rate chemistry based on a skeletal chemical kinetic mechanism (44 species, 140 reactions). The results are compared with optical engine experimental data and satisfactory agreement with the experiments is obtained in terms of the liquid spray length, cylinder pressure trace and ignition location. A majority of the fuel consumption is found to be in ignition fronts where small variations in temperature at low fuel concentrations are observed to cause large stratification in ignition delay time

Incidence of central nervous system metastases in patients with human epidermal growth factor receptor 2-positive metastatic breast cancer treated with trastuzumab: A meta-analysis

This study aimed to estimate the incidence of central nervous system (CNS) metastases in patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) treated with trastuzumab. Studies were identified through a literature search of electronic databases. Random-effects meta-analyses were performed to estimate the incidence rate of CNS metastases, trastuzumab therapy duration, and time from trastuzumab therapy to CNS metastasis diagnosis. A meta-analysis of odds ratios was performed to evaluate the significance of a difference in CNS metastasis incidence between patients with and without trastuzumab treatment. Thirty studies (8121 trastuzumab-treated and 3972 control patients) were included. The follow-up duration was 18.9 months (95% confidence interval [CI]: 13.8, 24.1). The trastuzumab treatment duration was 9.0 months (95% CI: 7.0, 11.0). The median interval between the start of trastuzumab therapy and CNS metastasis diagnosis was 12.2 months (95% CI: 9.5, 14.7). The incidence of CNS metastasis after the start of trastuzumab therapy was 22% (95% CI: 16, 27). The incidence of CNS metastases was significantly higher in trastuzumab-treated than in non-trastuzumab-treated patients (odds ratio: 1.39 [95% CI: 1.06, 1.82], p=0.02). The survival time from the start of the study was 23.4 months (95% CI: 19.7, 27.1) in trastuzumab-treated patients and 18.4 months (95% CI: 12.7, 24.1) in patients treated with control regimens. The survival time after the development of CNS metastases in trastuzumab-treated patients was 19.2 months (95% CI: 15.6, 25.9). Approximately 22% of patients with HER2-positive MBC who were treated with trastuzumab developed CNS metastases. However, trastuzumab-treated patients had a longer survival than patients who were not treated with trastuzumab

Assessment of a flamelet approach to evaluating mean species mass fractions in moderately and highly turbulent premixed flames

Complex-chemistry Direct Numerical Simulation (DNS) data obtained from lean methane-air turbulent flames are analysed to perform a priori assessment of predictive capabilities of the flamelet approach to evaluating mean concentrations of various species in turbulent flames characterized by Karlovitz numbers Ka=6.0, 74.0, and 540. Six definitions of a combustion progress variable c are probed and two types of Probability Density Functions (PDFs) are adapted: (i) actual PDFs extracted directly from the DNS data or (ii) presumed β-function PDFs obtained using the DNS data on the first two moments of the c-field. Results show that the mean density, the mean temperature, and the mean mass fractions of CH4, O2, H2O, CO2, CO, CH2O, CH3, and HCO are very well predicted using the temperature-based combustion progress variable c_ and the actual PDF. For other considered species, the quantitative predictions are worse, but still appear to be encouraging (with the exception of CH3O at Ka=540). The use of the flamelet library obtained from the equidiffusive laminar flame improves results for H2, HO2, and H2O2 at the highest Karlovitz number. Alternative definitions of the combustion progress variable perform worse and the reasons for this are explored. The use of the β-function PDF yields worse results fo r intermediate species such as OH, O, H, CH3, and HCO, with this PDF being significantly different from the actual PDF. Application of the flamelet approach to rates of production/consumption of various species is also addressed and implications of obtained results for modeling are discussed

effect of in cylinder flow structures on late cycle soot oxidation in a quiescent heavy duty diesel engine

ABSTRACTThis paper reports on CFD simulations of in-cylinder flow and combustion in an open-bowl heavy duty diesel engine at high load. The focus of the study is to unravel the effect of swirl moti..

LES/FGM investigation of ignition and flame structure in a gasoline partially premixed combustion engine

This paper presents a joint numerical and experimental study of the ignition process and flame structures in a gasoline partially premixed combustion (PPC) engine. The numerical simulation is based on a five-dimension Flamelet-Generated Manifold (5D-FGM) tabulation approach and large eddy simulation (LES). The spray and combustion process in an optical PPC engine fueled with a primary reference fuel (70% iso-octane, 30% n-heptane by volume) are investigated using the combustion model along with laser diagnostic experiments. Different combustion modes, as well as the dominant chemical species and elementary reactions involved in the PPC engines, are identified and visualized using Chemical Explosive Mode Analysis (CEMA). The results from the LES-FGM model agree well with the experiments regarding the onset of ignition, peak heat release rate and in-cylinder pressure. The LES-FGM model performs even better than a finite-rate chemistry model that integrates the full-set of chemical kinetic mechanism in the simulation, given that the FGM model is computationally more efficient. The results show that the ignition mode plays a dominant role in the entire combustion process. The diffusion flame mode is identified in a thin layer between the ultra fuel-lean unburned mixture and the hot burned gas region that contains combustion intermediates such as CO. The diffusion flame mode contributes to a maximum of 27% of the total heat release in the later stage of combustion, and it becomes vital for the oxidation of relatively fuel-lean mixtures

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.Comment: Journal extension of arXiv:2003.01663; Accepted by IEEE TPAMI; Code is available at https://github.com/cherubicxn/haw

Learning Regional Attraction for Line Segment Detection

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with arXiv:1812.0212

Holistically-Attracted Wireframe Parsing

This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban dataset. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN, it improves the challenging mean structural average precision (msAP) by a large margin ($2.8\%$ absolute improvements) and achieves 29.5 FPS on single GPU ($89\%$ relative improvement). A systematic ablation study is performed to further justify the proposed method.Comment: Accepted by CVPR 202

Thin reaction zone and distributed reaction zone regimes in turbulent premixed methane/air flames : Scalar distributions and correlations

A series of premixed turbulent methane/air jet flames in the thin reaction zone (TRZ) and distributed reaction zone (DRZ) regimes were studied using simultaneous three-scalar high-resolution imaging measurements, including HCO/OH/CH2O, CH/OH/CH2O, T/OH/CH2O and T/CH/OH/. These scalar fields offer a possibility of revisiting the structures of turbulent premixed flames in different combustion regimes. In particular, CH2O provides a measure of the preheat zone, CH/HCO a measure of the inner layer of the reaction zone, and OH a measure of the oxidation zone. Scalar correlations are analyzed on both single-shot and statistical basis, and resolvable correlated structures of ∼100 µm between scalars are captured. With increasing turbulence intensity, it is shown that the preheat zone and the inner layer of the reaction zone become gradually broadened/distributed, and the correlation between HCO and [OH]LIF×[CH2O]LIF decreases. A transition from the TRZ regime to the DRZ regime is found around Karlovitz number of 70–100. The physical and chemical effects on the broadening of the flame are investigated. In the TRZ regime the inner layer marker CH and HCO remains thin in general although occasional local broadening of CH/HCO could be observed. Furthermore, there is a significant probability of finding CH and HCO at rather low temperatures even in the TRZ regime. In the DRZ regime, the broadening of CH and HCO are shown to be mainly a result of local reactions facilitated by rapid turbulent transport of radicals and intermediate reactants in the upstream of the reaction paths. Differential diffusion is expected to have an important effect in the DRZ regime, as H radicals seemingly play a more important role than OH radicals