Fundamental studies of flame propagation in lean-burn natural gas engines

Lean-burn natural gas engines offer enhanced thermal efficiencies and reduced soot and NOx emissions. However, cycle-to-cycle variability in combustion that can result from unreliable ignition, variability in equivalence ratio and quenching is a challenge. Reliability of ignition can be improved by employing a dual-fuel ignition strategy in which a small quantity of diesel fuel is injected to initiate ignition. Computational studies of n-heptane/methane-air mixing layers are performed to provide insight into the fundamental physics of dual-fuel ignition. The results show that the characteristic time required for steady premixed flame propagation has three components: time for autoignition to occur, time for peak temperature to be achieved following autoignition, and time for steady flame propagation in the premixed fuel/air mixture to be achieved. The autoignition time correlates well with pressure and temperature of the unburned premixed charge. The time to achieve peak temperature is relatively short, but correlates with mixing layer thickness and premixed equivalence ratio. The time to achieve steady propagation correlates with mixing layer thickness and laminar flame speed and thickness. Subsequent work focuses on turbulent flame propagation in lean homogeneous mixtures by employing direct numerical simulations (DNS) under conditions that are relevant to lean-burn engines. Attention is specifically focused on the turbulent flame speed (ST) as a parameter of interest because of its importance in modeling combustion in engines. The studies are carried out in the thin reaction zone (TRZ) regime of turbulent premixed combustion. Normalized turbulence intensity (urms/SL) varies from 2 to 25 and the ratio of integral length scale to flame thickness (L o/δL) varies from 3.2 to 12.8. Initial studies show that the normalized turbulent flame speed (ST/SL) depends on more parameters than urms/SL suggested by some models. Although it is known that the turbulent flame speed varies with equivalence ratio, it is shown that the normalized turbulent flame speed does not change with equivalence ratio provided the Karlovitz (Ka) and Damköhler (Da) numbers are fixed. This suggests that Kaand/or Da are important parameters in characterizing the turbulent flame speed. Furthermore, ST/SL can be related to the flame area enhancement AT/AL and an efficiency factor Io which is close to unity. AT/AL is raised by increasing turbulent Reynolds number ReT and by reducing Ka. Increasing ReT leads to a broader spectrum of turbulent eddies that generate flame surface area. Increasing Karesults in fine wrinkling at the expense of larger scale wrinkling. This results in a net reduction in the effective surface area enhancement. Based on these insights, a correlation for ST that shows a dependence on Re T and Ka is proposed. Modeling of the Flame Surface Density (FSD) evolution is also considered. FSD is influenced by tangential strain rate and flame displacement speed. Surface averaged tangential strain rate is found to scale linearly with Ka. The effects of Ka on flame displacement speed are modeled using a Probability Density Function (PDF) based approach. The effects of premixed combustion on turbulence are investigated. For flames in the TRZ regime, the turbulence kinetic energy (TKE) decays monotonically across the flame brush. Scaling analyses of the terms in the transport equation of TKE reveal that viscous dissipation is the dominant contribution in the TKE equation. The relative importance of the other terms in the TKE equation decreases with increasing Ka

Analysis of the Dilemma and Strategies of Elderly Patients Access to Outpatient Services - Based on the Examples from three Grade A Tertiary Hospitals in Jiangxi Province

Objective: To identify the dilemma of elderly patients' access to outpatient services, develop strategies to improve the environment and functions of the outpatient department, and encourage the elderly to access medical services independently. Methods: By observing and interviewing, this paper studies the environment, behavior, and experiences of elderly patients when accessing medical services, identifies and classifies the key issues, and provides corresponding suggestions. Results: Existing signs and voice prompt systems fail to guide elderly patients to access to medical services; Elderly patients have difficulty in finding places to transit and rest when accessing to outpatient services; Elderly patients have problems in using AI (artificial intelligence) technologies when they access to outpatient services; There are communication barriers between elderly patients and medical staffs. Conclusion: Optimizing the guiding signs and voice prompt systems according to the characteristics of elderly patients; Designing the areas of transition and rest reasonably; Enhancing the ability of elderly patients to use self-service equipment; Promoting the medical treatment process to the elderly in a humanized way

The Separation of Charm and Bottom Decays Measured in p+Au Collisions at 200 GeV

It has long been observed experimentally, from previous heavy-flavor electron measurements, that heavy quarks are subject to substantial modifications of their momentum spectrum. Using the PHENIX detector at the Relativistic Heavy Ion Collider (RHIC), measurements of the production of open heavy flavor hadrons with charm and bottom quarks in p+Au collisions at 200 GeV are studied and presented in this thesis. Distance of closest approach analysis of electron tracks is used to study the semileptonic decay electrons from charm and bottom hadrons. The results include invariant yield and fraction of bottom electrons. In addition to the p+p and Au+Au collisions’ studies previously done by the PHENIX Collaboration, this p+Au study provides another contributing factor to heavy-ion collision as the cold nuclear matter baseline

Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus image or an OCT image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the fundus and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based fundus / OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a fundus image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,099 color fundus images and 1,290 OCT images acquired from 1,099 distinct eyes verify the effectiveness of the proposed solution for multi-modal AMD categorization

Endocrine disrupting and carcinogenic effects of decabromodiphenyl ether

BackgroundDecabromodiphenyl ether (BDE209), an essential industrial flame retardant that is widely used, has recently been reported to be increasing in human serum. Due to the structural similarity between BDE209 and thyroid hormones, its toxic effects on the thyroid are of particular concern.MethodsOriginal articles in the PubMed database were collected using the terms “BDE209”, “decabromodiphenyl ether”, “endocrine disrupting”, “thyroid”, “carcinogenesis”, “polybrominated diphenyl ethers”, “PBDEs,” and their synonyms from inception up to October of 2022.ResultsOf the 748 studies initially identified, 45 were selected, which emphasized the adverse effects of BDE209 on endocrine system. BDE209 may have a toxic effect not only on thyroid function but also on thyroid cancer tumorigenesis at multiple levels, such as by directly interfering with the TR, hypothalamic-pituitary-thyroid (HPT) axis, enzyme activity, and methylation. However, it is impossible to draw a definitive conclusion on the exact pathway of thyroid toxicity from BDE209.ConclusionsAlthough the toxic effects of BDE209 on the thyroid have been well investigated, its tumorigenic effects remain unclear and further research is necessary