A molecular dynamics study of evaporation of multicomponent stationary and moving fuel droplets in multicomponent ambient gases under supercritical conditions

The evaporation of a six-component fuel droplet under supercritical conditions is investigated using molecular dynamics (MD) simulations. The focus here is on effects of multicomponent ambient gases and the relative motion between the droplet and the ambient. The ambient pressure ranges from 8 MPa to 36 MPa and the ambient temperature ranges from 750 K to 3600 K. In the lower range of the temperature and pressure, the average displacement increment (ADI) per fuel atom gradually increases with time and the classic evaporation is observed. In the higher range of the temperature and pressure, the ADI profile has a unimodal distribution with time and the diffusive mixing between the droplet and the ambient gases dominates. Based on the ADI profile of fuel atoms, a criterion (τ0.9P) for mode transition from evaporation to diffusion is proposed. Among the ambient gases investigated, the mode transition is the most difficult in the nitrogen ambient but the easiest in combustion exhaust gases. For multicomponent fuel droplets close to or in diffusion mode, with higher relative velocities, the relative difference between evaporation rates for light/heavy fuel components is reduced. This study demonstrates that supercritical conditions alone are insufficient for mode transition of evaporation

A molecular dynamics study of evaporation mode transition of hydrocarbon fuels under supercritical conditions

The mode transition of evaporation for single- and multi-component hydrocarbon fuels is investigated at the molecular level. This study scrutinizes first the subcritical and supercritical evaporation of nhexadecane droplets and liquid films by molecular dynamics (MD) simulations. The mode regime map of n-hexadecane droplets is obtained. Then the mode transition of evaporation of a three-component droplet and a six-component droplet is studied. A critical dimensionless number τ 0.9P of 0.5 based on the average displacement increment (ADI) of fuel atoms is used to identify the evaporation mode transition of fuels with any type and number of components. It is found that in the diffusion mode of evaporation, the entropy becomes the dominant factor in the evaporation of fuels, and the disorder of the fuel molecules increases significantly compared with that in the classic evaporation mode. Compared with the case of the quiescent droplet, with increasing relative velocity between the droplet and the ambient gas, the mode transition becomes easier, although this is a non-linear process. Fuel droplets and liquid films with different initial sizes are investigated to understand the size effect. In addition, for the same ambient temperature and pressure, the smaller the normalized specific heat transfer surface area of the fuel is, the easier the mode transition of evaporation is. A correlation was proposed to compare the possibility of mode transition of evaporation for single- and multi-component fuels

Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person features. Furthermore, traditional methods ignore important relationship among frames, which causes information redundancy in fusion along the time axis. To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. As for the semantic aspect, a multi-stage fusion network is explored to fuse richer frame features at multiple semantic levels, which can effectively reduce the information loss caused by the traditional single-stage fusion. While, for the time axis, the existing intra-frame attention method is improved by adding a novel inter-frame attention module, which effectively reduces the information redundancy in temporal fusion by taking the relationship among frames into consideration. The experimental results show that our approach can effectively improve the video-based re-identification accuracy, achieving the state-of-the-art performance

Baseline differences in metabolic profiles of patients with lung squamous cell carcinoma responding or not responding to treatment with nanoparticle albumin-bound paclitaxel (nab-paclitaxel)

Background: Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) is a preparation widely used in chemotherapy for cancers. However, only some patients benefit from this treatment. Therefore, identifying which patients will respond to nab-paclitaxel therapy is crucial. Methods: A cohort of 32 patients with lung squamous cell carcinoma (LUSC) treated with nab-paclitaxel were enrolled in this study. Plasma samples were collected before chemotherapy and used to perform metabolomic and lipidomic analyses. Tumor response to two cycles of chemotherapy was evaluated. Metabolites differentially present among populations were screened and analyzed. Results: According to the RECIST criteria, one-third of patients had a significant response to nab-paclitaxel, whereas one-fifth showed no discernible benefit. According to the criteria of variable importance in projection >1 and fold change >2, we identified 61, 81 and 54 differential metabolites between the progressive disease (PD) vs partial response (PR), PD vs stable disease (SD), and SD vs PR groups, respectively. Moreover, we used three variation in logistic regression models and ROC diagnostic curves to identify optimal metabolites for stratifying patients with differing chemotherapeutic responses. The PD vs SD, SD vs PR, and PD vs PR groups were well separated on the basis of cis-9,10-epoxystearic acid/octapentaenoic acid (AUC 0.9330), salicyluric acid/DG (18:1/20:5/0:0) (AUC 1.0000) and D-glyceric acid/9,12-octadecadienoic acid (AUC 1.0000), respectively. Conclusion: The baseline metabolic profiles significantly differed between responder and non-responder patients with LUSC treated with nab-paclitaxel. These differential metabolites have the potential to predict the outcomes of patients with LUSC before chemotherapy