A fundamental investigation of scaling up turbulent liquid-phase vortex reactor using experimentally validated CFD models

The production of uniform-sized nanoparticles has potential application in a wide variety of fields, but is still a challenge. One main reason that many lab-scale manufactured nanoparticles have not appeared in industry is because there is lack of control on physical properties and surface functionality of nanoparticles during massive production. Recently, a process called Flash Nanoprecipitation (FNP) has been developed to produce nanoparticles with controlled size and high drug-loading rate. In FNP, fast mixing is required to make sure that solvent and non-solvent mix homogeneously so that competitive precipitation of organics and polymer could result in functional nanoparticles with narrow size distribution. A multi-inlet vortex reactor (MIVR) has been developed to provide fast mixing for the FNP. The MIVR includes four inlets which are tangential to the mixing chamber of reactor. The MIVR has the operational advantage of providing different inlet-flow momentum and configurations compared to other reactors used in the FNP such as confined impinging jet reactor (CIJR). Former studies have already shown its ability of providing fast mixing and successfully producing functional nanoparticles in the FNP. However, until now all previous investigations about the MIVR only focused in its micro-scale (dimensions in millimetre). While the micro-scale MIVR does show great promise in the production of functional nanoparticles, the small dimensions and correspondingly small output of the micro-scale MIVR limit its usefulness to producing functional nanopraticles for applications requiring small production run such as high-value pharmaceutical agents. Some applications such as nanoparticle used in pesticides and cosmetics may require larger production run than the micro-scale MIVR can provide, making it economically unrealistic based on the relatively high capital and operating costs needed for a large number of reactors operating in parallel. For this reason, in the study we are interested in investigating the feasibility of scaling up the FNP process to a macro-scale MIVR capable of generating large quantities of functional nanoparticles, both rapidly and economically, and consequently developing experimentally verified computational fluid dynamics (CFD) models that can be used as design tools for further optimizing reactor design and operation parameters to produce customized functional nanoparticles. To accomplish this investigation, a macro-scale MIVR has been built with optical access. Non-intrusive, optical-based measurement techniques including particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) were used to measure flow field and mixing, and related CFD models, specifically turbulence models were validated and developed for optimizing the MIVR and future model development of the FNP process

Selection Method for Kernel Function in Nonparametric Extrapolation Based on Multicriteria Decision-Making Technology

Selecting the most appropriate kernel function to extrapolate a load set is the paramount step in compiling load spectrum, as it affects the results of nonparametric extrapolation largely. Aiming at this issue, this paper provides a new approach in selecting kernel function for the nonparametric extrapolation. To solve the complexity and uncertainty of nonparametric extrapolation, characteristics of four kernel functions and their effects on the results are explained in the “from-to” diagram obtained by rainflow counting. Multicriteria decision-making (MCDM) is then applied to solve the selection problem of kernel function. To evaluate the dispersion degrees of the mean and amplitude of a load set accurately, their range, standard deviation, and interquartile range are selected as the evaluation criteria. The weight of each criterion, which represents the impact degree on the selection of the kernel function, is calculated separately using the eigenvector and entropy method. The comprehensive weights are obtained by applying the optimization theory and Jaynes’ maximum entropy principle. Finally, the importance of each criterion is discussed according to their calculated comprehensive weights, and the selection method for kernel functions is obtained, which is illustrated by extrapolating the output torque of the power split device of hybrid electrical vehicles

SplatFlow: Learning Multi-frame Optical Flow via Splatting

The occlusion problem remains a crucial challenge in optical flow estimation (OFE). Despite the recent significant progress brought about by deep learning, most existing deep learning OFE methods still struggle to handle occlusions; in particular, those based on two frames cannot correctly handle occlusions because occluded regions have no visual correspondences. However, there is still hope in multi-frame settings, which can potentially mitigate the occlusion issue in OFE. Unfortunately, multi-frame OFE (MOFE) remains underexplored, and the limited studies on it are mainly specially designed for pyramid backbones or else obtain the aligned previous frame's features, such as correlation volume and optical flow, through time-consuming backward flow calculation or non-differentiable forward warping transformation. This study proposes an efficient MOFE framework named SplatFlow to address these shortcomings. SplatFlow introduces the differentiable splatting transformation to align the previous frame's motion feature and designs a Final-to-All embedding method to input the aligned motion feature into the current frame's estimation, thus remodeling the existing two-frame backbones. The proposed SplatFlow is efficient yet more accurate, as it can handle occlusions properly. Extensive experimental evaluations show that SplatFlow substantially outperforms all published methods on the KITTI2015 and Sintel benchmarks. Especially on the Sintel benchmark, SplatFlow achieves errors of 1.12 (clean pass) and 2.07 (final pass), with surprisingly significant 19.4% and 16.2% error reductions, respectively, from the previous best results submitted. The code for SplatFlow is available at https://github.com/wwsource/SplatFlow

A Delayed Detached Eddy Simulation Model with Low Reynolds Number Correction for Transitional Swirling Flow in a Multi-Inlet Vortex Nanoprecipitation Reactor

The objective of the presented work is to verify a delayed detached eddy simulation (DDES) model for simulating transitional swirling flow in a micro-scale multi-inlet vortex reactor (MIVR). The DDES model is a k-w based turbulence model with a low Reynolds number correction applied to the standard k-w model such that the Reynolds-averaged Navier-Stokes (RANS) component of the DDES model is able to account for low Reynolds number flow. By limiting the dissipation rate in the k-equation, the large-eddy simulation (LES) part of the DDES model behaves similarly to a one-equation sub-grid model. The turbulent Reynolds number is redefined to represent both modeled and resolved turbulence level so that underestimation of the RANS length scale in the LES range can be reduced. Applying the DDES model to simulate both laminar and transitional flow in the micro-scale MIVR produces an accurate prediction of mean velocity and turbulent intensity compared with experimental data. It is demonstrated that the proposed DDES model is capable of simulating transitional flow in the complex geometry of the micro-scale MIVR. These simulation results also help to understand the flow and mixing patterns in the micro-scale MIVR and provide guidances to optimize the reactor for the application of producing functional nanoparticles

Effects of Chocolate Milk Supplementation on Recovery from Cycling Exercise and Subsequent Time Trial Performance

PURPOSE: Supplementing with carbohydrate plus protein following strenuous endurance exercise has been found to improve both recovery and subsequent aerobic endurance performance beyond that of a carbohydrate supplement alone. The purpose of the present study was to compare the effects of chocolate milk (CM), an isocaloric carbohydrate only supplement (CHO), and placebo (PLA) on markers of endurance exercise recovery and subsequent time trial performance in trained cyclists. METHODS: Ten trained male and female cyclists (5 males, 5 females) performed 3 trials in which they first cycled for 1.5 h at 70% of VO2max, followed by 10 min of intervals that alternated 45% and 90% VO2max. They then recovered in the laboratory for 4 h, and performed a 40 km time trial (TT). The supplements were provided immediately after the first bout and 2 h into the recovery period. Treatments were administered using a double-blind randomized design. RESULTS: TT time was significantly shorter in CM than CHO and PLA (79.43±2.11 vs. 85.74±3.44 and 86.92±3.28 min, respectively, p=\u3c.05). Significant treatment differences were found for plasma insulin, glucose, free fatty acids (FFA) and glycerol. Plasma insulin levels were significantly lower in CM than CHO at recovery time points R45 (47.30±10.54 vs. 58.71±6.01 &#;U/ml, p\u3c.05), R120 (14.32±1.34 vs. 22.53±3.37 &#;U/ml, p\u3c.05) and REnd (15.57±1.53 vs. 34.35±4.55 &#;U/ml, p\u3c.05). Plasma glucose was significantly lower in CM than CHO at recovery time points R45 (76.61±3.08 vs. 101.65±3.47 mg/dL, p\u3c.05) and R120 (74.72±2.22 vs. 81.46±4.87 mg/dL, p\u3c.05). While FFA and glycerol were both higher in PLA than in CM and CHO overall (p\u3c.05 for both), FFA and glycerol were higher in CM than in CHO (p\u3c.05 for both) during recovery and at TTEnd. Blood lactate was significantly higher at R45 and TTEnd in both CM and CHO than in PLA, but no differences were found between CM and CHO. No significant treatment differences were found for myoglobin, CPK, cortisol, and 5 pro- and anti-inflammatory cytokines (TNF-&#;, IL-6, IL-10, IL-8, and IL-1Ra). CONCLUSIONS: Chocolate milk provided during recovery can improve subsequent time trial performance in trained cyclists more effectively than an isocaloric CHO supplement. This may be due to a faster rate of muscle glycogen resynthesis

Exploring Future Storage Options for ATLAS at the BNL/SDCC facility

The ATLAS experiment is expected to deliver an unprecedented amount of scientific data in the High Luminosity(HL-LHC) era. As the demand for disk storage capacity in ATLAS continues to rise steadily, the BNL Scientific Data and Computing Center (SDCC) faces challenges in terms of cost implications for maintaining multiple disk copies and adapting to the coming ATLAS storage requirements. To address these challenges, the SDCC Storage team has undertaken a thorough analysis of the ATLAS experiment’s requirements, matching them to suitable storage options and strategies, and has explored alternatives to enhance or replace the current storage solution. This paper aims to present the main challenges encountered while supporting big data experiments such as ATLAS. We describe the experiment’s specific requirements and priorities, particularly focusing on the critical storage system characteristics of the high-luminosity run and how the key storage components provided by the Storage team work together: the dCache disk storage system; its archival back-end, HPSS; and its OS-level backend Storage. Specifically, we investigate a novel approach to integrate Lustre and XRootD. In this setup, Lustre serves as backend storage and XRootD acts as an access layer frontend, supporting various grid access protocols. Additionally, we also describe the validation and commissioning tests, including the performance comparison between dCache and XRootd. Furthermore, we provide a performance and cost analysis comparing OpenZFS and LINUX MD RAID, evaluate different storage software stacks, and showcase stress tests conducted to validate Third Party Copy (TPC) functionality

Aerobic Exercise Training Adaptations Are Increased by Postexercise Carbohydrate-Protein Supplementation

Carbohydrate-protein supplementation has been found to increase the rate of training adaptation when provided postresistance exercise. The present study compared the effects of a carbohydrate and protein supplement in the form of chocolate milk (CM), isocaloric carbohydrate (CHO), and placebo on training adaptations occurring over 4.5 weeks of aerobic exercise training. Thirty-two untrained subjects cycled 60 min/d, 5 d/wk for 4.5 wks at 75–80% of maximal oxygen consumption (VO2 max). Supplements were ingested immediately and 1 h after each exercise session. VO2 max and body composition were assessed before the start and end of training. VO2 max improvements were significantly greater in CM than CHO and placebo. Greater improvements in body composition, represented by a calculated lean and fat mass differential for whole body and trunk, were found in the CM group compared to CHO. We conclude supplementing with CM postexercise improves aerobic power and body composition more effectively than CHO alone