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1. Best practices for precipitation sample storage for offline studies of ice nucleation in marine and coastal environments

   https://doi.org/10.5194/amt-13-6473-2020  

   Beall, Charlotte M.; Lucero, Dolan; Hill, Thomas C.; DeMott, Paul J.; Stokes, M. Dale; Prather, Kimberly A. (January 2020, Atmospheric Measurement Techniques)

   null (Ed.)

   Abstract. Ice-nucleating particles (INPs) are efficiently removed fromclouds through precipitation, a convenience of nature for the study of thesevery rare particles that influence multiple climate-relevant cloudproperties including ice crystal concentrations, size distributions andphase-partitioning processes. INPs suspended in precipitation can be used toestimate in-cloud INP concentrations and to infer their originalcomposition. Offline droplet assays are commonly used to measure INPconcentrations in precipitation samples. Heat and filtration treatmentsare also used to probe INP composition and size ranges. Many previousstudies report storing samples prior to INP analyses, but little is knownabout the effects of storage on INP concentration or their sensitivity totreatments. Here, through a study of 15 precipitation samples collected at acoastal location in La Jolla, CA, USA, we found INP concentration changes upto > 1 order of magnitude caused by storage to concentrations ofINPs with warm to moderate freezing temperatures (−7 to−19 ∘C). We compared four conditions: (1) storage at roomtemperature (+21–23 ∘C), (2) storage at +4 ∘C, (3) storage at −20 ∘C and (4) flash-freezing samples with liquid nitrogen prior to storage at −20 ∘C. Results demonstrate that storage can lead to bothenhancements and losses of greater than 1 order of magnitude, withnon-heat-labile INPs being generally less sensitive to storage regime, butsignificant losses of INPs smaller than 0.45 µm in all tested storageprotocols. Correlations between total storage time (1–166 d) and changesin INP concentrations were weak across sampling protocols, with theexception of INPs with freezing temperatures ≥ −9 ∘C in samples stored at room temperature. We provide thefollowing recommendations for preservation of precipitation samples fromcoastal or marine environments intended for INP analysis: that samples bestored at −20 ∘C to minimize storage artifacts, thatchanges due to storage are likely an additional uncertainty in INPconcentrations, and that filtration treatments be applied only to freshsamples. At the freezing temperature −11 ∘C, average INPconcentration losses of 51 %, 74 %, 16 % and 41 % were observed foruntreated samples stored using the room temperature, +4, −20 ∘C, and flash-frozen protocols, respectively.Finally, the estimated uncertainties associated with the four storage protocolsare provided for untreated, heat-treated and filtered samples for INPsbetween −9 and −17 ∘C. 

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2. Heat transfer, vapour diffusion and Stefan flow around levitating droplets near a heated liquid surface

   https://doi.org/10.1017/jfm.2023.351  

   Davis, Jacob E.; Kabov, Oleg A.; Zaitsev, Dmitry V.; Ajaev, Vladimir S. (June 2023, Journal of Fluid Mechanics)

   We consider a slowly condensing droplet levitating near the surface of an evaporating layer, and develop a mathematical model to describe diffusion, heat transfer and fluid flow in the system. The method of separation of variables in bipolar coordinates is used to obtain the series expansions for temperature, vapour concentration and the Stokes stream function. This framework allows us to determine temperature profiles and condensation rates at the surface of the droplet, and to calculate the upward force that allows the droplet to levitate. Somewhat counter-intuitively, condensation is found to be the strongest near the bottom of the droplet, which faces the hot liquid layer. The experimentally observed deviations from the classical law predicting the square of the radius to grow linearly in time are explained by the model. A spatially non-uniform phase change rate results in a contribution to the force not considered in previous studies, and comparable to droplet weight and the upward force calculated from the Stokes drag law. The levitation conditions are formulated accordingly, resulting in the prediction of levitation height as a function of droplet size without any fitting parameters. A simple criterion is formulated to define the parameter ranges in which levitation is possible. The results are in good agreement with the experimental data except that the model tends to slightly underpredict the levitation height. 

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3. Turbulent droplet breakage in a von Kármán flow cell

   https://doi.org/10.1063/5.0096395  

   Ravichandar, Krishnamurthy; Vigil, R. Dennis; Fox, Rodney O.; Nachtigall, Stephanie; Daiss, Andreas; Vonka, Michal; Olsen, Michael G. (July 2022, Physics of Fluids)

   Droplet dispersion in liquid–liquid systems is a crucial step in many unit operations throughout the chemical, food, and pharmaceutic industries, where improper operation causes billions of dollars of loss annually. A theoretical background for the description of droplet breakup has been established, but many assumptions are still unconfirmed by experimental observations. In this investigation, a von Kármán swirling flow device was used to produce homogeneous, low-intensity turbulence suitable for carrying out droplet breakage experiments using optical image analysis. Individual droplets of known, adjustable, and repeatable sizes were introduced into an isotropic turbulent flow field providing novel control over two of the most important factors impacting droplet breakage: turbulence dissipation rate and parent droplet size. Introducing droplets one at a time, large data sets were gathered using canola, safflower, and sesame oils for the droplet phase and water as the continuous phase. Automated image analysis was used to determine breakage time, breakage probability, and child droplet size distribution for various turbulence intensities. Breakage time and breakage probability were observed to increase with increasing parent droplet size, consistent with the classic and widely used Coulaloglou–Tavlarides breakage model (C–T model). The shape of the child drop size distribution function was found to depend upon the size of the parent droplet. 

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4. Supply Air Temperature Prediction in an Air-Handling Unit Using Artificial Neural Network

   https://doi.org/10.1115/IMECE2018-88507  

   Sukthankar, Nikita; Walekar, Abhishek; Agonafer, Dereje (November 2018, ASME 2018 International Mechanical Engineering Congress and Exposition)

   Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit. 

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5. Development of a local wall concentration model for the design of single pass tangential flow filtration (SPTFF) systems with viral vector surrogates

   https://doi.org/10.1016/j.memsci.2024.123276  

   Chaubal, Akshay S; Single, Alexis J; Zydney, Andrew L (January 2025, Journal of Membrane Science)

   Recent advances in the use of viral vectors for gene therapy has created a need for efficient downstream processing of these novel therapeutics. Single-pass tangential flow filtration (SPTFF) can potentially improve final product quality via reductions in shear, and it can increase manufacturing productivity via simple implementation into continuous/intensified processes. This study investigated the impact of variations in pressure and flow rate along the length of the membrane on overall SPTFF performance. Constant-flux filtration experiments at feed fluxes from 14 to 420 L/m2/h (Reynolds numbers <20) were performed using Pellicon® 3 TFF cassettes with fluorescent nanoparticles as model viral vectors. The location of nanoparticle accumulation shifted towards the filter outlet at high conversion and was also a function of the permeate flow configuration. These phenomena were explained using a newly developed concentration polarization model that predicts the distribution in local wall concentration over the length of the membrane. The model accurately captured the observed nanoparticle accumulation trends, including the effects of the permeate flow profile (co-current, divergent, or convergent flow) on nanoparticle accumulation within the SPTFF module. Nanoparticle accumulation at moderate conversion was more uniform using convergent flow, but nanoparticle accumulation at 80 % conversion (5x concentration factor) can be minimized using a divergent flow configuration. The local wall concentration model was also used to evaluate the critical flux by assuming that fouling occurs when the nanoparticle concentration at any point along the membrane surface exceeds 15 % by volume. These results provide important insights for the design and operation of SPTFF technology for inline concentration of viral vectors. 

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