Estimation of evaporation from open water is essential for hydrodynamics, manufacturing industries, irrigation, farming, environmental protection and many other purposes. It is also important for proper management of hydrological resources such as reservoirs, lakes and rivers. Recent methods are mostly data-driven methods, such as using Artificial Intelligence techniques. Adaptive Neuro Fuzzy Inference System (ANFIS) is one of them and has been widely adopted in many hydrological fields for its simplicity. The current research presents a comparative study on the impact of optimization techniques such as Firefly Algorithm (FFA), Genetic Algorithm (GA), Particle Swarm Optimizer (PSO) and Ant Colony Optimization (ACO) on obtained results. In addition, a practical method named Multi Gene-genetic Programming (MGGP) is employed to propose an equation for the estimation of the Evaporation. Six different measured weather variables are taken, which are maximum, minimum and average air temperature, sunshine hours, wind speed and relative humidity. Models are separately calibrated with total data set collected over an eight-year period of 2010-2017 at the specified station “Arizona” in the United States of America. Ten statistical indices are calculated to verify the results. All optimizers were observed and compared to check if the results are better than ANFIS or not. The objectives of the adoption of different optimizer techniques was to verify the accuracy of the prediction by ANFIS model. Comparisons showed that ANFIS and MGGP are slightly better than the other models. MGGP model is different from other models in a way that it provides a set of equations instead of showing numerical values; therefore, the computational time is high. PSO, FFA, ACO and GA are considered as optimizers in the main model. Though PSO provided very similar results to the ANFIS model and MGGP gives even better results than basic ANFIS model. ANFIS is easier in terms of model formation. ANFIS is simpler to build and easy to operate. Since the prediction was quite identical in all cases, the ANFIS model was suggested due to its simplicity.

As the emission legislation becomes more stringent, higher demands are put on the aftertreatment system in trucks. For dealing with nitrous oxides, AdBlue® (urea–water solution) is injected into the exhausts which evaporates and reduces nitrous oxides to nitrogen. At low exhaust temperatures, it is more difficult to evaporate the injected AdBlue® as the exhausts contain less energy. The injected solution may instead form a wall film. In this wall film, side reactions can occur which leads to the formation of deposits. This thesis aims at understanding how and when wall films and deposits are formed. To achieve this, a test rig that allowed visual and infrared observations of the process and variation of governing properties was designed and built. The results show that thicker plates can sustain higher dosages than thinner plates since the temperature drop and film area are smaller for the thicker plate. It was also observed that at plate temperatures >340 °C, the water in the impacting spray evaporated, leaving a urea dust in the gas phase. It is also clear that deposits form faster at higher gas temperatures (> 350 °C) compared to at lower temperatures (200–250 °C). The deposits form at the edge of the wall film in a region with a temperature higher than in the middle of the wall film. At lower temperatures, a wall film that spreads out over a very large area is formed and after a longer time period, deposits form at obstacles and at the wall film edge. Experiments for 2 h at lower temperatures left approximately the same amount of deposits as experiments for 30 min at higher temperatures. / När lagkraven blir strängare sätts högre krav på efterbehandlingssystemet i lastbilar. För att få bort nitrösa gaser injiceras AdBlue® (urea–vattenlösning) in i avgaserna vilken förångas och reducerar de nitrösa gaserna till ofarligt kväve. Vid låga avgastemperaturer är det svårare att förånga den injicerade AdBlue®–lösningen då avgaserna innehåller mindre energi. Den injicerade lösningen kan istället bilda en väggfilm. I denna väggfilm kan sidoreaktioner ske vilket leder till bildning av utfällningar. Detta examensarbete syftar till att öka förståelsen för hur och när väggfilmer och utfällningar bildas. För att uppnå detta designades och byggdes en testrigg i vilken visuella och infraröda observationer kan göras och influerande parametrar varieras. Resultaten visar att tjockare plåtar kan utstå högre doseringar jämfört med tunnare plåtar, eftersom lägre temperaturminskning och film area uppmätts för den tjockare plåten. Det sågs också att vid plåttemperaturer >340 °C så förångades vattnet i AdBlue®-lösningen först och efterlämnade ett ureadamm i gasfasen. Det konstaterades också att utfällningar bildas snabbare vid högre gastemperaturer (> 350 °C) jämfört med vid lägre temperaturer (200–250 °C). Utfällningarna bildas vid kanten av väggfilmen i en region som har en temperatur som är högre än den i mitten av väggfilmen. Vid lägre temperaturer bildas en väggfilm som sprider ut sig över en stor area och med tiden bildas utfällningar vid hinder och vid filmkanten. Experiment under 2 h vid låg gastemperatur gav jämförbara mängder utfällningar som experiment under 30 min vid högre temperatur.

>Magister Scientiae - MSc / This research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater.

We suggest a computational approach for estimating the ring-like deposition of nanoparticles contained in a drying liquid droplet. The proposed method involves a Monte Carlo scheme, based on three independent probabilistic processes: (a) evaporation at the liquid surface, (b) convective motion of nanoparticles to the contact line, and (c) treatment of the nanoparticles floating in the air. According to the computational results, while the liquid is evaporating in nanoparticle-suspended liquid droplet (NSLD), the nanoparticles are moved to the contact line as the mass of droplet decreases linearly with time. Since the resulting ring-like deposition can be accounted for in terms of nanoparticle mobility and liquid evaporation from the droplet, our computational approach achieves a morphological and kinematical description of NSLD drying. Some other important features, such as self-pinning of the contact line, reduction of the droplet radius, and pattern formation, are also obtained from this simulation.

Philosophiae Doctor - PhD / Low- and medium-spin bands of 187Os have been studied using the AFRODITE array, following the 186W(4He,3n)187Os reaction at a beam energy of 37 MeV. The measurements of 􀀀 coincidences, angular distribution ratios (RAD), polarization and -intensities were performed using eleven High Purity Germanium (HPGe) clover detectors. In the current work, all the previously known bands have been signi cantly extended and ve new bands have been added to the level scheme. The observed bands are interpreted within the cranked shell model (CSM), cranked Nilsson-Strutinsky- Bogoliubov (CNSB) formalism and Quasiparticle-plus-Triaxial-Rotor (QTR) model. Systematic comparison of bands with the neighbouring isotopes has also been made. Comparison of the models with experimental data shows good agreement.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18077号 / 理博第3955号 / 新制||理||1570(附属図書館) / 30935 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 川合 光, 准教授 福間 將文, 教授 畑 浩之 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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Experimental and numerical investigations of water microdroplet evaporation on heated, laser patterned polymer substrates are reported. The study is focused on both (1) validating numerical models with experimental data, (2) identifying how changes in the contact line infuences evaporative heat transfer and (3) determining methods of controlling contact line dynamics during evaporation. Droplets are formed using a bottom-up methodology, where a computer-controlled syringe pump supplies water to a ~200[micro]m in diameter fluid channel within the heated substrate. This methodology facilitates precise control of the droplets growth rate, size, and inlet temperature. In addition to this microchannel supply line, the substrate surfaces are laser patterned with a moat-like trench around the fluid-channel outlet, adding additional control of the droplets contact line motion, area, and contact angle. In comparison to evaporation on non-patterned substrate surfaces, this method increases the contact line pinning time by ~60% of the droplets lifetime. The evaporation rates are compared to the predictions of a commonly reported model based on a solution of the Laplace equation, providing the local evaporation flux along the droplets liquid-vapor interface. The model consistently overpredicts the evaporation rate, which is presumable due to the models constant saturated vapor concentration along the droplets liquid-vapor interface. In result, a modified version of the model is implemented to account for variations in temperature along the liquid-vapor interface. A vapor concentration distribution is then imposed using this temperature distribution, increasing the accuracy of predicting the evaporation rate by ~7:7% and ~9:9% for heated polymer substrates at T[sub]s = 50[degrees]C‰ and 65‰[degrees]C, respectively.

Drying and evaporation from nanoscale conduits are two ubiquitous phenomena found in nature. As these two nanoscale liquid-vapor phase change phenomena are significantly “accelerated” compared with the corresponding ones at micro- and macro-scales, various industrial applications, including oil recovery, electronic cooling, membrane desalination, and energy harvesting, have been developed. Despite their important implications, the fundamental mechanisms for these two accelerated phase-change processes have not been completely understood. For drying, it is widely accepted that liquid corner flow and film flow could significantly enhance mass transport in microscale conduits other than the sole contribution by vapor diffusion. However, it is unclear if the same principles apply to smaller scales and if the vapor diffusivity will change at the nanoscale. For evaporation, the evaporation kinetics at the nanoscale interface, rather than liquid/vapor transport toward/from the interface, determine the ultimate transport limit, which can be significantly higher than the classical prediction derived under quasi-equilibrium evaporation conditions. Still, the contributions to such enhanced kinetically limited evaporation remain unclear. This thesis aims to answer these unsolved questions by conducting systematic experimental studies on drying and evaporation from single nanochannels and nanopores. We used state-of-art fabrication to create close-end 2D nanochannels with heights from 29 to 122 nm and measure water drying in such channels using an optical microscope. Combining with the channel confinement study and relative humidity study, we decoupled the individual contributions from vapor and liquid transport to the drying and extracted the water vapor diffusivity in nanochannels. We also developed a hybrid nanochannel-nanopore design to achieve and measure kinetically limited evaporation flux from silicon nitride nanopores and graphene nanopores with pore diameters ranging from 24 to 347 nm. Our results show that the evaporation flux increases with the decreasing diameter for both types of nanopores. Furthermore, graphene nanopores overall exhibit higher evaporation fluxes than silicon nitride nanopores with similar diameters. We attribute the diameter-dependent evaporation flux to the diameter-dependent hydronium ion concentration in silicon nitride nanopores and the edge-facilitated evaporation in graphene nanopores, respectively. We expect this work to advance our understanding of nanoscale fast drying and evaporation and provide design guidance for novel nanoporous membrane evaporators.