Thermo-fluid Dynamics of Two-Phase Flow, Second Edition is focused on the fundamental physics of two-phase flow. The authors present the detailed theoretical foundation of multi-phase flow ...thermo-fluid dynamics as they apply to: Nuclear reactor transient and accident analysis, Energy systems, Power generation systems, Chemical reactors and process systems, Space propulsion, Transport processes. This edition features updates on two-phase flow formulation and constitutive equations and CFD simulation codes such as FLUENT and CFX, new coverage of the lift force model, which is of particular significance for those working in the field of computational fluid dynamics, new equations and coverage of 1 dimensional drift flux models and a new chapter on porous media formulation.
We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each ...active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus <M class or ≥C class versus <C class). From 3 × 105 observation images taken during 2010-2015 by the Solar Dynamic Observatory, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 (T ≥ 107 K) and the X-ray and 131 intensity data 1 and 2 hr before an image. For operational evaluation, we divided the database into two for training and testing: the data set in 2010-2014 for training, and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS = 0.80 for ≥M-class flares and TSS = 0.63 for ≥C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.
Thermo-fluid dynamics of two-phase flow is an important subject for various scientific and engineering fields. It plays a particularly significant role in thermal-hydraulic analysis of nuclear ...reactor transients and accidents. The topics of multiphase flow are also essential for various engineering systems related to energy, chemical engineering processes and heat transfer.Thermo-fluid Dynamics of Two-phase Flow is written for graduate students, scientists and engineers who need in depth theoretical foundations to solve two-phase problems in various technological systems.Based on the extensive research experiences focused on the fundamental physics of two-phase flow, the authors present the detailed theoretical foundation of multi-phase flow thermo-fluid dynamics.
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms ...that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
RA is a chronic autoimmune disease characterized by joint synovial inflammation and progressive cartilage/bone destruction. Although various kinds of RA drug have been developed worldwide, there are ...currently no established methods for preventing RA-associated bone destruction, the most severe outcome of this disease. One of the major pathogenic factors in arthritic bone destruction is the enhanced activity of osteoclasts at inflammatory sites. Osteoclasts are bone-resorbing giant polykaryons that differentiate from mononuclear macrophage/monocyte-lineage haematopoietic precursors. Upon stimulation by cytokines, such as M-CSF and RANK ligand, osteoclast precursor monocytes migrate and attach onto the bone surface (migration). They then fuse with each other to form giant cells (differentiation) and mediate bone resorption (function). In this review, we summarize the current understanding regarding the mechanisms underlying these three dynamic steps of osteoclastic activity and discuss novel lines of osteoclast-targeted therapies that will impact future treatment of RA.
In this paper, we present existing and planned Japanese space weather research activities. The program consists of several core elements, including a space weather prediction system using numerical ...forecasts, a large‐scale ground‐based observation network, and the cooperative framework “Project for Solar‐Terrestrial Environment Prediction (PSTEP)” based on a Grant‐in Aid for Scientific Research on Innovative Areas.
Plain Language Summary
It is the time to apply space weather studies to social activities, particularly communication and aviation. The effects of space weather on electric power grids are also widely discussed with power companies. Communication with end users clarifies the goal and strategy of space weather studies. There are still several unrevealed processes in space weather, e.g., solar flare processes and the timing of geomagnetic reconnection, and they make it difficult to improve further the precision of space weather forecasting. We hope that some recent AI technology can contribute to compensating these missing links.
Key Points
Many of research institutes work for space weather research in Japan; NICT leads the operational space weather forecast and related studies
Many of academic space weather research are published by universities; ISEE of Nagoya University especially leads the activity
On 2015, PSTEP is established and works for a framework of cooperation among the institutes
The Southern Ocean (44-75° S) plays a critical role in the global carbon cycle, yet remains one of the most poorly sampled ocean regions. Different approaches have been used to estimate sea-air CO2 ...fluxes in this region: synthesis of surface ocean observations, ocean biogeochemical models, and atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Southern Ocean sea-air CO2 fluxes between 1990-2009. Using all models and inversions (26), the integrated median annual sea-air CO2 flux of -0.42 ± 0.07 Pg C yr-1 for the 44-75° S region, is consistent with the -0.27 ± 0.13 Pg C yr-1 calculated using surface observations. The circumpolar region south of 58° S has a small net annual flux (model and inversion median: -0.04 ± 0.07 Pg C yr-1 and observations: +0.04 ± 0.02 Pg C yr-1 ), with most of the net annual flux located in the 44 to 58° S circumpolar band (model and inversion median: -0.36 ± 0.09 Pg C yr-1 and observations: -0.35 ± 0.09 Pg C yr-1 ). Seasonally, in the 44-58° S region, the median of 5 ocean biogeochemical models captures the observed sea-air CO2 flux seasonal cycle, while the median of 11 atmospheric inversions shows little seasonal change in the net flux. South of 58° S, neither atmospheric inversions nor ocean biogeochemical models reproduce the phase and amplitude of the observed seasonal sea-air CO2 flux, particularly in the Austral Winter. Importantly, no individual atmospheric inversion or ocean biogeochemical model is capable of reproducing both the observed annual mean uptake and the observed seasonal cycle. This raises concerns about projecting future changes in Southern Ocean CO2 fluxes. The median interannual variability from atmospheric inversions and ocean biogeochemical models is substantial in the Southern Ocean; up to 25% of the annual mean flux, with 25% of this interannual variability attributed to the region south of 58° S. Resolving long-term trends is difficult due to the large interannual variability and short time frame (1990-2009) of this study; this is particularly evident from the large spread in trends from inversions and ocean biogeochemical models. Nevertheless, in the period 1990-2009 ocean biogeochemical models do show increasing oceanic uptake consistent with the expected increase of -0.05 Pg C yr-1 decade-1 . In contrast, atmospheric inversions suggest little change in the strength of the CO2 sink broadly consistent with the results of Le Quéré et al. (2007).
It is well established that the ocean plays an important role in absorbing anthropogenic carbon C
ant from the atmosphere. Under global warming, Earth system model simulations and theoretical ...arguments indicate that the capacity of the ocean to absorb C
ant will be reduced, with this constituting a positive carbon–climate feedback. Here we apply a suite of sensitivity simulations with a comprehensive Earth system model to demonstrate that the surface waters of the shallow overturning structures (spanning 45°S–45°N) sustain nearly half of the global ocean carbon–climate feedback. The main results reveal a feedback that is initially triggered by warming but that amplifies over time as C
ant invasion enhances the sensitivity of surface pCO₂ to further warming, particularly in the warmer season. Importantly, this “heat–carbon feedback” mechanism is distinct from (and significantly weaker than) what one would expect from temperature-controlled solubility perturbations to pCO₂ alone. It finds independent confirmation in an additional perturbation experiment with the same Earth system model. There mechanism denial is applied by disallowing the secular trend in the physical state of the ocean under climate change, while simultaneously allowing the effects of heating to impact sea surface pCO₂ and thereby CO₂ uptake. Reemergence of C
ant along the equator within the shallow overturning circulation plays an important role in the heat–carbon feedback, with the decadal renewal time scale for thermocline waters modulating the feedback response. The results here for 45°S–45°N stand in contrast to what is found in the high latitudes, where a clear signature of a broader range of driving mechanisms is present.
A near‐future, 2‐K warming climate simulation comprising over 3,000 years of ensemble simulations was performed using 60‐km global and 20‐km regional atmospheric models. Even in the +2‐K climate, ...indices of extreme precipitation and dryness increased significantly in the extratropics compared with the historical climate. Mean precipitation increases in the rainy season and decreases in the dry season, indicating that the seasonal precipitation range becomes amplified with global warming. The intensification of precipitation and dryness from +2 to +4 K was also robust in the mean for climatological wet and arid regions. Around Japan, which was classified as a wet region, the regional atmospheric model predicts that the extreme hourly precipitation in the future climate becomes more extreme on hot days, but slightly weaker on cold days. This extreme precipitation has a high sensitivity to air temperature exceeding 7%/K.
Plain Language Summary
Our study shows the precipitation changes in the near‐future around the 2040s, +2‐K climate. Even in the +2‐K climate, both extreme precipitation and dryness in the extratropics increase significantly. These results urge to plan for adaptation to extreme weather in the near future. The 2‐K warming climate simulation was performed with over 3,000 years of ensemble member using 60‐km global and 20‐km regional atmospheric models. A large number of ensemble data elucidated statistically significant increment of the precipitation extremes, moreover the amplifying of the seasonal precipitation range. The data are helpful to investigate the climate in the near‐future containing mixed uncertainty both internal variation and effects of future scenario. It would provide valuable information for policy‐making, planning of mitigation, and adaptation for extreme weather events, such as flooding or droughts in the near‐future climate.
Key Points
High‐resolution large‐ensemble simulations are performed to assess precipitation change under the +2‐K climate
Even in the +2‐K climate, both extreme precipitation and dryness in the extratropics increase significantly compared with the historical climate
The tendency for the seasonal precipitation range to amplify becomes robust with global warming from the +2‐K to +4‐K climate
The evolution of ocean temperature measurement systems is presented with a focus on the development and accuracy of two critical devices in use today (expendable bathythermographs and ...conductivity‐temperature‐depth instruments used on Argo floats). A detailed discussion of the accuracy of these devices and a projection of the future of ocean temperature measurements are provided. The accuracy of ocean temperature measurements is discussed in detail in the context of ocean heat content, Earth's energy imbalance, and thermosteric sea level rise. Up‐to‐date estimates are provided for these three important quantities. The total energy imbalance at the top of atmosphere is best assessed by taking an inventory of changes in energy storage. The main storage is in the ocean, the latest values of which are presented. Furthermore, despite differences in measurement methods and analysis techniques, multiple studies show that there has been a multidecadal increase in the heat content of both the upper and deep ocean regions, which reflects the impact of anthropogenic warming. With respect to sea level rise, mutually reinforcing information from tide gauges and radar altimetry shows that presently, sea level is rising at approximately 3 mm yr−1 with contributions from both thermal expansion and mass accumulation from ice melt. The latest data for thermal expansion sea level rise are included here and analyzed.
Key Points
Oceanographic techniques and analysis have improved over many decades
These improvements allow more accurate Earth‐energy balance estimates
Understanding of ocean heat content and sea‐level rise has also increased
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