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We developed an operational solar flare prediction model using deep neural networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two categories, such as >=M-class and =C-class and =M-class flares and TSS = 0.63 for >=C-class flares. For comparison, we evaluated the operationally forecast results from January 2019 to June 2020. We found that operational DeFN forecasts achieved TSS = 0.70 (0.84) for >=C-class flares with the probability threshold of 50 (40)%, although there were very few M-class flares during this period and we should continue monitoring the results for a longer time. Here, we adopted a chronological split to divide the database into two for training and testing. The chronological split appears suitable for evaluating operational models. Furthermore, we proposed the use of time-series cross-validation. The procedure achieved TSS = 0.70 for >=M-class flares and 0.59 for >=C-class flares using the datasets obtained from 2010 to 2017., Comment: 12 pages, 4 figures, 4 tables, online published in EPS

This study aims to experimentally investigate the two-group interfacial area transport in small diameter pipes. Experimental data focusing on the bubbly to slug transition regime, namely one-group to two-group transport region, are collected in a 12.7 mm vertical pipe under adiabatic, air-water conditions. The result shows the intergroup transfer in the small diameter pipe can be drastic, especially under low superficial liquid velocities. The cause of this phenomenon is mainly due to the large relative bubble size comparing to the pipe cross-sectional area. The wake entrainment effect could be enhanced by the small spherical bubbles that are acting like cap or slug bubbles in a medium-size pipe. Based on the experimental observation, a throughout analysis of the dependence of the drastic intergroup transfer is provided in this study. The models predicting the initiation of drastic intergroup transfer in small diameter pipes in terms of the bubble diameter and the void fraction are developed. These models are compared with the two-phase data among the different pipe sizes and the results show a good agreement. These newly developed models are applied to the IATE wake entrainment model by developing a transition function analogous to the sigmoid function. With the transition function, the revised IATE model is given the new ability on predicting the drastic intergroup transfer phenomenon.

This experimental study focuses on the characteristics of air-water two-phase interfacial structure. Interfacial parameters including void fraction, interfacial area concentration, and bubble interfacial velocity are measured using four-sensor electrical conductivity probe on a 12.7 mm ID vertical tube. The tube size is approximately equal to the maximum distorted bubble size. Therefore, the bubbly-to-slug transition characteristics can be different from in other sizes of tubes. Comparing with previous studies, this study provides an experimental database with a wide range on the bubbly-to-slug transition flows, with 4 different superficial liquid velocities (0.3, 0.5, 1.0, and 2.0 m/s) and void fraction ranging from 0.07 to 0.66. Experimental results show that the wall-peak void distribution does not appear in a small diameter tube under the bubbly-to-slug flow transition flow. The distribution is related with both void fraction and the relative bubble size to the tube size. In this sense, a new correlation of distribution parameter in the Drift Flux model is proposed based on the previous studies by Ishii and Hibiki et al. This experimental study can be a good reference for the model development of flow regime transition and the Interfacial Area Transport Equation., Comment: 22 pages, 15 figures. Preprint submitted to International Journal of Heat and Mass Transfer

We developed a reliable probabilistic solar flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur in the following 24 h after observing images, along with the event occurrence probability. We detected active regions from 3x10^5 solar images taken during 2010-2015 by Solar Dynamic Observatory and extracted 79 features for each region, which we annotated with flare occurrence labels of X-, M-, and C-classes. The extracted features are the same as used by Nishizuka et al. (2018); for example, line-of-sight/vector magnetograms in the photosphere, brightening in the corona, and the X-ray emissivity 1 and 2 h before an image. We adopted a chronological split of the database into two for training and testing in an operational setting: the dataset in 2010-2014 for training and the one in 2015 for testing. DeFN-R is composed of multilayer perceptrons formed by batch normalizations and skip connections. By tuning optimization methods, DeFN-R was trained to optimize the Brier skill score (BSS). As a result, we achieved BSS = 0.41 for >=C-class flare predictions and 0.30 for >=M-class flare predictions by improving the reliability diagram while keeping the relative operating characteristic curve almost the same. Note that DeFN is optimized for deterministic prediction, which is determined with a normalized threshold of 50%. On the other hand, DeFN-R is optimized for a probability forecast based on the observation event rate, whose probability threshold can be selected according to users' purposes.

The prediction of interfacial structure in two-phase flow systems is difficult and challenging. In this paper, a novel physics-informed reinforcement learning-aided framework (PIRLF) for the interfacial area transport is proposed. A Markov Decision Process that describes the bubble transport is established by assuming that the development of two-phase flow is a stochastic process with Markov property. The framework aims to capture the complexity of two-phase flow using the advantage of reinforcement learning (RL) in discovering complex patterns with the help of the physical model (Interfacial Area Transport Equation) as reference. The details of the framework design are described including the design of the environment and the algorithm used in solving the RL problem. The performance of the PIRLF is tested through experiments using the experimental database for vertical upward bubbly air-water flows. The result shows a good performance of PIRLF with rRMSE of 6.556%. The case studies on the PIRLF performance also show that the type of reward function that is related to the physical model can affect the framework performance. Based on the study, the optimal reward function is established. The approaches to extending the capability of PIRLF are discussed, which can be a reference for the further development of this methodology.

Long short-term memory (LSTM) and recurrent neural network (RNN) has achieved great successes on time-series prediction. In this paper, a methodology of using LSTM-based deep-RNN for two-phase flow regime prediction is proposed, motivated by previous research on constructing deep RNN. The method is featured with fast response and accuracy. The built RNN networks are trained and tested with time-series void fraction data collected using impedance void meter. The result shows that the prediction accuracy depends on the depth of network and the number of layer cells. However, deeper and larger network consumes more time in predicting.

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h 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 =C class versus =10^7 K) and the X-ray and 131 A intensity data 1 and 2 h before an image. For operational evaluation, we divided the database into two for training and testing: the dataset 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., Comment: 24 pages, 7 figures, 3 tables, Accepted for publication in ApJ

In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance difference between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting., Comment: 29 pages, 7 figures and 6 tables. Accepted for publication in Journal of Space Weather and Space Climate (SWSC)