In the decommissioning work of Tokyo Electric Power Company Holdings Fukushima Daiichi nuclear power plant, the water contained in the fuel debris is radiolyzed when the fuel debris containing water is taken out from the bottom of the reactor vessel and packed into a storage container, and as a result, hydrogen and oxygen will generate in the storage container. Since hydrogen is flammable gas, it has a high risk of combustion and explosion. In order to reduce the hydrogen concentration and to secure long-term integrity of the storage container for fuel debris, Passive Autocatalytic Recombiner (PAR) was used. It consists of a spherical alumina as a base material and a small amount of platinum coated on the outer surface, and a prototype of PAR was fabricated and installed into an experimental container. The experimental container roughly simulated the volume of a currently designed fuel debris long-term storage container. In order to clarify the controlling factors affecting the reduction of hydrogen concentration in the storage vessel including PAR and the magnitude of the effect due to those, parameter experiments were carried out for each of the supposed controlling factors. As controlling factors, the hydrogen flow rate, the quantity of PAR, the installation position of PAR, the water-repellent treated PAR, the porosity in a container and so on were taken into consideration, and their effects on the reduction of hydrogen concentration were quantitatively evaluated. As a result, the conditions of each controlling were clarified to reduce the hydrogen concentration in the experimental container with PAR to less than 4 wt%, which is the lower explosion limit of hydrogen.

To clarify the effects of reaction mechanisms on soot formation, a two-dimensional numerical simulation was performed for a burner-stabilized stagnation flame. The GRI-3.0, Appel-Bockhorn-Frenklach (ABF), and DLR models were employed as representative reaction mechanisms. The soot particle formation model was a two-equation model, and only acetylene was assumed to be the soot-nucleating species. The ambient pressure and inlet temperature were 0.1 MPa and 473 K, respectively, and the distance between the burner surface and the stagnation plate (H_p) varied from 5.5 to 12 mm. The simulation was conducted using OpenFOAM, and the results were validated with experimental data based on the temperature distribution and soot volume fraction at H_p. After soot nucleation began, the number density became large in the preheating zone. As the soot transported downstream, coagulation and surface growth accelerated, and the mass density became large. Furthermore, while the effect of the reaction mechanism on temperature distribution was small, the effect on the soot growth rate was large. In the preheating zone, the DLR model showed the largest nucleation rate, which could be due to the abundant acetylene formation reaction. At the downstream, the DLR model showed the smallest nucleation rate, which is probably because many acetylene consumption reactions were included in the DLR model. As a result, the soot volume fraction of the DLR model at H_p was smaller than that of the GRI-3.0 and ABF models. These results suggest that the reaction mechanism, especially the fuel-pyrolysis sub-mechanism, has a significant impact on the prediction of soot formation.

In this study, an attempt has been made to apply ammonia to the heating furnaces, which is the current major CO₂ source in the industrial division. The use of the highly preheated air around 1000℃ differs the combustion field of the heating furnaces from the others, and NH₃ application with such highly preheated air has hardly been reported. Then, in this study, a bench-scale furnace capable of creating a combustion environment at elevated inlet and in-furnace gas temperature has been designed and constructed. With a fuel of 30% ammonia and 70% methane based on the lower heating value (55% ammonia and 45% methane based on volumetric flow rate), radical profiles, exhaust gas compositions, and gaseous distributions inside the furnace have been measured. Fuel injection configurations are changed by using 4 “side nozzles” and 1 “center nozzle”. The former is located just inside the annular air nozzle, while the latter is further inside. The results show that the addition of NH₃ to CH₄ drastically changes the profiles of OH, NH, and CN radicals and, thus, the structure of the reaction zone. Then, the peaks of radical chemi-luminescence are higher in the case of side-CH₄ injection than side-NH₃. The higher peak indicates the formation of the hot spot for the side-CH₄ case, and thus, exhaust NO concentration is found to be high (over 1000ppm) for the side-CH₄ case. The results of the gaseous measurements inside the furnace indicate elevated NO concentrations at all points, which is in accordance with high NO at the exhaust. Besides its high NO concentration, NH₃ and N₂O have hardly been detected due to the high-temperature inlet air and atmosphere.

Thermodynamic calculations are important procedures for preliminary design or performances assessment of supercritical carbon dioxide (SCO₂) centrifugal compressors. However, the thermodynamic calculations are usually based on the discrete database (NIST REFPROP). No analytical models with enough accuracies are alternative for the thermodynamic calculations of SCO₂ compressors. For this, a hybrid analytical model is proposed for their thermodynamic calculations. Firstly, a BWRS-based model for thermodynamic properties calculation of CO₂ is derived by combining the BWRS equation of state (EOS) and the fundamental thermodynamic relations. Validations show that the accuracy of the model is acceptable except for the regions near the critical point as well as the relatively low temperature range (300~360 K). Then, a polynomial model is introduced for the regions with large errors. Eventually, a hybrid model is established by combining the BWRS-based model and the polynomial model. The hybrid model is used to calculate the thermodynamic properties of the Sandia SCO₂ compressor, and applied to the preliminary design of a high-pressure-ratio SCO₂ compressor. The maximum errors of the two test cases are about 1.5% and 2.6% respectively compared to the reference values. The hybrid model has provided an alternative approach for the thermodynamic calculations of SCO₂ compressors.

Direct absorption solar collectors are a solution to the problem of energy depletion: they work by dispersing plasmonic nanoparticles in a liquid and exposing them directly to sunlight. Improving the sunlight absorption performance of plasmonic nanoparticles is an important issue to increase their utilization efficiency and reduce their production cost. To solve this problem, we have proposed metal-insulator-magnet plasmonic nanoparticles with a layered structure consisting of a spherical insulator sandwiched between thin films. These particles can be easily fabricated by sputtering a thin film on a spherical insulator, which significantly reduces the amount of material used and the process is inexpensive. However, the combination of factors that determine the radiative properties of the particles is enormous. Therefore, the goal of this study is to find the optimal particle design using machine learning. Three types of machine learning were used: neural networks, support vector machines, and light gradient boosting machines. Learning is done by performing an electromagnetic field analysis based on the finite element method and using the calculated radiative properties as the correct values. The accuracy of the machine learning was evaluated by predicting the absorption property from the particle parameters. The constructed machine learning code was then used to optimize the particle parameters. It was shown that machine learning is effective for optimization design of objects with a large number of parameters.

Fire whirls, also known as fire tornadoes, represent an extraordinarily severe fire phenomenon, exemplified by the catastrophic events shortly after the great Kanto earthquake in 1923. This research aims to propose an early-warning analysis employing image processing techniques for predicting before the fire-whirl formation. Scaled-down experiments were conducted using two speed-adjustable fans to control the wind movement and generate fire whirls. Through image processing, a set of input flame images is transformed into the evolution of flame height as output data, which serves as the basis of the signal processing. The difference between whether fire whirls occur or not can be detected from the flickering of the flame-height signal. Without delivering external winds, minor changes in noise components are observed at subsequent times, showing no signs of fire whirls. On the other hand, the noise component preceding the fire whirl occurrence highlights a significant increase in standard deviation and autocorrelation, attributing to a slower recovery rate from a perturbed state near a transition to a fire whirl. In this paper, a dynamical marker is constructed as a composite metric of smoothed flame height, standard deviation, and autocorrelation coefficient at lag 1 of the noise component, showing upward trends prior to fire whirl formation. The effectiveness of the dynamical marker as a warning sign for predicting fire-whirl occurrences is validated through experiments of three different wind speeds with the alarm threshold of +3σ to mitigate an unnecessary false detection.