Smart mobility solutions are trending in the mobility domain realized through pilot activities and commercial solutions, but there is a lack of a broad framework defining the readiness to introduce such mobility solutions in a specific area. In this research, smart mobility solutions are examined in the perspective of the Mobility-as-a-Service (MaaS) scheme that is an adequate representation of the maturity of a region regarding smart mobility solutions including technology, business, and coopetition aspects. These three aspects define the feature selection, whereas surveys are used to collect input data from local experts (LEs). For weighting the features, the analytic hierarchy process (AHP) is used with a modified interpretive structural modeling (ISM). With this modification, an expert-friendly process is developed without affecting the results. The elaborated MaaS readiness index (MRI) is applied to six regions with different types of mobility related pilot activities to demonstrate the MRI as a comparison tool between regions and between ex-ante and ex-post pilot activities. The developed interpretive structural modeling with Graph (ISM-G) methodology requites remarkably less work from the evaluators compared to the ISM, while no important difference appeared in the results. The MRI can support smart mobility related pilot evaluations, whereas the ISM-G can be used widely in decision-making.

Attila Aba; Domokos Esztergár-Kiss. Creation of the MaaS readiness index with a modified AHP-ISM method. Communications in Transportation Research 2024, 4 .

Attila Aba, Domokos Esztergár-Kiss. Creation of the MaaS readiness index with a modified AHP-ISM method. Communications in Transportation Research. 2024; 4 ():.

Attila Aba; Domokos Esztergár-Kiss. 2024. "Creation of the MaaS readiness index with a modified AHP-ISM method." Communications in Transportation Research 4, no. : .

This paper deals with group constant parameterization, a necessary step to utilize the results of assembly-level neutronics calculations at the full-core level. The focus is on low sample size problems when the commonly used linear interpolation approach is inadequate, a typical situation of using Monte Carlo codes for group constant generation. This work presents a newly developed code package for automated group constant parameterization. It implements several machine learning regression models − including a novel polynomial regression algorithm − performs hyperparameter optimization and selects the best model based on a detailed evaluation. The applicability of the new code package is demonstrated in a case study for a VVER-1200 fuel assembly covering both normal operation and transient conditions. In this example, the novel polynomial regression model provides a 73 pcm average error in $k_{\mathit{inf}}$ that leads to reactivity coefficients well within the desired precision.

Dániel Sebestény; István Panka; Bálint Batki. Automated group constant parameterization for low sample sizes using different Machine learning approaches. Annals of Nuclear Energy 2024, 204 .

Dániel Sebestény, István Panka, Bálint Batki. Automated group constant parameterization for low sample sizes using different Machine learning approaches. Annals of Nuclear Energy. 2024; 204 ():.

Dániel Sebestény; István Panka; Bálint Batki. 2024. "Automated group constant parameterization for low sample sizes using different Machine learning approaches." Annals of Nuclear Energy 204, no. : .

Understanding spray evolution in a reacting environment is critical to designing advanced, clean combustion systems. The processes in the upstream region determine flame shape, stability, ignition characteristics, pollutant emission, and combustion efficiency. The developed spray is never achieved in combustion since the early regions feature primary and secondary atomization, while droplets evaporate as they approach the flame. Consequently, there is no thermodynamic equilibrium before the flame front. The principal goal of this paper is to provide detailed information to model developers on various sprays measured by a Phase Doppler Anemometer; the processed measurement data is available as supplementary material, while the raw data will be provided upon request. Four different fuels were tested: diesel fuel, aviation kerosene type JP-8, biodiesel, and a 50 % biodiesel-diesel blend by volume. The plain-jet airblast atomizer was tested at four atomization gauge pressures (0.3, 0.45, 0.6, 0.75 bar_g). Therefore, sixteen different sprays were measured along one spray diameter at each of four downstream distances of 15, 25, 35, and 45 mm, measured from the nozzle tip. The paper details the droplet size distribution, droplet axial velocity, fluctuations, and correlation between size and velocity to facilitate a comprehensive understanding of liquid fuel sprays. This latter measure helps identify the overshooting phenomenon, i.e., localizing the regions where the large droplets move faster than the gas phase.

Réka Anna Kardos; Erika Rácz; Milan Malý; Jan Jedelský; Viktor Józsa. Detailed spray analysis of airblast atomization of various fuels in a reacting environment. International Journal of Heat and Mass Transfer 2024, 227 .

Réka Anna Kardos, Erika Rácz, Milan Malý, Jan Jedelský, Viktor Józsa. Detailed spray analysis of airblast atomization of various fuels in a reacting environment. International Journal of Heat and Mass Transfer. 2024; 227 ():.

Réka Anna Kardos; Erika Rácz; Milan Malý; Jan Jedelský; Viktor Józsa. 2024. "Detailed spray analysis of airblast atomization of various fuels in a reacting environment." International Journal of Heat and Mass Transfer 227, no. : .