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The effective utilization of the cryogenic exergy associated with liquefied natural gas (LNG) vaporization is important. In this paper, a novel combined power cycle is proposed which utilizes LNG in different ways to enhance the power generation of a power plant. In addition to the direct expansion in the appropriate expander, LNG is used as a low-temperature heat sink for a middle-pressure gas cycle which uses nitrogen as working fluid. Also, LNG is used to cool the inlet air of an open Brayton gas turbine cycle. These measures are accomplished to improve the exergy recovery of LNG. In order to analyze the performance of the system, the influence of several key parameters such as pressure ratio of LNG turbine, ratio of the mass flow rate of LNG to the mass flow rate of air, pressure ratio of different compressors, LNG pressure and inlet pressure of nitrogen compressor, on the thermal efficiency and exergy efficiency of the offered cycle is investigated. Finally, the proposed combined cycle is optimized on the basis of first and second laws of thermodynamics. Nomenclature mMass flow rate (kg/s) h Enthalpy (kJ/kg) LHV Lower heating value (kJ/kg) T Temperature (K) e Specific exergy (kJ/kg) E Exergy (kW)

Current empirical and semi-empirical based design manuals are restricted to the analysis of simple building configurations against blast loading. Prediction of blast loads for complex geometries is typically carried out with computational fluid dynamics solvers, which are known for their high computational cost. The combination of high-fidelity simulations with machine learning tools may significantly accelerate processing time, but the efficacy of such tools must be investigated. The present study evaluates various machine learning algorithms to predict peak overpressure and impulse on a protruded structure exposed to blast loading. A dataset with over 250,000 data points extracted from ProSAir simulations is used to train, validate, and test the models. Among the machine learning algorithms, gradient boosting models outperformed neural networks, demonstrating high predictive power. These models required significantly less time for hyperparameter optimization, and the randomized search approach achieved relatively similar results to that of grid search. Based on permutation feature importance studies, the protrusion length was considered a significantly more influential parameter in the construction of decision trees than building height.

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