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Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set. [ABSTRACT FROM AUTHOR]

Reservoirs in the Weicheng Oilfield, which occur in Paleogene Shahejie Formation Member 4 (Es4), were formed during the initial and strongly rifting stage of the Dongpu Depression. The geometry and distribution of sands of different types have controlled the heterogeneity of the reservoirs and have resulted in exploration challenges and relatively low recoveries. Based on various static and dynamic geological data including drilling, logging, seismic and production data, the microfacies features and distribution of Es4 were analyzed. Geostatistical three-dimensional (3-D) models and an integrated numerical simulation were constructed to quantify the geometry, spatial distribution and continuity of the reservoir sands and to predict the remaining oil distribution rules. Two depositional systems were recognized in Es4: terminal fans deposits in the lower part and partial shallow-lake deposits in the upper part. The three-dimensional geological model provided insights into the external and internal geometries, distributions and physical properties of the reservoir units. A sedimentary and connectivity feature analysis suggested four types of flow units: Type I units, which are primary sands of distributary channels and bars; Type II units, which are primary sands of proximal overbank and beaches; Type III units, which are sands of distal overbank; and Type IV units, which are mud flats and inter-beach-bar. The numerical simulation reproduced the changing process of the remaining oil, and the areas that have both high remaining oil saturations and reserve abundance, which are controlled by micro-facies and influenced by the structural trend and injection-production relations, comprise the key potential reservoirs: the sand pinch-out area, the imperfect pattern area and structural highs in Type I units and most of the areas in Type II units. [ABSTRACT FROM AUTHOR]

Model predictive torque control with duty cycle control (MPTC-DCC) is widely used in motor drive systems because of its low torque ripple and good steady-state performance. However, the selection of the optimal voltage vector and the calculation of the duration are extremely dependent on the accuracy of the motor parameters. In view of this situation, A modified MPTC-DCC is proposed in this paper. According to the variation of error between the measured value and the predicted value, the motor parameters are calculated in real-time. Meanwhile, Model reference adaptive control (MRAC) is adopted in the speed loop to eliminate the disturbance caused by the ripple of real-time update parameters, through which the disturbance caused by parameter mismatch is suppressed effectively. The simulation and experiment are carried out on MATLAB / Simulink software and dSPACE experimental platform, which corroborate the principle analysis and the correctness of the method. [ABSTRACT FROM AUTHOR]