Thermodynamic and techno-economic analysis of a direct thermal oil vaporization solar power system

A unique direct thermal oil vaporization solar power system employing cascade organic-steam Rankine cycle is proposed. The oil is a mixture of biphenyl and diphenyl oxide, and it is used for heat transfer, storage and power cycle fluid in the novel system. Stable electricity output and prolonged storage capacity can be facilitated. In the rated mode, the oil is vaporized at 390 °C in the collectors and drives a top turbine. The exhaust heat is used for preheating and evaporating water of the bottom cycle. Meanwhile, the hot oil in a high-temperature tank (HTT) superheats and reheats the generated steam. When the irradiation is insufficient, the heat released by the oil from the HTT to a low-temperature tank drives the bottom cycle. Fundamentals, thermodynamic performance and techno-economic feasibility are elaborated. The results indicate that, compared with the mainstream dual-tank solar power systems, the proposed system has a higher thermal efficiency with a lower water evaporation temperature (42.90% at 260 °C vs. 38.06% at 310 °C) and a larger temperature drop between the two tanks (121 °C vs. 100 °C). The equivalent payback time with respect to the top oil cycle is less than 3 years

Thermo-economic analysis of a novel partial cascade organic-steam Rankine cycle

Conventional heat batteries and concentrated solar power systems adopt subcritical steam Rankine cycles (SRCs) to avoid the technical challenges of supercritical cycles. The water evaporation temperature of 310–337 °C and live steam pressure of 10–14 MPa limit the cycle efficiency (around 42%). This paper proposes a novel partial cascade organic-steam Rankine cycle (ORC-SRC) system to increase the fluid evaporation temperature and thermal efficiency. The ORC-SRC uses a mixture of biphenyl and diphenyl oxide as the top cycle fluid. The mixture absorbs heat from the molten salts and evaporates at about 400 °C to drive a turbine, and then the exhaust vapor releases heat to the bottom SRC. The ORC contributes to saturated steam generation, and molten salts supply the rest heat to the SRC through the steam superheater and reheater. The fundamentals of the system are illustrated, and mathematical models are built. Thermo-economic performance of the system is investigated. The results show that the proposed system significantly increases the average temperature of the power fluid in the heating process, leading to a maximum cycle efficiency of 45.3%. Meanwhile, the moderate live steam pressure of 7.44 MPa in the SRC reduces the leakage loss of the high-pressure turbine and equipment costs. Despite a smaller temperature drop of molten salts during discharge, the equivalent payback period of the ORC-SRC is within 4 years

Holistic Strategies Lead to Enhanced Efficiency and Stability of Hybrid Chemical Vapor Deposition Based Perovskite Solar Cells and Modules

Hybrid chemical vapor deposition (HCVD) is a promising method for the up-scalable fabrication of perovskite solar cells/modules (PSCs/PSMs). However, the efficiency of the HCVD-based perovskite solar cells still lags behind the solution-processed PSCs/PSMs. In this work, the oxygen loss of the electron transport layer of SnO2 in the HCVD process and its negative impact on solar cell device performance are revealed. As the counter-measure, potassium sulfamate (H2KNO3S) is introduced as the passivation layer to both mitigate the oxygen loss issue of SnO2 and passivate the uncoordinated Pb2+ in the perovskite film. In parallel, N-methylpyrrolidone (NMP) is used as the solvent to dissolve PbI2 by forming the intermediate phase of PbI2•NMP, which can greatly lower the energy barrier for perovskite nucleation in the HCVD process. The perovskite seed is employed to further modulate the kinetics of perovskite crystal growth and improve the grain size. The resultant solar cells yield a champion power conversion efficiency (PCE) of 21.98% (0.09 cm2) with a stable output performance of 21.15%, and the PCEs of the mini-modules are 16.16% (22.4 cm2, stable output performance of 14.72%) and 12.12% (91.8 cm2). Furthermore, the unencapsulated small area device shows an outstanding operational stability with a T80 lifetime exceeding 4000 h.journal articl

Real-Time Multimodal 3D Object Detection with Transformers

The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combining both can improve results but incurs significant computational overhead, affecting real-time performance. To address these challenges, this paper presents a real-time multimodal fusion model called Fast Transfusion that combines the benefits of LiDAR and camera sensors and reduces the computational burden of their fusion. Specifically, our Fast Transfusion method uses QConv (Quick Convolution) to replace the convolutional backbones compared to other models. QConv concentrates the convolution operations at the feature map center, where the most information resides, to expedite inference. It also utilizes deformable convolution to better match the actual shapes of detected objects, enhancing accuracy. And the model incorporates EH Decoder (Efficient and Hybrid Decoder) which decouples multiscale fusion into intra-scale interaction and cross-scale fusion, efficiently decoding and integrating features extracted from multimodal data. Furthermore, our proposed semi-dynamic query selection refines the initialization of object queries. On the KITTI 3D object detection dataset, our proposed approach reduced the inference time by 36 ms and improved 3D AP by 1.81% compared to state-of-the-art methods

A feature selection method with feature ranking using genetic programming

Feature selection is a data processing method which aims to select effective feature subsets from original features. Feature selection based on evolutionary computation (EC) algorithms can often achieve better classification performance because of their global search ability. However, feature selection methods using EC cannot get rid of invalid features effectively. A small number of invalid features still exist till the termination of the algorithms. In this paper, a feature selection method using genetic programming (GP) combined with feature ranking (FRFS) is proposed. It is assumed that the more the original features appear in the GP individuals' terminal nodes, the more valuable these features are. To further decrease the number of selected features, FRFS using a multi-criteria fitness function which is named as MFRFS is investigated. Experiments on 15 datasets show that FRFS can obtain higher classification performance with smaller number of features compared with the feature selection method without feature ranking. MFRFS further reduces the number of features while maintaining the classification performance compared with FRFS. Comparisons with five benchmark techniques show that MFRFS can achieve better classification performance

Preparation and thermophysical properties of graphite flake-carbon fiber coreinforced copper matrix composites

Graphite flake-carbon fiber coreinforced copper matrix composites were prepared by vacuum hot pressing technology. The carbon fibers were dispersed ultrasonic in alcohol and then mixed with graphite flake and alloys powder (Zr and Cu) for hot pressing sintering. The effects of the carbon fiber content on the microstructure, bending strength and thermal conductivity of the composites were investigated. The results show that the interface of the composites is well bonded. When the volume fraction of carbon fiber is 1%–3%, the carbon fiber can be uniformly dispersed in the matrix, and the bending strength of the composites can be improved effectively. When the volume fraction of carbon fiber is 2%, the bending strength reaches a maximum of 152 MPa, which is an increase of 60% compared with that of the composites without carbon fiber. However, an excessive addition of carbon fiber (4% or more) leads to an uneven distribution of carbon fiber, and the bending strength of the composites decreases. When the volume fraction of carbon fiber is 2%, the thermal conductivity of the composite is 597 W·m ^−1 ·K ^−1 . The acoustic mismatch model (AMM) associated with the Digimat MF module is able to predict the thermal conductivity of the anisotropic multiphase composites

Up-Scalable Fabrication of SnO2 with Multifunctional Interface for High Performance Perovskite Solar Modules

Tin dioxide (SnO2) has been demonstrated as one of the promising electron transport layers for high-efficiency perovskite solar cells (PSCs). However, scalable fabrication of SnO2 films with uniform coverage, desirable thickness and a low defect density in perovskite solar modules (PSMs) is still challenging. Here, we report preparation of high-quality large-area SnO2 films by chemical bath deposition (CBD) with the addition of KMnO4. The strong oxidizing nature of KMnO4 promotes the conversion from Sn(II) to Sn(VI), leading to reduced trap defects and a higher carrier mobility of SnO2. In addition, K ions diffuse into the perovskite film resulting in larger grain sizes, passivated grain boundaries, and reduced hysteresis of PSCs. Furthermore, Mn ion doping improves both the crystallinity and the phase stability of the perovskite film. Such a multifunctional interface engineering strategy enabled us to achieve a power conversion efficiency (PCE) of 21.70% with less hysteresis for lab-scale PSCs. Using this method, we also fabricated 5 × 5 and 10 × 10 cm2 PSMs, which showed PCEs of 15.62% and 11.80% (active area PCEs are 17.26% and 13.72%), respectively. For the encapsulated 5 × 5 cm2 PSM, we obtained a T80 operation lifetime (the lifespan during which the solar module PCE drops to 80% of its initial value) exceeding 1000 h in ambient condition

Phase Aggregation Suppression of Homogeneous Perovskites Processed in Ambient Condition toward Efficient Light‐Emitting Diodes

Perovskite light-emitting diodes (PeLEDs) have attracted attention because of their high efficiencies. However, due to the sensitivity of perovskites to ambient condition, perovskite emitter layers are generally fabricated under an inert gas environment (e.g., dry N2), which increases processing complexity and cost. Here, air-prepared quasi-2D perovskites are reported for efficient PeLEDs. It is found that the phase aggregation is the major obstacle deteriorating the characteristics of air-prepared perovskites. Through antisolvent engineering to modulate the nucleation and growth characteristics of perovskite films from precursor solution, phase aggregations are restrained. Confocal laser scanning fluorescence microscopy results demonstrate homogeneous perovskite films with uniform photoluminescence distributions. Traps at grain boundaries are passivated, and exciton transfer among perovskite phases becomes effective. Finally, efficient green PeLEDs based on air-prepared perovskites are realized with an external quantum efficiency of 15.4%. This work provides a promising strategy to fabricate cost-effective perovskite devices in ambient air condition