Cobalt-based alloy coatings and WC-Co-based ceramic–metal (cermet) coatings have been widely used because of their desirable mechanical properties and corrosion resistance. In this work, the influence of Co content on the microstructure, mechanical properties and cavitation erosion (CE) resistance were investigated. A cobalt-based alloy coating, a WC-12Co coating, and a WC-17Co cermet coating were deposited by high-velocity oxygen fuel (HVOF) spraying on 1Cr18Ni9Ti substrates. Results indicate that the cobalt-based alloy coating had the largest surface roughness because surface-bonded particles of lower plastic deformation were flattened. The existence of WC particles had led to an increase in hardness and improved the fracture toughness due to inhibit crack propagation. The pore appeared at the interface between WC particles, and the matrix phase had introduced an increase in porosity. With the increase in Co content, the cohesion between matrix friction and WC particles increased and then decreased the porosity (from 0.99% to 0.84%) and surface roughness (R_a from 4.49 to 2.47 μm). It can be concluded that the hardness had decreased (from 1181 to 1120 HV_0.3) with a decrease in WC hard phase content. On the contrary, the fracture toughness increased (from 4.57 to 4.64 MPa∙m^1/2) due to higher energy absorption in the matrix phase. The WC-12Co and WC-17Co coatings with higher hardness and fracture toughness exhibited better CE resistance than the cobalt-based alloy coating, increasing more than 20% and 16%, respectively. Especially, the WC-12Co coating possessed the best CE resistance and is expected to be applicable in the hydraulic machineries.

Considering energy conversion efficiency, pollution emissions, and economic benefits, combining biomass with fossil fuels in power generation facilities is a viable approach to address prevailing energy deficits and environmental challenges. This research aimed to investigate the thermodynamic and exergoeconomic performance of a novel power and cooling cogeneration system based on a natural gas–biomass dual fuel gas turbine (DFGT). In this system, a steam Rankine cycle (SRC), a single-effect absorption chiller (SEAC), and an organic Rankine cycle (ORC) are employed as bottoming cycles for the waste heat cascade utilization of the DFGT. The effects of main operating parameters on the performance criteria are examined, and multi-objective optimization is accomplished with a genetic algorithm using exergy efficiency and the sum unit cost of the product (SUCP) as the objective functions. The results demonstrate the higher energy utilization efficiency of the proposed system with the thermal and exergy efficiencies of 75.69% and 41.76%, respectively, while the SUCP is 13.37 $/GJ. The exergy analysis reveals that the combustion chamber takes the largest proportion of the exergy destruction rate. The parametric analysis shows that the thermal and exergy efficiencies, as well as the SUCP, rise with the increase in the gas turbine inlet temperature or with the decrease in the preheated air temperature. Higher exergy efficiency and lower SUCP could be obtained by increasing the SRC turbine inlet pressure or decreasing the SRC condensation temperature. Finally, optimization results indicate that the system with an optimum solution yields 0.3% higher exergy efficiency and 2.8% lower SUCP compared with the base case.

In this study, an optimization method using various multi-output regression models as model proxies within the NSGA-II framework was applied to determine the geometric parameters (P, W, D) of L-shaped twisted tape inserts for achieving the optimal overall heat transfer performance in a circular heat exchange tube. Herein, 4 multi-output regression models, namely, MOLR, MOSVR, MOGPR, and BPNN, were selected as proxy models and trained on a dataset containing 74 groups of data. The training results indicated that the MOGPR model, balancing high accuracy and low error conditions, exhibited moderate training times among the four algorithms. BPNN showed a comparatively lower comprehensive training effect, obtaining training accuracy close to that of the MOGPR algorithm but with approximately twice the training time. The worst fitting performance was gained with the MOSVR algorithm. Due to its fitting performance, the MOSVR algorithm was excluded from the subsequent NSGA-II model proxy. Through multi-objective optimization with NSGA-II, the optimal structural dimensions for three sets of L-shaped twisted tape inserts were obtained to achieve the best overall heat transfer efficiency within the tube.