ABSTRACT Accurate forecasting of carbon emissions from power generation enterprises is essential under China's dual‐control policy. Although deep learning methods show strong potential, studies on their optimal configuration remain limited. This paper proposed a hybrid deep learning framework integrating a convolutional neural network (CNN), bidirectional long short‐term memory (BiLSTM), and an attention mechanism for carbon emission prediction in natural gas power plants. The present study utilized two distinct optimization methodologies: a structured design strategy encompassing light, medium, and heavy configurations, while the other employed Bayesian optimization for hyperparameter tuning. The models were evaluated using 5‐fold cross‐validation on 619 operational samples from two 487.1‐MW condensing units in a power plant in Hainan, China. The medium configuration achieved the best balance between accuracy, efficiency, and stability, with R 2 = 0.9833, RMSE = 0.0342, and MAE = 0.0242. Under small‐sample conditions, the structured design approach outperformed Bayesian optimization by 0.16% in accuracy while requiring only 7.42% of the training time. The proposed framework provides an efficient and interpretable reference for selecting deep learning architectures in small‐sample industrial regression tasks and supports intelligent, low‐carbon power generation applications.

We designed and built a three degrees-of-freedom (DOF) flapping wing robot, Flapperoo, to study the aerodynamic benefits of wing folding and twisting. Forces and moments of this physical model are measured in wind tunnel experiments over a Strouhal number range of St = 0.2–0.4 - typical for animal flight. We perform particle image velocimetry (PIV) measurements to visualize the air jet produced by wing clapping under the ventral side of the body when wing folding is at the extreme. The results show that this jet can be directed by controlling the wing twist at the moment of clapping, which leads to greatly enhanced cycle-averaged thrust, especially at high St or low flight speeds. Additional benefits of more thrust and less negative lift are gained during upstroke using wing twist. Remarkably, less total actuating force, or less total power, is required during upstroke with wing twist. These findings emphasize the benefits of critical wing articulation for the future flapping wing/fin robots and for an accurate test platform to study natural flapping wing flight or underwater vehicles.

Inspired by bat flight performance, we explore the advantages of wing twist and fold for flapping wing robots. For this purpose, we develop a dynamical model that incorporates these two degrees of freedom to the wing. The twist is assumed to be linearly-increasing along the wing, while the wing fold is modeled as a relative rotation of the handwing with respect to the armwing. An optimization scheme parameterizes the wing kinematics for 2, 5 and 8 m/s forward flight velocities. The intricate interplay between wing orientation, effective angle of attack and the ensuing lift and thrust generation are discussed. The results show that wing twist and fold alleviate negative lift and thrust in the upstroke, and in some cases producing persistent positive thrust throughout cycle for handwing. As a result, power consumption drops precipitously compared to the base case of a rigid flat plate. Another crucial realization is the relative importance of wing twist and fold in achieving efficient flight strongly depends on speeds. At slow flight, twist is significantly more effective in minimizing the power, but becomes energetically inefficient for fast speeds. The results also show that a 45° wing fold during upstroke is energetically beneficial for all speeds. The synergy of wing twist and fold are most prominent at slow flight. These findings provides useful guidelines for designing flapping wing robots.