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Power generation is an important part of air vehicle energy management when developing long-endurance and reusable hypersonic aircraft. In order to utilize an air turbine power generation system on board, fuel-based rotating cooling has been researched to cool the turbine’s rotor blades. For fuel-cooling air turbines, each blade corresponds to a separate cooling channel. All the separate cooling channels cross together and form a distributary cavity and a confluence cavity in the center of the disk. In order to determine the flow characteristics in the distributary and confluence cavities, computational fluid dynamics (CFD) simulations using the shear–stress–transport turbulence model were carried out under the conditions of different rotating speeds and different mass flow rates. The results showed great differences between non-rotating flow and rotating flow conditions in the distributary and confluence cavities. The flow in the distributary and confluence cavities has rotational velocity, with obvious layering distribution regularity. Moreover, a high-speed rotational flow surface is formed in the confluence cavity of the original structure, due to the combined functions of centrifugal force, inertia, and the Coriolis force. Great pressure loss occurs when fluid passes through the high-speed rotational flow surface. This pressure loss increases with the increase in rotating speed and mass flow rate. Finally, four structures were compared, and an optimal structure with a separated outlet channel was identified as the best structure to eliminate this great pressure loss.

Power generation is an important part of air vehicle energy management when developing long-endurance and reusable hypersonic aircraft. In order to utilize an air turbine power generation system on board, fuel-based rotating cooling has been researched to cool the turbine’s rotor blades. For fuel-cooling air turbines, each blade corresponds to a separate cooling channel. All the separate cooling channels cross together and form a distributary cavity and a confluence cavity in the center of the disk. In order to determine the flow characteristics in the distributary and confluence cavities, computational fluid dynamics (CFD) simulations using the shear–stress–transport turbulence model were carried out under the conditions of different rotating speeds and different mass flow rates. The results showed great differences between non-rotating flow and rotating flow conditions in the distributary and confluence cavities. The flow in the distributary and confluence cavities has rotational velocity, with obvious layering distribution regularity. Moreover, a high-speed rotational flow surface is formed in the confluence cavity of the original structure, due to the combined functions of centrifugal force, inertia, and the Coriolis force. Great pressure loss occurs when fluid passes through the high-speed rotational flow surface. This pressure loss increases with the increase in rotating speed and mass flow rate. Finally, four structures were compared, and an optimal structure with a separated outlet channel was identified as the best structure to eliminate this great pressure loss.

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

Iron-based oxygen carriers (OCs) have received much attention due to their low costs, high mechanical strengths and high-temperature stabilities in the chemical looping gasification (CLG) of biomass, but their chemical reactivity is very ordinary. Converter steel slags (CSSs) are steelmaking wastes and rich in Fe2O3, CaO and MgO, which have good oxidative ability and good stability as well as catalytic effects on biomass gasification. Therefore, the composite OCs prepared by mechanically mixing CSSs with iron-based OCs are expected to be used to increase the hydrogen production in the CLG of biomass. In this study, the catalytic performance of CSS/Fe2O3 composite OCs prepared by mechanically mixing CSSs with iron-based OCs on the gasification of brewers’ spent grains (BSGs) were investigated in a tubular furnace experimental apparatus. The results showed that when the weight ratio of the CSSs in composite OCs was 0.5, the relative volume fraction of hydrogen reached the maximum value of 49.1%, the product gas yield was 0.85 Nm3/kg and the gasification efficiency was 64.05%. It could be found by X-ray diffraction patterns and scanning electron microscope characterizations that the addition of CSSs helped to form MgFe2O4, which are efficient catalysts for H2 production. Owing to the large and widely distributed surface pores of CSSs, mixing them with iron-based OCs was beneficial for catalytic steam reforming to produce hydrogen.

Iron-based oxygen carriers (OCs) have received much attention due to their low costs, high mechanical strengths and high-temperature stabilities in the chemical looping gasification (CLG) of biomass, but their chemical reactivity is very ordinary. Converter steel slags (CSSs) are steelmaking wastes and rich in Fe2O3, CaO and MgO, which have good oxidative ability and good stability as well as catalytic effects on biomass gasification. Therefore, the composite OCs prepared by mechanically mixing CSSs with iron-based OCs are expected to be used to increase the hydrogen production in the CLG of biomass. In this study, the catalytic performance of CSS/Fe2O3 composite OCs prepared by mechanically mixing CSSs with iron-based OCs on the gasification of brewers’ spent grains (BSGs) were investigated in a tubular furnace experimental apparatus. The results showed that when the weight ratio of the CSSs in composite OCs was 0.5, the relative volume fraction of hydrogen reached the maximum value of 49.1%, the product gas yield was 0.85 Nm3/kg and the gasification efficiency was 64.05%. It could be found by X-ray diffraction patterns and scanning electron microscope characterizations that the addition of CSSs helped to form MgFe2O4, which are efficient catalysts for H2 production. Owing to the large and widely distributed surface pores of CSSs, mixing them with iron-based OCs was beneficial for catalytic steam reforming to produce hydrogen.

Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NOx emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m3 and RMSE of 3.35 mg/m3. Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NOx emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.

Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NOx emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m3 and RMSE of 3.35 mg/m3. Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NOx emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.

Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for the online monitoring of batteries. This paper proposes a lithium-ion battery degradation research method based on the CNN-Transformer model. By leveraging the efficiency of the CNN model in feature extraction, it reduces the dependency of the Transformer model on data volume, thereby ensuring faster overall model training without a significant loss in model accuracy. This facilitates the online monitoring of battery degradation. The dataset used for training and validation consists of charge–discharge data from 124 lithium iron phosphate batteries. The experimental results include an analysis of the model training results for both single-battery and multiple-battery data, compared with commonly used models such as LSTM and Transformer. Regarding the instability of single-battery data in the CNN-Transformer model, statistical analysis is conducted to analyze the experimental results. The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values.