Traditional leakage prediction models for long-distance pipelines have limitations in effectively synchronizing spatial and temporal features of leakage signals, leading to data processing that heavily relies on manual experience and exhibits insufficient generalization capabilities. This paper introduces a novel leakage detection and localization algorithm for oil and gas pipelines, integrating wavelet denoising with a Long Short-Term Memory (LSTM)-Transformer model. The proposed algorithm utilizes pressure sensors to collect real-time pipeline pressure data and applies wavelet denoising to eliminate noise from the pressure signals. By combining LSTM’s temporal feature extraction with the Transformer’s self-attention mechanism, we construct a short-term average pressure gradient-average instantaneous flow network model. This model continuously predicts pipeline flow based on real-time pressure gradient inputs, monitors deviations between actual and predicted flow, and employs a pressure curve distance algorithm to accurately determine the leakage location. Experimental results from the Jilin-Changchun long-distance oil pipeline demonstrate that the model possesses superior leakage warning and localization capabilities. Specifically, the leakage prediction accuracy reaches 99.995%, with a leakage location error margin below 2.5%. Additionally, the model can detect leaks exceeding 0.6% of the main pipeline flow without generating false alarms during operation.

The mechanical stability of carbon aerogels, particularly their thermal insulation performance, is closely linked to their elastic modulus. This property plays a crucial role in determining the material’s overall mechanical stability. The objective of this study is to enhance the accuracy of elastic modulus predictions for carbon aerogels using a power-scaling model. By setting the prefactor of the Gibson and Ashby equation to 1.0, accurate predictions of the elastic modulus can be achieved if the correct scaling exponent is determined. Twelve sets of experimental data were used to fit the power-scaling model, revealing that the scaling exponent for the elastic modulus of carbon aerogels typically falls between 2.2 and 3.0. This range is narrower than the 2.0 to 4.0 range reported in the literature, with a median value of 2.6 providing reliable predictions. Additionally, a relationship between the solid thermal conductivity and the elastic modulus of carbon aerogels was established using a thermal conduction model. The study also examined the elastic modulus of carbon nanotube and graphene aerogels—both allotropes of carbon aerogel. By fitting experimental data into the power-scaling model, the scaling exponents for carbon nanotube aerogels and graphene aerogels were found to range from 2.7 to 3.5 and 2.7 to 3.7, respectively. Median exponent values of 3.1 and 3.2 were identified as optimal for predicting the elastic moduli of carbon nanotube and graphene aerogels.

Alumina aerogels are a class of nanoporous materials featuring tailorable three-dimensional pore architectures with porosities exceeding 90%, exhibiting promising prospects for energy-efficient thermal management applications. However, quantitative models linking pore structure to macroscopic thermo-mechanical performance remain scarce, hindering their rational design and performance optimization. The thermal conductivity of alumina aerogel was experimentally studied based on the steady-state heat conduction method, and the elastic modulus of the material was also measured based on the thermal mechanical coupling effect method. Combining with the literature data, the engineering calculation models were proposed to predict the thermal conductivity and elastic modulus as well as their upper and lower limits, which can help to provide guidance for the rational design of porous architectures. Furthermore, these models were extended to two representative porous composite materials, namely alumina–silica aerogels and fiber-reinforced alumina aerogels. The effective thermal conductivity and elastic modulus of the two composites were estimated by the Geometric Mean Model and the Gibson and Ashby scaling formula, respectively. Literature data were employed to validate the present predictions. The effect of the molar ratio of Al:Si on the thermal conductivity and elastic modulus of the alumina–silica aerogel was analyzed: the thermal conductivity can be reduced by about 13.1–25.5% as the Al:Si decreases from 8:1 to 2:1 for densities in the range of 50–250 kg/m³, while the reduction of the elastic modulus does not exceed 7% as the Al:Si decreases from 8:1 to 3:1. The effect of the glass fiber content on the fiber-reinforced alumina aerogel was also discussed: the elastic modulus can be reinforced by about 1.61–119.2% as the volume fraction of the glass fiber increases from 0.1 to 1.4% in the density range of 100–200 kg/m³, while the thermal conductivity is only increased by about 0.18–4.7%. These predictive tools provide a quantitative framework for achieving the optimization of structure–property relationships in advanced porous thermal insulation materials.