Complex Pipeline Research Articles

A Neural Network-Based State-Constrained Control Strategy for Underactuated Aerial Transportation Systems Within Narrow Workspace

The aerial transportation system belongs to a symmetrical system and has recently garnered increasing attention from researchers due to its broad application range and convenient operation. The control difficulty of the aerial transportation system lies in the fact that the load is not directly actuated, posing a significant challenge for state-constrained control. Taking the motion of an unmanned aerial vehicle (UAV) suspension transportation system within complex pipelines as an example, this paper employs the the swept volume signed distance field (SVSDF) method to search for state boundaries, which is an aspect not considered or elaborated in many state-constrained control approaches. Furthermore, adaptive state-constrained control based on the radial basis function (RBF) neural network is utilized for the case of experiencing unknown air resistance. The convergence of the proposed method for underactuated and actuated state variables is theoretically demonstrated based on the Lyapunov technique. Compared with existing methods, the error integral index demonstrates that the proposed method displays better convergence capability in the simulation section when considering state constraints under disturbance and air resistance.

Precision and uncertainty in natural gas calorific value estimation: advanced combinatorial predictive models for complex pipeline systems

Abstract In recent years, natural gas pipelines have been characterized by multiple intake points, the mixing of different gas sources, and significant variations in gas quality. The existing volumetric measurement system is no longer suitable for the development and operation of natural gas pipelines in China. To ensure accurate and equitable implementation of natural gas measurement, there is a gradual shift towards energy measurement. However, due to numerous measurement interfaces, installing chromatographs at all measurement stations would lead to substantial investment costs. Therefore, predicting the calorific value of natural gas is a key technology for energy measurement. In view of the issues existing in the current methods of obtaining natural gas calorific value, different prediction models of natural gas calorific value are constructed. According to the influencing factors of natural gas calorific value, the uncertainty evaluation is carried out, and the accuracy of different calorific value prediction models is analyzed by case data. The maximum error of regression equation (RE), response surface analysis (RSA), BP neural network and genetic algorithm (GA) prediction model is 2.29%, 0.43%, 0.59% and 0.45% respectively. However, the combined calorific value (CCV)prediction model is 0.41%. The results indicate that the predicted value calculated by the CCV prediction model is closer to the actual value and has higher prediction performance, which provides a new method for the calorific value prediction and measurement management of natural gas.

Performance and energy consumption study of a dual-evaporator loop heat pipe for chip-level cooling

The dual-evaporator loop heat pipe (DeLHP) exhibits more applications than a single-evaporator loop heat pipe in a chip-level cooling field. However, increasing the evaporators leads to a complex pipeline structure. This study focuses on developing and investigating a new parallel structure for the DeLHP. Experimental research was conducted to analyze the startup characteristics of DeLHP under single-load and dual-load conditions, and its operational characteristics under various variable power conditions. Numerical simulation was employed to analyze the fluid distribution and flow characteristics. The results indicate that heating the evaporator near the vapor pipeline achieves faster startup and more stable temperatures under a single load. Under dual loads, DeLHP exhibits a faster startup compared to a single load. When the thermal load near the vapor pipeline is greater than the load on the evaporator near the liquid pipeline, DeLHP starts faster and maintains a lower stable temperature. Under a maximum total load of 300 W, the heating surface temperature stabilizes below 80 ℃. The numerical simulation results indicate that when evaporator 1 near the liquid pipeline is individually heated, the temperatures, vapor fractions, and fluid velocities of the two evaporators are more balanced. The power usage effectiveness reaches a minimum value of 1.09 at 150 W. These research findings provide reliable and substantive support for the performance optimization of DeLHPs in practical applications.

Soft modular pipe robot inspired by earthworm for adaptive pipeline internal structure

Abstract The inspection, maintenance, and repair of complex pipelines have motivated the development of soft robots with highly flexible and good adaptability. In this study, inspired by the unique locomotion of earthworms, we developed a type of smart material–driven soft modular pipe robot capable of stable manipulation and performing in unstructured pipe environments, which easily assembles into more complex configurations with multiple modules for practical use. Our prototype robot consists of three soft telescopic modules connected in series with flexible bellows and a tail friction mechanism, where the modules adopt a high-energy density shape memory alloy spring as an actuator. Based on analyzing the peristaltic process of the module inside the pipe, it is ensured that the geometric constraint performance of the braided mesh pipe is reasonably matched with the thermomechanical performance of the SMA spring to realize the alternating conversion of anchoring and releasing. By optimizing the overall robotic structure, it is demonstrated that our robot achieves robust crawling in horizontal, vertical, variable-diameter, and curved pipes, wet pipes with the partial presence of water, and pipes with complex cavities through simple open-loop on/off control.

Extraction of complex pipeline features from incomplete point clouds

Abstract Control point detection in industrial pipelines, characterized by flanges
and multiple passes, is critical for accurate virtual simulations and quality assessments
in manufacturing. This paper introduces an innovative method for detecting control
points in complex pipelines using incomplete point clouds, significantly streamlining
the process. Our approach uniquely requires only straight sections and end-plane
localizations as inputs, markedly reducing both data acquisition and processing times.
We develop a robust feature descriptor to align the CAD model with incomplete
point clouds, facilitating semantic automatic segmentation despite the lack of explicit
semantic information. Following this, geometric primitives are fitted to the segmented
clouds, and a cylindrical fitting algorithm tailored for incomplete data is introduced.
The control points are computed based on the relative positions and geometric
parameters of these primitives. Our method has been validated through experiments
on several real-world industrial complex pipelines. The results confirm that our
approach achieves a high measurement accuracy of 0.067 mm, even with point cloud
incompleteness of up to 50%. These findings highlight the effectiveness of our method in
accurately determining the geometric parameters of complex pipelines and suggesting its
considerable potential for practical applications.

Research on predicting air resistance in complex pipelines with fans

Abstract This article proposes a prediction method for air resistance in aircraft brake chambers by analyzing the theoretical resistance along the pipeline and local resistance. It develops an air resistance calculation software based on this method. Finally, the air resistance of aircraft brake chamber pipes with three different specifications of fans was predicted using the methods and software presented in this article. The comparison between the predicted results and the simulation results shows that the method is accurate and reliable, and can be used to predict the air resistance inside the aircraft brake chamber.

Thanos: An R Package for the Gene-Centric Analysis of Functional Potential in Metagenomic Samples.

As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.

Transformable Inspection Robot Design and Implementation for Complex Pipeline Environment

Transformable Inspection Robot Design and Implementation for Complex Pipeline Environment

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

This paper presents a maintenance model for optimal planning of pipeline network inspections subjected to corrosion and residual stress. The proposed model includes predictive degradation modeling, spatio-temporal reliability, and expected cost minimization. The pipeline networks are formed by linear segments and irregular zones such as elbows, weld joints, and flanges for putting pipes together and changing the direction of gas or liquid flow. The mean of Karhunen-Loève expansion is used to model space-variant corrosion, residual stress, and stochastic dependency in different exposure zones. Monte Carlo simulations are employed to compute the failure probability of individual components. Subsequently, a Bayesian network evaluates the failure probability of the complex pipeline network and identifies the most critical components. Depending on the objective or market demand uncertainties, a cost model is also proposed to optimize the inspection policy. The methodology is applied to a case study, and the results show that it can provide valuable insights to decision-makers to enhance complex systems' safety and reliability.

Modeling and simulation of xenon storage and supply system for electric propulsion system based on fundamental equation of state

The electric propulsion system (EPS) xenon storage and supply system (SSS) is a complex pipeline network system, which involves the multi-physics coupled fields of transcritical flow, heat transfer, rigid body motion, electromagnetics, and control. Based on the fundamental equation of state, a one-dimensional finite volume model system is proposed for compressible transcritical transient pipe flow. Six typical components of the SSS are modularization modeled by using an object-oriented programming method in Fortran 2018. To evaluate the performance of the algorithm, a simulation model consisting of 77 components is developed for a proportional controlled EPS xenon SSS, and a system-level simulation is performed. Compared with the standard case constructed on the Amesim platform, the model has a pressure rise time error lower than 6%, and the restrictors mass-flow-rate errors of the anode, cathode, and neutralizer are less than 2.8% of the rated flow rate after stabilization. Moreover, the proposed model yields more accurate results at certain operating points. The model system provides an efficient reference for the design and optimization of EPS SSSs, and offers a new approach to simulate complex cryogenic pipe flow systems.

Numerical Simulation of Shock Wave Propagation Law of Coal Dust Explosion in Complex Pipeline Networks.

In order to study the shock wave propagation law of coal dust explosion in complex structures, the diagonal pipe network model was established by using computational fluid dynamics software, and the numerical simulation of the coal dust explosion in the diagonal branch was carried out. The change law of the shock wave and its attenuation at the corner were analyzed. The results show that the pressure change process of coal dust explosion can be divided into three stages, namely, the stage where the pressure value at the ignition point is relatively high, the stage where the pressure value on both sides of the ignition source is maximum, and the stage where the pressure value at the shock wavefront is relatively maximum; the overpressure of the shock wave decays at the bifurcation point, and the larger the bifurcation angle, the greater the pressure attenuation amplitude. For the same bifurcation angle, the greater the initial pressure at the monitoring point, the greater its attenuation amplitude. Multiple regression fitting on the shock wave pressure values was performed before and after turning, and the function relationship between the shock wave pressure attenuation coefficient and the initial pressure, as well as different turning angles, was obtained. The fitting results can be used as a formula for calculating the ventilation network during the disaster process, which has theoretical significance for evaluating the consequences of coal dust explosion accidents.

Investigations of the Formation Mechanism and Pressure Pulsation Characteristics of Pipeline Gas-Liquid Slug Flows

The pipeline system is widely used in marine engineering, and the formation mechanism and flow patterns of two-phase slug flows are of great significance for the optimal design of and vibration prevention in a complex pipeline system. Aiming at the above problems, this paper proposes a modeling and solving method for gas-liquid slug flows. First, a VOF-PLIC-based coupling gas-liquid slug flow transport model is conducted. Second, to reduce the fuzzy boundary between the gas-liquid coupling interfaces, an artificial compression term is added to the transport equations, and the formation and evolution mechanism of severe slugging flow in piping systems is investigated. The pressure pulsation and gas content characteristics of the gas-liquid coupling process are explored. Research results found that the slugging phenomenon occurs at the gas-liquid interface, where liquid slugging frequency reaches its peak. The pipeline system has prominent periodic characteristics of the slugging phenomenon, and the period decreases when the gas-phase converted speed rises; pressure fluctuation amplitude increases, and the gas-phase velocity change is the inducing factor for the drastic change of pressure fluctuation. The research results can offer theoretical references for optimal designs of and vibration prevention in marine pipeline systems.

CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning

Neural Radiance Fields have demonstrated impressive performance in novel view synthesis. However, NeRF and most of its variants still rely on traditional complex pipelines to provide extrinsic and intrinsic camera parameters, such as COLMAP. Recent works, like NeRFmm, BARF, and L2G-NeRF, directly treat camera parameters as learnable and estimate them through differential volume rendering. However, these methods work for forward-looking scenes with slight motions and fail to tackle the rotation scenario in practice. To overcome this limitation, we propose a novel camera parameter free neural radiance field (CF-NeRF), which incrementally reconstructs 3D representations and recovers the camera parameters inspired by incremental structure from motion. Given a sequence of images, CF-NeRF estimates camera parameters of images one by one and reconstructs the scene through initialization, implicit localization, and implicit optimization. To evaluate our method, we use a challenging real-world dataset, NeRFBuster, which provides 12 scenes under complex trajectories. Results demonstrate that CF-NeRF is robust to rotation and achieves state-of-the-art results without providing prior information and constraints.

DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding

Point scene understanding is a challenging task to process real-world scene point cloud, which aims at segmenting each object, estimating its pose, and reconstructing its mesh simultaneously. Recent state-of-the-art method first segments each object and then processes them independently with multiple stages for the different sub-tasks. This leads to a complex pipeline to optimize and makes it hard to leverage the relationship constraints between multiple objects. In this work, we propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation to facilitate learning with multiple objects for the multiple sub-tasks in a unified manner. Each object is represented as a query, and a Transformer decoder is adapted to iteratively optimize all the queries involving their relationship. In particular, we introduce a semantic-geometry disentangled query (SGDQ) design that enables the query features to attend separately to semantic information and geometric information relevant to the corresponding sub-tasks. A hybrid bipartite matching module is employed to well use the supervisions from all the sub-tasks during training. Qualitative and quantitative experimental results demonstrate that our method achieves state-of-the-art performance on the challenging ScanNet dataset. Code is available at https://github.com/SAITPublic/DOCTR.

Vibration Control of Reinjection Pump Room Pipeline System Based on Dynamic Characteristic Analysis

The reinjection pump room plays a crucial role in industries such as petroleum and chemical engineering. Its main responsibility is to safely and effectively inject treated wastewater, exhaust gas, or other fluid substances into the formation, aiming to achieve resource recycling and environmental protection. However, the pipeline system of the reinjection pump room usually has complex spatial distribution, long span, and multi-point elastic support characteristics, which may cause structural vibration during operation, and have adverse effects on the stable operation, safety, and surrounding environment of the equipment. Therefore, in the pipeline design stage, it is crucial to conduct in-depth vibration analysis of complex pipeline systems to ensure that harmful resonance phenomena do not occur in the pipeline under working conditions. Given the potential impact of pipeline system vibration on equipment performance and surrounding environment, effective suppression of pipeline system structural vibration is of great practical significance. This article focuses on the vibration control strategy of the reinjection pump room pipeline system based on dynamic characteristic analysis. By conducting in-depth research on the dynamic characteristics of pipeline systems, including vibration frequency, modal analysis, and vibration transmission paths, the aim is to propose a set of targeted vibration control measures.

Efficient Malware Analysis Using Metric Embeddings

Real-world malware analysis consists of a complex pipeline of classifiers and data analysis—from detection to classification of capabilities to retrieval of unique training samples from user systems. In this article, we aim to reduce the complexity of these pipelines through the use of low-dimensional metric embeddings of Windows PE files, which can be used in a variety of downstream applications, including malware detection, family classification, and malware attribute tagging. Specifically, we enrich labeling of malicious and benign PE files with computationally-expensive, disassembly-based malicious capabilities information. Using this enhanced labeling, we derive several different types of efficient metric embeddings utilizing an embedding neural network trained via contrastive loss, Spearman rank correlation, and combinations thereof. Our evaluation examines performance on a variety of transfer tasks performed on the EMBER and SOREL datasets, demonstrating that low-dimensional, computationally-efficient metric embeddings maintain performance with little decay. This offers the potential to quickly retrain for a variety of transfer tasks at significantly reduced overhead and complexity. We conclude with an examination of practical considerations for the use of our proposed embedding approach, such as robustness to adversarial evasion and introduction of task-specific auxiliary objectives to improve performance on mission critical tasks.

Wireless flow-powered miniature robot capable of traversing tubular structures.

Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external powering constrains these robots' operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent on different proposed control designs. In addition, we designed kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance.

Research on vibration characteristics of rocket engine flow pipeline

PurposeAbnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.Design/methodology/approachIn this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.FindingsIn this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.Practical implicationsThe results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.Originality/valueThe causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

Nuclear power plant pipeline detection robot based on a new radiation-proof material

Soft robots are gradually gaining recognition in recent years for their ability to work in extreme environments. However, radiation shielding is a major issue when designing soft robots for use in nuclear environments. In this paper we present an effective soft robot design and develop new radiation shielding materials that enable the robot to perform a variety of tasks in the complex pipelines of a nuclear power plant while being sufficiently radiation shielded. To achieve this, we used a mixture of silicone and lead powder with excellent radiation shielding capabilities and designed and built a soft robot prototype consisting of multiple pneumatic actuators and a resin frame. It is capable of moving through complex pipelines and performing inspection tasks, while also providing radiation shielding for the internal components of the robot. Finite element simulations and prototype experiments were then carried out to evaluate the robot's performance in various scenarios. Simulation results show that the soft robot is able to perform inspection of nuclear power plant pipelines efficiently and effectively. Furthermore, the radiation shielding provided by the radiation-proof material is effective in protecting the internal components of the robot from radiation exposure. In conclusion, we have successfully developed a soft robot prototype that is capable of performing pipeline inspections in extreme environments such as nuclear power plants, while providing adequate radiation shielding. The proposed approach can be extended to other soft robotics applications in the nuclear environment and has the potential to improve the safety and efficiency of nuclear power plant operations.

Analysis of gas-solid flow characteristics in intricate pipelines

Pneumatically conveying powder particles is predominantly employed in pipelines. Transportation variables significantly alter the behavior of powder particles within complex pipelines. To investigate the gas-solid two-phase flow characteristics of powder particles in dense phase within complex pipelines, Computational fluid dynamics and Discrete element model (CFD-DEM) were applied to the multi-opening pipelines for numerical simulation. The study examines the impact of gas velocity, particle mass flow rate, and particle size on the flow properties of solid-phase particles. It reveals that the particle velocity does not notably elevate as conveying gas velocity increases in the dense phase transportation of complex pipelines; Particle transport continuity is degraded at low mass flow rates, whereas higher filling rates are observed at high mass flow rates; With increasing particle size, particle inertia force escalates, leading to decreased particle velocity and conveying efficiency. The CFD-DEM method enables accurate portrayal of particle flow properties, offering a valuable reference for examining pneumatic conveying in complex pipelines.