Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Wei Li, Yapeng Liu, Xiang Li, Bowen Deng + 4 more
Optimizing drilling parameters is essential for improving drilling efficiency and reducing operational costs in oil and gas engineering. This study presents an intelligent optimization approach for drilling parameters based on a hydraulic-mechanical specific energy (MSE) model. A time-series data fusion framework…
Linsheng Liu, Yushu Lai, Yiwei Zhang, Lin Huang + 7 more
Carbon fiber reinforced polymer (CFRP) is prone to delamination damage during drilling, which seriously affects the processing quality. This study focuses on the use of variable parameter drilling technology. Firstly, an anisotropic constitutive model and a Hashin failure model for CFRP were constructed. Then, based on…
Mustafa Günay, Mehmet Boy, Mehmet Erdi Korkmaz, Yizhuo Gu
Carbon fiber-reinforced thermoplastic composite drilling is a secondary manufacturing process because the quality of drilled holes affects assembly system performance, structure, and sustainability. This paper compares all drill coating types and cutting conditions for PEEK-CF30 composite drilling utilizing a hybrid…
Yonggang Deng, Xiaojing Zhou, Zixuan Feng, Xin Li + 1 more
Introduction Accurately predicting the rate of penetration (ROP) is a critical benchmark for evaluating operational efficiency in drilling operations, and it is necessary to optimize the drilling parameters and construct an accurate ROP prediction model. At present, the correlations between drilling operation…
Maolin Lei, Edoardo Romiti, Arturo Laurenzi, Rui Dai + 4 more
Modular manipulators, composed of pre-manufactured and interchangeable base modules, provide high adaptability across diverse task. However, deploying such systems requires generating feasible motions while simultaneously optimizing the manipulator's morphology and mounted pose under kinematic, dynamic, and physical…
An Ping, Yibin Zha, Yichen Li, Yiwen Jiang + 5 more
Carbon fiber-reinforced polymer (CFRP) is extensively applied in aerospace and rail transportation industries, and the quality of CFRP joint holes is crucial for ensuring joint performance and service reliability. However, CFRP drilling involves complex interactions among drilling parameters, tool geometry, and…
Térence Essomba, Yu-Wen Wu, Abdelbadia Chaker, Med Amine Laribi
In some surgical operations targeting the spine, it is required to drill cavities in the vertebrae for the insertion of pedicle screws. A new mechanical architecture is proposed for this application. It is based on an augmented version of the full translational R-CUBE mechanism, with improved linkages to implement…
Zebing Wu, Lianghui Song, Jun Xu, Jian Chen + 5 more
Highlights What are the main findings?1. A WOA-optimized physics-informed CNN-BiGRU model was developed for DWOB prediction. 2. Drill-string frictional attenuation was incorporated into the loss function. What are the implications of the main findings?1. DWOB can be estimated from surface sensor data without direct…
Seshu Kumar Damarla, Xiuli Zhu
—Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate prediction of fluid loss is therefore essential for improving drilling safety and efficiency. This study presents a probabilistic machine…
Thomas Berger, Thavamani Govindaraj, Pushya Mitra, Timo Reis
We study the output tracking problem for a vertically driven drill string system described by a nonlinear boundary-coupled PDE-ODE model. Solvability analysis of the drill string model is achieved by first casting the model in an abstract boundary value problem involving set-valued operators on an appropriate Hilbert…
Aleksander Berezowski, Hassan Hassanzadeh, Gouri Ginde
Oil and gas drilling operations generate extensive time-series data from surface sensors, yet accurate real-time prediction of critical downhole metrics remains challenging due to the scarcity of labelled downhole measurements. This systematic mapping study reviews thirteen papers published between 2015 and 2025 to…
Mehdi Heydari Shahna, Tuomo Kivelä, Jouni Mattila
Long-reach drilling booms must reach successive poses without self-collision. Moving from operator-supervised control toward autonomy requires collision-aware motion planning and execution. For the Sandvik SB60, this study adapts established methods by integrating pose-parameterized planning with a capsule-based…
Hana Yahia, Bruno Figliuzzi, Florent Di Meglio, Laurent Gerbaud + 2 more
This paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at the drill bit. The study aims to develop a robust…
Kumar, Shubham, Sahoo, Anshuman
Precise wellbore trajectory prediction is a crucial task in subsurface engineering, dictated by nonlinear interactions among the drilling assembly and inhomogeneous geology. In this project, we devise a systematic, mathematically well-posed framework for trajectory prediction that transitions beyond empirical modeling…
Anja Adamov, Christian L. Müller, Nicholas A. Bokulich
Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured. Predictive modelling from these data typically relies on ad hoc choices of feature representation, obscuring their impact on performance and biological interpretation. A standardized, compute-efficient framework…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Authors not listed
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Authors not listed
Azo dyes constitute one of the largest and most commercially important classes of synthetic colorants, widely applied in textiles, plastics, inks, and food. However, their manufacture through traditional batch processes is often constrained by safe-ty risks, poor heat and mass transfer, and inconsistent product…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…