....2019.116076 [13] Liu , Z. , and Liu , J. , 2022 , “ Machine Learning Assisted Analysis of an Ammonia Engine Performance ,” ASME J. Energy Resour. Technol. , 144 ( 11 ), p. 112307 . 10.1115/1.4054287/1140078 [14] Atasoy , V. E. , Suzer , A. E. , and Ekici , S. , 2022...

...-means++ algorithm and Gaussian mixture gradient algorithm unsupervised machine learning algorithms are used to determine the classification standard of general reservoirs and high-quality sweet spot reservoirs in the lower 1 layer of He-8 in the x block of the Sulige gas field. This application...

... the above experimentally observed non-monotonous viscosity behavior, various machine learning models were employed; support vector regression (SVR)based models predicted the slickwater fluid viscosity with maximum accuracy. Sensitivity analysis was carried out to determine the prominence of the studied...

.... The available experimental results of such a modified engine including noise and the test conditions were randomly distributed without careful design. As a result, the machine learning model was utilized to assist in analyzing the ammonia engine performance by reducing the experimental uncertainty. The results...

... parameters in an FPSO with CCUS (CO 2 capture, utilization, and storage). Twenty-seven thermodynamic and structural design variables are selected as input parameters for the sensitivity analyses. Four machine learning-based screening analysis algorithms such as smooth spline-analysis of variance (SS-ANOVA...

... reduction in the aerospace, automotive, and construction industries. This integrated modeling approach has not been fully applied to nuclear safeguards programs in the past. Digital twinning, combined with machine learning technologies, can lead to new innovations in process-monitoring detection...

... data. Two different machine learning tools (FN and SVM) were employed to introduce the predictive models using the logging data (GR, RHOB, NPHI, DTC, and DTS) as inputs to predict formation resistivity. The study results can be summarized as follows: The FN-based model outperformed the SVM-based...

... and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only...

... machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, a big portion of ML development projects was actually spent on improving...

... to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict S w from...

...Jinlong Liu; Qiao Huang; Christopher Ulishney; Cosmin E. Dumitrescu Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods toward predicting the performance of a diesel engine modified...

.... Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD...

... to collect a large amount of data for data-driven methods development and testing. The main study in this article is to develop machine learning algorithms for identifying abnormal drilling and test these algorithms on the rig based on the responses of the rig sensors in real-time operations. The idea also...

...Umang H. Rathod; Vinayak Kulkarni; Ujjwal K. Saha This article addresses the application of artificial neural network (ANN) and genetic expression programming (GEP), the popular artificial intelligence, and machine learning methods to estimate the Savonius wind rotor’s performance based...

... and require many input parameters. In this study, an improved natural gas density prediction model is presented using robust machine learning techniques such as artificial neural networks and functional networks. A total of 3800 data points were collected from different published sources covering a wide range...

... that the drilling data such as drill pipe torque, weight on bit, and rate of penetration are available at an early stage without additional cost. Three machine learning algorithms were used to correlate the drilling data with Young's modulus: random forest, adaptive neuro-fuzzy inference system, and functional...

...Hany Osman; Abdulwahab Ali; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny Predicting the rate of penetration (ROP) is challenging especially during horizontal drilling. This is because there are many factors affecting ROP. Machine learning techniques are very promising in identifying...

... the combined use of two machine learning (ML) technique, viz., functional network (FN) coupled with particle swarm optimization (PSO) in predicting the black oil PVT properties such as bubble point pressure (P b ), oil formation volume factor at Pb, and oil viscosity at Pb. This study also proposes new...

...Opeoluwa Owoyele; Pinaki Pal; Alvaro Vidal Torreira The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational...

.... In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model...