Shapley Additive Explanations of Multi-Geometrical Variable Coupling Effect in Transonic Compressor

2021 ◽  
Author(s):  
Junying Wang ◽  
Xiao He ◽  
Baotong Wang ◽  
Xinqian Zheng
Keyword(s):  
Neural Network ◽  
Coupling Effect ◽  
Design Guidelines ◽  
Design Stage ◽  
Coupling Effects ◽  
Sweep Angle ◽  
Transonic Compressor ◽  
The Neural Network ◽  
Lean Angle ◽  
Flow Mechanisms

Abstract Optimization algorithms in the compressor detailed design stage generate big data of geometries and corresponding performances, but these data are often not exploited efficiently to unveil hidden compressor design guidance. In this work, the Shapley Additive Explanations (SHAP) method from game theory is proposed as an efficient methodology to extract design guidelines from databases. A database was generated when optimizing the blade features (sweep, lean, end-bend) of Rotor 37. Based on this, a neural network is trained to predict compressor efficiency. The SHAP method is then applied to explain the neural network behavior, which provides information on the sensitivity of single geometrical variables and the coupling effect between multiple geometrical variables. Results show that the near-tip sweep and mid-span lean angles are most influential on efficiency. Within the same group of variables, the adjacent variables tend to present strong positive coupling effects on efficiency. Among different groups, evident coupling effects are observed between sweep and lean and between lean and end-bend, but the coupling effect between sweep and end-bend is negligible. Flow mechanisms behind the coupling effects are discussed. For near-tip lean angles L3 and L4, the positive coupling effect is due to the change of the passage shock. For near-tip lean angle L4 and sweep angle S4, the change of detached shock leads to a negative coupling effect. The proposed data mining method based on the neural network and SHAP is promising and transferable to other turbomachinery optimization databases in the future.

scholarly journals Application of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector

2021 ◽  
Vol 19 (3) ◽  
pp. 55-64
Author(s):  
K. N. Maiorov ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Machine Learning Algorithms ◽  
Design Stage ◽  
Learning Methods ◽  
Field Development ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
The Neural Network

The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.

Prediction of Surface Ship's Residual Resistance Coefficient Using Neural Networks

2013 ◽  
Vol 756-759 ◽  
pp. 3141-3144
Author(s):  
Li Hua Song ◽  
Zhi Guo Zhang ◽  
Xian Zhou Wang ◽  
Da Kui Feng
Keyword(s):  
Neural Network ◽  
Froude Number ◽  
Resistance Coefficient ◽  
Design Stage ◽  
Accurate Estimation ◽  
Target Variable ◽  
Residual Resistance ◽  
The Neural Network ◽  
The Relationship

The Holtrop method, which provides a prediction of the components of surface ships total resistance, is widely used at ships initial design stage for estimating the resistance. In this paper a neural network model which performs the same role as the Holtrop method is presented to predict the residual resistance. A multilayer perceptron has been trained with the data generated by the Holtrop method to learn the relationship between the input (length-displacement ratio, prismatic coefficient, breadth-draft ratio and Froude number) and the target variable (the residual resistance coefficient). The results of this model have been compared against those provided by the Holtrop method and it is found that the quality of the prediction is improved over the entire range of data. The neural network provides an accurate estimation of the residual resistance with the Froude number and the hull geometry coefficients as variables.

Neural Network-Based Fuzzy Reasoning for Conceptual Design Evaluation

1999 ◽  
Author(s):  
J. Sun ◽  
D. K. Kalenchuk ◽  
D. Xue ◽  
P. Gu
Keyword(s):  
Neural Network ◽  
Conceptual Design ◽  
Fuzzy Reasoning ◽  
Design Stage ◽  
Design Evaluation ◽  
Design Quality ◽  
Customer Needs ◽  
Improve Design ◽  
Design Specifications ◽  
The Neural Network

Abstract This paper presents a neural network-based fuzzy reasoning method for design candidate evaluation and identification to improve design quality and efficiency at the crucial conceptual design stage. The evaluation and identification of design candidates are carried out through the following four steps: (1) acquisition of customer needs and ranking of their importance, (2) establishment of measurable metrics and their relations with customer needs, (3) development of design specifications and initial evaluation of design candidates, and (4) evaluation and identification of design candidates based on design specifications and customer needs. A case study is given to show the effectiveness of the neural network-based fuzzy reasoning method in conceptual design evaluation.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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