scholarly journals Applying machine learning to study fluid mechanics

2022 ◽  
Author(s):  
Steven L. Brunton
Keyword(s):  
Machine Learning ◽  
Fluid Mechanics ◽  
Loss Function ◽  
Optimization Algorithm ◽  
Training Data ◽  
Data Driven ◽  
Physical Knowledge ◽  
Short Overview

Abstract This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics. Graphic abstract

scholarly journals Energy Consumption Prediction for 3-RRR PPM through Combining LSTM Neural Network with Whale Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yin Gao ◽  
Ke Chen ◽  
Hong Gao ◽  
Hongmei Zheng ◽  
Lei Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Loss Function ◽  
Optimization Algorithm ◽  
Movement Time ◽  
Training Data ◽  
Whale Optimization ◽  
Moving Platform ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

In the process of minimizing the energy consumption of a 3-RRR planar parallel manipulator (3-RRR PPM) and even general parallel kinematic manipulators, obtaining optimal results usually depends on particular functional relation between the instantaneous position of the moving platform and the kinetic time, which is called a displacement model (DM). Nevertheless, it is likely that although the movement time and path of a moving platform are the same, different amounts of energy are consumed for different DMs of the moving platform. To address this, a method of using long short-term memory neural network (LSTM-NN) instead of a complex theoretical model to predict the energy consumption of a 3-RRR PPM was presented. Subsequently, inverse dynamic equations of 3-RRR PPM were established based on the Newton–Euler method and solved using QR decomposition. Meanwhile, energy consumption between any two points in workspace of the 3-RRR PPM was programmed to provide the LSTM-NN with abundant precise training data. In view of time-varying characteristics of energy consumption prediction, the network architecture was developed based on the principle of LSTM-NN, and root-mean-square error (RMSE) was taken as the loss function. After acquiring training data, the RMSE of the LSTM-NN reached 0.00041 using whale optimization algorithm (WOA) with no need for the gradient of the loss function, so the lack of solving precision in training LSTM-NN was effectively improved. Finally, two different DMs of a moving platform with the same path and movement time were chosen to compare the total energy consumption of the 3-RRR PPM from the simulations, predictions, and experiments. The results showed that the relative error between predicted and experimental data was less than 2.50%. Therefore, the energy consumption prediction based on the LSTM-NN will be useful for achieving the intelligent application of 3-RRR PPMs.

scholarly journals Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

2021 ◽  
Author(s):  
I-Chun Sun ◽  
Renchi Cheng ◽  
Kuo-Shen Chen
Keyword(s):  
Artificial Intelligence ◽  
Performance Index ◽  
Prediction Accuracy ◽  
Training Data ◽  
Data Driven ◽  
Data Types ◽  
Physical Knowledge ◽  
Status Monitoring ◽  
The Status ◽  
Data Driven Approach

Abstract The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.

scholarly journals Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Jung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Performance ◽  
Fault Isolation ◽  
Training Data ◽  
Data Driven ◽  
Design Data ◽  
Industrial Systems ◽  
Free Data ◽  
Model Equations

Localization of unknown faults in industrial systems is a dif- ficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, anomaly classifiers. However, mapping these unknown faults to an actual location in the real system is a non-trivial problem. In model-based diagnosis, physical-based models are used to create residuals that isolate faults by mapping model equations to faulty system components. De- veloping sufficiently accurate physical-based models can be a time-consuming process. Hybrid modeling methods combining physical-based methods and machine learning is one solu- tion to design data-driven residuals for fault isolation. In this work, a set of neural network-based residuals are designed by incorporating physical insights about the system behavior in the residual model structure. The residuals are trained using only fault-free data and a simulation case study shows that they can be used to perform fault isolation and localization of unknown faults in the system.

scholarly journals Optimizing Fractional Compositions To Achieve Extraordinary Properties

2021 ◽  
Author(s):  
Andrew Falkowski ◽  
Steven Kauwe ◽  
Taylor Sparks
Keyword(s):  
Machine Learning ◽  
Loss Function ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Inverse Design ◽  
Data Driven ◽  
Model Parameters ◽  
Chemical Systems ◽  
Target Property ◽  
Successful Candidate

Traditional, data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates that excel in a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach shifts the optimization focus from model parameters to the fractions of each element in a composition. Using a pretrained network, CrabNet, and writing a custom loss function to govern a vector of element fractions, compositions can be optimized such that a predicted property is maximized or minimized. Single and multi-property optimization examples are presented that highlight the capabilities and robustness of this approach to inverse design.

scholarly journals A physics-informed neural network for sound propagation in the atmospheric boundary layer

2021 ◽  
Author(s):  
Chris Pettit ◽  
D. Wilson
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Loss Function ◽  
Sound Speed ◽  
Network Architecture ◽  
Sound Propagation ◽  
Transmission Loss ◽  
Training Data ◽  
Data Driven

We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.

scholarly journals Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

2018 ◽  
Vol 474 (2219) ◽  
pp. 20180335 ◽  
Author(s):  
E. Kaiser ◽  
J. N. Kutz ◽  
S. L. Brunton
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Flight Control ◽  
Linear Models ◽  
Training Data ◽  
Data Driven ◽  
Computationally Efficient ◽  
Hiv Model

Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and may not generalize beyond the attractor where models are trained. These factors limit their use for the online identification of a model in the low-data limit, for example following an abrupt change to the system dynamics. In this work, we extend the recent sparse identification of nonlinear dynamics (SINDY) modelling procedure to include the effects of actuation and demonstrate the ability of these models to enhance the performance of MPC, based on limited, noisy data. SINDY models are parsimonious, identifying the fewest terms in the model needed to explain the data, making them interpretable and generalizable. We show that the resulting SINDY-MPC framework has higher performance, requires significantly less data, and is more computationally efficient and robust to noise than NN models, making it viable for online training and execution in response to rapid system changes. SINDY-MPC also shows improved performance over linear data-driven models, although linear models may provide a stopgap until enough data is available for SINDY. SINDY-MPC is demonstrated on a variety of dynamical systems with different challenges, including the chaotic Lorenz system, a simple model for flight control of an F8 aircraft, and an HIV model incorporating drug treatment.

scholarly journals Short- and long-term predictions of chaotic flows and extreme events: a physics-constrained reservoir computing approach

2021 ◽  
Vol 477 (2253) ◽  
pp. 20210135
Author(s):  
N. A. K. Doan ◽  
W. Polifke ◽  
L. Magri
Keyword(s):  
Machine Learning ◽  
Extreme Events ◽  
Training Data ◽  
Reservoir Computing ◽  
Machine Learning Method ◽  
Learning Method ◽  
Chaotic Flows ◽  
Physical Knowledge ◽  
Velocity Statistics

We propose a physics-constrained machine learning method—based on reservoir computing—to time-accurately predict extreme events and long-term velocity statistics in a model of chaotic flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics from data only, and physical modelling based on conservation laws. This enables the reservoir computing framework to output physical predictions when training data are unavailable. We show that the combination of the two approaches is able to accurately reproduce the velocity statistics, and to predict the occurrence and amplitude of extreme events in a model of self-sustaining process in turbulence. In this flow, the extreme events are abrupt transitions from turbulent to quasi-laminar states, which are deterministic phenomena that cannot be traditionally predicted because of chaos. Furthermore, the physics-constrained machine learning method is shown to be robust with respect to noise. This work opens up new possibilities for synergistically enhancing data-driven methods with physical knowledge for the time-accurate prediction of chaotic flows.

scholarly journals Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

2020 ◽  
Vol 4 (3) ◽  
pp. 92
Author(s):  
André Hürkamp ◽  
Sebastian Gellrich ◽  
Tim Ossowski ◽  
Jan Beuscher ◽  
Sebastian Thiede ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bond Strength ◽  
Composite Structures ◽  
Mechanical Performance ◽  
Training Data ◽  
Data Driven ◽  
Thermoplastic Composites ◽  
Estimation Accuracy ◽  
Limiting Factor ◽  
Digital Twin

The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support.

scholarly journals Probabilistic pushover analysis of reinforced concrete frame structures using dropout neural network

2021 ◽  
Vol 15 (1) ◽  
pp. 30-40
Author(s):  
Dang Viet Hung ◽  
Nguyen Truong Thang ◽  
Pham Xuan Dat
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Lateral Displacement ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Training Data ◽  
Data Driven ◽  
Frame Structures ◽  
Nonlinear Phenomena ◽  
Base Shear

When taking into consideration nonlinear phenomena such as material plasticity, plastic hinge, and P-Delta effect, the pushover analysis can provide more realistic structures’ nonlinear responses. However, this method is not widely used in practice as it is more complex and requires more expertise than elastic approaches. On the other hand, the data-driven method emerges as an increasingly appealing alternative since it requires only input parameters, then directly yields results in conditions that enough training data are provided, as well as an appropriate machine learning model is devised. Thus, this study develops a probabilistic data-driven approach using the Multiple Layer Perceptron network coupled with the Dropout mechanism to perform the pushover analysis of reinforced concrete (RC) frame structures, predicting base shear, lateral displacement, as well as their relationship between the two formers. Moreover, corresponding confidence intervals of predicted values are also available owing to the probabilistic nature of the method, thus helping engineers design conservative solutions. Keywords: pushover analysis; reinforced concrete; structure; probabilistic analysis; machine learning; dropout mechanism; OpenSees.

scholarly journals Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

Metals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 220 ◽  
Author(s):  
Johan Stendal ◽  
Markus Bambach ◽  
Mark Eisentraut ◽  
Irina Sizova ◽  
Sabine Weiß
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Flow Stress ◽  
Phenomenological Model ◽  
Titanium Aluminide ◽  
Learning Algorithm ◽  
Computing Time ◽  
Training Data ◽  
Data Driven ◽  
Experimental Conditions

Data-driven or machine learning approaches are increasingly being used in material science and research. Specifically, machine learning has been implemented in the fields of materials discovery, prediction of phase diagrams and material modelling. In this work, the application of machine learning to the traditional phenomenological flow stress modelling of the titanium aluminide (TiAl) alloy TNM-B1 (Ti-43.5Al-4Nb-1Mo-0.1B) is investigated. Three model types were developed, analyzed and compared; a physics-based phenomenological model (PM) originally developed for steel by Cingara and McQueen, a purely data-driven machine learning model (MLM), and a hybrid model (HM), which uses characteristic points predicted by a learning algorithm as input for the phenomenological model. The same amount of data was used to both fit the PM and train the MLM and HM. The models were analyzed and compared based on the accuracy of their predictions, development and computing time, and their ability to predict on interpolated and extrapolated inputs. The results revealed that for the same amount of experimental data, the MLM was more accurate than the PM. In addition, the MLM was better able to capture the characteristic peak stress in the TNM-B1 the flow curves, and could be developed and computed faster. Furthermore, the MLM was able to make realistic predictions for inputs outside the experimental data used for training. The HM showed comparable accuracy to the PM for the experimental conditions. However, the HM was able to produce a better fit for input conditions outside the training data.

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