scholarly journals Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

2021 ◽  
Vol 9 ◽  
Author(s):  
Wazif Sallehhudin ◽  
Aya Diab
Keyword(s):  
Machine Learning ◽  
Uncertainty Quantification ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Severe Accidents ◽  
Loss Of Coolant Accident ◽  
Lumped Parameter ◽  
Loss Of Coolant ◽  
Artificial Neural Network Ann ◽  
Uncertainty Parameters

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

scholarly journals Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

2018 ◽  
Author(s):  
Jatin Kumar ◽  
Qianxiao Li ◽  
Karen Y.T. Tang ◽  
Tonio Buonassisi ◽  
Anibal L. Gonzalez-Oyarce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Point ◽  
Disordered Systems ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inverse Design ◽  
Gradient Boosting ◽  
Multiple Length Scales ◽  
The Status

<div><div><div><p>Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.</p></div></div></div>

scholarly journals Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

2020 ◽  
Vol 8 (12) ◽  
pp. 992
Author(s):  
Mengning Wu ◽  
Christos Stefanakos ◽  
Zhen Gao
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
Computational Cost ◽  
Model Performance ◽  
The North ◽  
The North Sea ◽  
Wave Conditions ◽  
Wave Models ◽  
Artificial Neural Network Ann

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

Large Break LOCA Accident Management Strategies for Accidents With Large Containment Leaks

2006 ◽  
Author(s):  
Gert Sdouz
Keyword(s):  
Source Term ◽  
Management Strategies ◽  
Severe Accidents ◽  
Loss Of Coolant Accident ◽  
Management Measures ◽  
Accident Management ◽  
Loss Of Coolant ◽  
Station Blackout ◽  
Accident Sequences ◽  
Comprehensive Study

The goal of this work is the investigation of the influence of different accident management strategies on the thermal-hydraulics in the containment during a Large Break Loss of Coolant Accident with a large containment leak from the beginning of the accident. The increasing relevance of terrorism suggests a closer look at this kind of severe accidents. Normally the course of severe accidents and their associated phenomena are investigated with the assumption of an intact containment from the beginning of the accident. This intact containment has the ability to retain a large part of the radioactive inventory. In these cases there is only a release via a very small leakage due to the untightness of the containment up to cavity bottom melt through. This paper represents the last part of a comprehensive study on the influence of accident management strategies on the source term of VVER-1000 reactors. Basically two different accident sequences were investigated: the “Station Blackout”-sequence and the “Large Break LOCA”. In a first step the source term calculations were performed assuming an intact containment from the beginning of the accident and no accident management action. In a further step the influence of different accident management strategies was studied. The last part of the project was a repetition of the calculations with the assumption of a damaged containment from the beginning of the accident. This paper concentrates on the last step in the case of a Large Break LOCA. To be able to compare the results with calculations performed years ago the calculations were performed using the Source Term Code Package (STCP), hydrogen explosions are not considered. In this study four different scenarios have been investigated. The main parameter was the switch on time of the spray systems. One of the results is the influence of different accident management strategies on the source term. In the comparison with the sequence with intact containment it was demonstrated that the accident management measures have quite lower consequences. In addition it was shown that in the case of a “Large Break LOCA”-sequence the intact containment retains the nuclides up to a factor of 20 000. This is much more than in the case of a “Station Blackout”-sequence. Within the frame of the study 17 source terms have been generated to evaluate in detail accident management strategies for VVER-1000 reactors.

scholarly journals Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 130 ◽  
Author(s):  
Lim ◽  
Kim ◽  
Park
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Center Of Mass ◽  
Lower Limbs ◽  
Joint Kinetics ◽  
Joint Torques ◽  
Dynamic Relationship ◽  
Reaction Forces ◽  
Artificial Neural Network Ann ◽  
Parameter Values

Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

Analysis of the Containment Thermal-Hydraulic Behavior of a Small Reactor During Loss of Coolant Accident

2017 ◽  
Author(s):  
Qian Lin ◽  
Weizhong Zhang
Keyword(s):  
Cooling System ◽  
Three Dimensional ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
One Dimensional ◽  
Loss Of Coolant Accident ◽  
Lumped Parameter ◽  
Loss Of Coolant ◽  
Calculation Results ◽  
The One

The containment thermal hydraulics of a small reactor during loss of coolant accident (LOCA) is studied by a lumped parameter one-dimensional model and a three-dimensional model. The capability of a kind of heat exchanger type passive containment cooling system (PCCS) is analyzed by the one-dimensional model. The calculation results show that, the decay heat can be removed and the containment pressure can be decreased by the proposed PCCS. The steam and non-condensable gas (the air) distribution in the containment is investigated, the mixing and stratification behaviors are analyzed for several different cases, in which the PCCS and condenser are located at higher, base or lower position. The sensitivity analysis of the PCCS elevation shows that, in despite of the different gas stratification, the containment pressures are nearly the same. Similar conclusions can be obtained by the one-dimensional model and three-dimensional model. The preliminary results may indicate that, the designed PCCS and condenser can be located at a lower part, which will be benefit for the economy of the small reactor or meet other requirements.

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

2021 ◽  
pp. 99-115
Author(s):  
Rekha K. V. ◽  
Anirudh Itagi ◽  
Bharath K. P. ◽  
Balaji Subramanian ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Learning Algorithm ◽  
Research Work ◽  
Early Period ◽  
Pulmonary Nodules ◽  
Artificial Neural Network Ann ◽  
Benign Nodules ◽  
Occurrence Matrix ◽  
Nodule Classification

The research work is to enhance the classification accuracy of the pulmonary nodules with the limited number of features extracted using Gray level co-occurrence matrix and linear binary pattern. The classification is done using the machine learning algorithm such as artificial neural network (ANN) and the random forest classifier (RF). In present, lung cancer seems to be the most deadly disease in the world which can be detected only after the computerized tomography (i.e., CT scan images of the person). Detecting the infected portion at the early period is the challenging task. Hence, the recent researchers where under the detection of pulmonary nodules to categorize it either as benign nodules which named as non-cancerous or as malignant nodules which are named as cancerous. When associated the results with the recent papers, the accuracy has been improved in classifying the lung nodules.

scholarly journals Modeling Focused-Ultrasound Response for Non-Invasive Treatment Using Machine Learning

2021 ◽  
Vol 8 (6) ◽  
pp. 74
Author(s):  
Tariq Mohammad Arif ◽  
Zhiming Ji ◽  
Md Adilur Rahim ◽  
Bharath Babu Nunna
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Computational Models ◽  
Focused Ultrasound ◽  
Planning Process ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Numerical Models ◽  
Information Criterion ◽  
Body Tissues

The interactions between body tissues and a focused ultrasound beam can be evaluated using various numerical models. Among these, the Rayleigh–Sommerfeld and angular spectrum methods are considered to be the most effective in terms of accuracy. However, they are computationally expensive, which is one of the underlying issues of most computational models. Typically, evaluations using these models require a significant amount of time (hours to days) if realistic scenarios such as tissue inhomogeneity or non-linearity are considered. This study aims to address this issue by developing a rapid estimation model for ultrasound therapy using a machine learning algorithm. Several machine learning models were trained on a very-large dataset (19,227 simulations), and the performance of these models were evaluated with metrics such as Root Mean Squared Error (RMSE), R-squared (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The resulted random forest provides superior accuracy with an R2 value of 0.997, an RMSE of 0.0123, an AIC of −82.56, and a BIC of −81.65 on an external test dataset. The results indicate the efficacy of the random forest-based model for the focused ultrasound response, and practical adoption of this approach will improve the therapeutic planning process by minimizing simulation time.

scholarly journals Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use

2021 ◽  
Vol 3 ◽  
Author(s):  
Marit P. van Dijk ◽  
Manon Kok ◽  
Monique A. M. Berger ◽  
Marco J. M. Hoozemans ◽  
DirkJan H. E. J. Veeger
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Body Segment ◽  
Inertial Measurement ◽  
Wheelchair Sports ◽  
Trunk Inclination ◽  
Better Than

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.

scholarly journals Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

2018 ◽  
Author(s):  
Jatin Kumar ◽  
Qianxiao Li ◽  
Karen Y.T. Tang ◽  
Tonio Buonassisi ◽  
Anibal L. Gonzalez-Oyarce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Point ◽  
Disordered Systems ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inverse Design ◽  
Gradient Boosting ◽  
Multiple Length Scales ◽  
The Status

<div><div><div><p>Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.</p></div></div></div>

scholarly journals Machine Learning Cases in Clinical and Biomedical Domains

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Mohd Zulfaezal Che Azemin ◽  
Tijani Ahmed Ashimi ◽  
Md Muziman Syah
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
University Of California ◽  
General Audience ◽  
Supervised And Unsupervised Learning ◽  
Hypothetical Data ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning ◽  
Compare And Contrast ◽  
Learning Concept

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little or no knowledge of machine learning with examples of applications in clinical and biomedical domains, and secondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic concept of intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised and unsupervised learning. To better understand the machine learning concept, hypothetical data of glaucoma cases are presented. ANN is then selected as an example of supervised learning and the underlying principles in ANN are presented with general audience in mind with an attempt to relate the mechanism employed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications of machine learning in clinical and biomedical domains are briefly demonstrated based on the author’s own research and most recent examples available from University of California, Irvine Machine Learning Repository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke to those who do not activate the intellect are presented. The evidence found from the verses suggests that ANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introduced to the algorithm.

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