Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

scholarly journals Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

Exhaustive Regressor Search (XRS) for Creating Models of Hydraulic Pumps and Motors

2021 ◽  
Author(s):  
Jack Johnson ◽  
John Montague ◽  
Jose Garcia-Bravo
Keyword(s):  
Experimental Data ◽  
Power Systems ◽  
Test Data ◽  
Physical Models ◽  
Fluid Power ◽  
Power Losses ◽  
Flow Response ◽  
Data Points ◽  
Fluid Power Systems ◽  
Theoretical Results

Abstract Physical models of fluid power systems rely on the validity of the principles used for creating such models. In many cases, pump and motor performance is considered a large contributor to the efficiency of a whole fluid power system and, is used to approximate the behavior of the component and the system coupled to it. Often, estimates of the power losses and efficiency of pumps and motors is limited to manufacturer test data or simplified assumptions based on first principles. However, the use of the limited test data or idealized assumptions reduces the accuracy of the models and limits the validity of the theoretical results. Moreover, the creation of accurate physical models, their numerical implementation using a computer to solve the model and the experimental validation is time consuming and costly. New advances in machine learning, statistical analysis and numerical methods can be used to reduce the time used to develop a model of a pump or motor producing similar or better results. This paper proposes the use of an autonomous and iterative algorithm to obtain linear regression coefficients necessary to characterize the flow response of a pump or motor from existing experimental data. In this study a multivariate linear model for predicting the flow output of a pump or a motor is derived from experimental data by iteratively adding data points and by iteratively and autonomously testing regressor combinations to find the best possible flow model.

Pressure and velocity measurements of air flow past a proposed generic Aircraft carrier geometry

2021 ◽  
pp. 147509022110489
Author(s):  
Kishore Vignesh Kumar ◽  
Sheikh Nasiruddin ◽  
Shrish Shukla ◽  
Sidh Nath Singh ◽  
Sawan Suman Sinha ◽  
...  
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Air Flow ◽  
Aircraft Carrier ◽  
Validation Data ◽  
Smoke Visualization ◽  
Flow Disturbances ◽  
Flow Investigation ◽  
Pressure And Velocity Measurements ◽  
Future Work

Research on the air flow disturbances in the aircraft carrier environment has gained prominence in recent times. However, there is presently no representative carrier model analogous to the Simplified Frigate Shape (SFS) which is generic naval frigate for air flow investigation. In the present study, a Generic Aircraft Carrier (GAC) model is proposed, as a simplified, benchmark model for aerodynamic research. With the motivation to provide validation data for future CFD studies, baseline experimental data is generated in the wind tunnel, in terms of pressure distribution over the deck, for two variants, namely, a complete flat deck configuration with no island and secondly, with the island in the baseline position of the GAC. Effect of the island in modifying the flow is discussed by a comparison between the two variants. Particle Image Velocimetry (PIV) is employed to record velocity and turbulence levels in the GAC environment, highlighting regions of velocity deficits, and unsteady flow which may hinder the landing procedure of an approaching pilot. Comprehensive database of experimental data is presented as baseline data for future work and for validation of numerical models. Traditional tuft and smoke visualization studies are also conducted to provide corroboratory qualitative insights.

scholarly journals Numerical Simulation of Hydraulic Jumps. Part 1: Experimental Data for Modelling Performance Assessment

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 36 ◽  
Author(s):  
Daniel Valero ◽  
Nicolò Viti ◽  
Carlo Gualtieri
Keyword(s):  
Experimental Data ◽  
Performance Assessment ◽  
Numerical Models ◽  
Three Dimensional ◽  
Physical Models ◽  
Hydraulic Jumps ◽  
Complete Case ◽  
Numerical Investigations ◽  
Comparable Performance

Hydraulic jumps have been the object of extensive experimental investigation, providing the numerical community with a complete case study for models’ performance assessment. This study constitutes an exhaustive literature review on hydraulic jumps’ experimental datasets. Both mean and turbulent parameters characterising hydraulic jumps are comprehensively discussed, presenting at least a reference to one dataset. Three studies stand out over other datasets due to their completeness. Using them as reference for model validation may ensure homogeneous and comparable performance assessment for the upcoming numerical models. Experimental inaccuracies are also addressed, allowing the numerical modeller to understand the uncertainties of reduced physical models and its limitations. Part 2 presents the three-dimensional numerical investigations to date and their main achievements.

Adaptive Neural Fuzzy Inference (ANFI) Modeling Technique for Production of Marine Biosurfactant

2012 ◽  
Author(s):  
Ali A. Abbasi ◽  
M. T. Ahmadian
Keyword(s):  
Experimental Data ◽  
Surfactant Concentration ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Training Data ◽  
Statistical Experimental Design ◽  
Medium Components ◽  
Input Variables ◽  
The Relationship ◽  
Neural Fuzzy

In this study; a Sugeno type ANFI model which describes the relationship between the bio surfactant concentration as a model output and the critical medium components as its inputs has been constructed. The critical medium components are glucose, urea, SrCl2 and MgSo4. The experimental data for training and testing capability of the model obtained by a statistical experimental design which have been captured from literatures. Six generalized bell shaped membership function have been selected for each of input variables and based on the training data ANFI model has been trained using the hybrid learning algorithm. The yielded biosurfactant concentration values from the model prediction shows close agreement with the experimental data.

Adolescent Language: Models, Assessment, and Links to Reading

2019 ◽  
Author(s):  
Amanda Goodwin ◽  
Yaacov Petscher ◽  
Jamie Tock
Keyword(s):  
Reading Comprehension ◽  
Bifactor Model ◽  
Language Models ◽  
Multiple Group ◽  
Global Factor ◽  
Eighth Grade Students ◽  
Key Aspects ◽  
Future Work ◽  
The Relationship ◽  
Best Fit

Various models have highlighted the complexity of language. Building on foundational ideas regarding three key aspects of language, our study contributes to the literature by 1) exploring broader conceptions of morphology, vocabulary, and syntax, 2) operationalizing this theoretical model into a gamified, standardized, computer-adaptive assessment of language for fifth to eighth grade students entitled Monster, PI, and 3) uncovering further evidence regarding the relationship between language and standardized reading comprehension via this assessment. Multiple-group item response theory (IRT) across grades show that morphology was best fit by a bifactor model of task specific factors along with a global factor related to each skill. Vocabulary was best fit by a bifactor model that identifies performance overall and on specific words. Syntax, though, was best fit by a unidimensional model. Next, Monster, PI produced reliable scores suggesting language can be assessed efficiently and precisely for students via this model. Lastly, performance on Monster, PI explained more than 50% of variance in standardized reading, suggesting operationalizing language via Monster, PI can provide meaningful understandings of the relationship between language and reading comprehension. Specifically, considering just a subset of a construct, like identification of units of meaning, explained significantly less variance in reading comprehension. This highlights the importance of considering these broader constructs. Implications indicate that future work should consider a model of language where component areas are considered broadly and contributions to reading comprehension are explored via general performance on components as well as skill level performance.

Potential Role of Inflammation in Associations between Particulate Matter and Heart Failure

2018 ◽  
Vol 24 (3) ◽  
pp. 341-358 ◽  
Author(s):  
Xiaotong Ji ◽  
Yingying Zhang ◽  
Guangke Li ◽  
Nan Sang
Keyword(s):  
Heart Failure ◽  
Particulate Matter ◽  
Inflammatory Response ◽  
Heart Diseases ◽  
Experimental Studies ◽  
Epidemiological Studies ◽  
Inflammatory Mechanism ◽  
High Concentrations ◽  
Future Work ◽  
The Relationship

Recently, numerous studies have found that particulate matter (PM) exposure is correlated with increased hospitalization and mortality from heart failure (HF). In addition to problems with circulation, HF patients often display high expression of cytokines in the failing heart. Thus, as a recurring heart problem, HF is thought to be a disorder characterized in part by the inflammatory response. In this review, we intend to discuss the relationship between PM exposure and HF that is based on inflammatory mechanism and to provide a comprehensive, updated evaluation of the related studies. Epidemiological studies on PM-induced heart diseases are focused on high concentrations of PM, high pollutant load exposure in winter, or susceptible groups with heart diseases, etc. Furthermore, it appears that the relationship between fine or ultrafine PM and HF is stronger than that between HF and coarse PM. However, fewer studies paid attention to PM components. As for experimental studies, it is worth noting that coarse PM may indirectly promote the inflammatory response in the heart through systematic circulation of cytokines produced primarily in the lungs, while ultrafine PM and its components can enter circulation and further induce inflammation directly in the heart. In terms of PM exposure and enhanced inflammation during the pathogenesis of HF, this article reviews the following mechanisms: hemodynamics, oxidative stress, Toll-like receptors (TLRs) and epigenetic regulation. However, many problems are still unsolved, and future work will be needed to clarify the complex biologic mechanisms and to identify the specific components of PM responsible for adverse effects on heart health.

Simulation on Extrusion Comminution Model of Roller Press

2010 ◽  
Vol 156-157 ◽  
pp. 1702-1707
Author(s):  
Xiang Wen Cheng ◽  
Jinchao Liu ◽  
Qi Zhi Ding ◽  
Li Ming Song ◽  
Zhan Lin Wang
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Field Experiments ◽  
Size Ratio ◽  
Optimum Operating ◽  
Extrusion Force ◽  
Particle Size Ratio ◽  
Small Constant ◽  
Working Parameters ◽  
The Relationship

How to predict the relationship among particle size and among product size, to establish the relationship between the granularity and working parameters in the process of grinding and to determine the optimum operating parameters. With proposing BS squeeze crush model by L. Bass and the idea of roll surface division as the material uneven extrusion force are adopted. Based on field experiments the experimental data is analyzed, the select function and the breakage functions are fitted with MATLAB software, and obtaining their model. The comminution model is determined by the roller division. We obtain the model parameter through the experimental data. Through model analysis shows: the relationship between particle breakage and energy absorption, namely the smaller size of the same power, the lower broken; the breakage diminishes with the decrease of particle size ratio and it will be tending to a small constant when the smaller particle size ratio. The breakage functions rapidly decrease within ratio of between 0.2-0.7. This shows: the energy consumption will rapidly increase when the particle size of less than 0.2 in broken; the selection diminish with the decrease of particle size. Pressure (8-9MPa) should be the most appropriate value.

scholarly journals Two-Dimensional Numerical Modeling of Flow in Physical Models of Rock Vane and Bendway Weir Configurations

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 458
Author(s):  
Drew C. Baird ◽  
Benjamin Abban ◽  
S. Michael Scurlock ◽  
Steven B. Abt ◽  
Christopher I. Thornton
Keyword(s):  
Numerical Modeling ◽  
Physical Model ◽  
Numerical Models ◽  
Hydraulic Performance ◽  
Physical Models ◽  
Protection Measures ◽  
Design Engineers ◽  
Model Study ◽  
Study Results ◽  
Wide Range

While there are a wide range of design recommendations for using rock vanes and bendway weirs as streambank protection measures, no comprehensive, standard approach is currently available for design engineers to evaluate their hydraulic performance before construction. This study investigates using 2D numerical modeling as an option for predicting the hydraulic performance of rock vane and bendway weir structure designs for streambank protection. We used the Sedimentation and River Hydraulics (SRH)-2D depth-averaged numerical model to simulate flows around rock vane and bendway weir installations that were previously examined as part of a physical model study and that had water surface elevation and velocity observations. Overall, SRH-2D predicted the same general flow patterns as the physical model, but over- and underpredicted the flow velocity in some areas. These over- and underpredictions could be primarily attributed to the assumption of negligible vertical velocities. Nonetheless, the point differences between the predicted and observed velocities generally ranged from 15 to 25%, with some exceptions. The results showed that 2D numerical models could provide adequate insight into the hydraulic performance of rock vanes and bendway weirs. Accordingly, design guidance and implications of the study results are presented for design engineers.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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