scholarly journals Diving into a Simple Anguilliform Swimmer’s Sensitivity

2020 ◽  
Vol 60 (5) ◽  
pp. 1236-1250 ◽  
Author(s):  
Nicholas A Battista
Keyword(s):  
Reynolds Number ◽  
Computational Models ◽  
Performance Metrics ◽  
Global Sensitivity Analysis ◽  
Swimming Performance ◽  
Natural Extension ◽  
Kinematic Parameter ◽  
Aquatic Locomotion ◽  
Behavioral Dynamics ◽  
Unique Parameter

Synopsis Computational models of aquatic locomotion range from modest individual simple swimmers in 2D to sophisticated 3D multi-swimmer models that attempt to parse collective behavioral dynamics. Each of these models contain a multitude of model input parameters to which its outputs are inherently dependent, that is, various performance metrics. In this work, the swimming performance’s sensitivity to parameters is investigated for an idealized, simple anguilliform swimming model in 2D. The swimmer considered here propagates forward by dynamically varying its body curvature, similar to motion of a Caenorhabditis elegans. The parameter sensitivities were explored with respect to the fluid scale (Reynolds number), stroke (undulation) frequency, as well as a kinematic parameter controlling the velocity and acceleration of each upstroke and downstroke. The input Reynolds number and stroke frequencies sampled were from [450, 2200] and [1, 3] Hz, respectively. In total, 5000 fluid–structure interaction simulations were performed, each with a unique parameter combination selected via a Sobol sequence, in order to conduct global sensitivity analysis. Results indicate that the swimmer’s performance is most sensitive to variations in its stroke frequency. Trends in swimming performance were discovered by projecting the performance data onto particular 2D subspaces. Pareto-like optimal fronts were identified. This work is a natural extension of the parameter explorations of the same model from Battista in 2020.

scholarly journals Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

2021 ◽  
Author(s):  
Syed Ishtiyaq Ahmed ◽  
Sreevatsan Radhakrishnan ◽  
Binoy B Nair ◽  
Rajagopalan Thiruvengadathan
Keyword(s):  
Machine Learning ◽  
Surface Area ◽  
Computational Models ◽  
Performance Metrics ◽  
Structural Features ◽  
Optimization Techniques ◽  
Storage Device ◽  
Micropore Surface Area ◽  
Supercapacitor Performance ◽  
Carbon Based

Abstract Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. This work reports the development and utilization of highly tuned and efficient Machine Learning (ML) models that give insights into correlation between structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The authors demonstrate the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.

Experimental and Computational Study of Turbulent Flows in a Channel With Two Pairs of Turbulence Promoters in Tandem

1990 ◽  
Vol 112 (3) ◽  
pp. 302-310 ◽  
Author(s):  
T.-M. Liou ◽  
Y. Chang ◽  
D.-W. Hwang
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Turbulent Flows ◽  
Turbulence Intensity ◽  
Computational Models ◽  
Computational Study ◽  
Reattachment Length ◽  
Transfer Enhancement ◽  
Mean Velocity ◽  
Pitch Ratio

Measurements and computations are presented of mean velocity and turbulence intensity for an arrangement of two pairs of turbulence promoters mounted in tandem in developing channel flow. The Reynolds number (ReD) and the pitch ratio (PR) were varied in the range of 1.2 × 104 to 1.2 × 105 and 1 to 100, respectively. The three pitch ratios 5, 10, 15 were found to provide three characteristic flows which are a useful test of the computational models. The effects of PR on the reattachment lengths and the pressure loss as well as the influence of ReD on the reattachment length were documented in detail. It was found that PR=10 was preferable to PR = 5 and PR = 15 from the standpoint of heat transfer enhancement.

scholarly journals Parameter and model recovery of reinforcement learning models for restless bandit problems

2021 ◽  
Author(s):  
Ludwig Danwitz ◽  
David Mathar ◽  
Elke Smith ◽  
Deniz Tuzsus ◽  
Jan Peters
Keyword(s):  
Kalman Filter ◽  
Cognitive Modeling ◽  
Computational Models ◽  
Model Comparison ◽  
Performance Metrics ◽  
Model Parameters ◽  
Parameter Recovery ◽  
Restless Bandit ◽  
Model Free ◽  
Lower Accuracy

Multi-armed restless bandit tasks are regularly applied in psychology and cognitive neuroscience to assess exploration and exploitation behavior in structured environments. These models are also readily applied to examine effects of (virtual) brain lesions on performance, and to infer neurocomputational mechanisms using neuroimaging or pharmacological approaches. However, to infer individual, psychologically meaningful parameters from such data, computational cognitive modeling is typically applied. Recent studies indicate that softmax (SM) decision rule models that include a representation of environmental dynamics (e.g. the Kalman Filter) and additional parameters for modeling exploration and perseveration (Kalman SMEP) fit human bandit task data better than competing models. Parameter and model recovery are two central requirements for computational models: parameter recovery refers to the ability to recover true data-generating parameters; model recovery refers to the ability to correctly identify the true data generating model using model comparison techniques. Here we comprehensively examined parameter and model recovery of the Kalman SMEP model as well as nested model versions, i.e. models without the additional parameters, using simulation and Bayesian inference. Parameter recovery improved with increasing trial numbers, from around .8 for 100 trials to around .93 for 300 trials. Model recovery analyses likewise confirmed acceptable recovery of the Kalman SMEP model. Model recovery was lower for nested Kalman filter models as well as delta rule models with fixed learning rates. Exploratory analyses examined associations of model parameters with model-free performance metrics. Random exploration, captured by the inverse softmax temperature, was associated with lower accuracy and more switches. For the exploration bonus parameter modeling directed exploration, we confirmed an inverse- U-shaped association with accuracy, such that both an excess and a lack of directed exploration reduced accuracy. Taken together, these analyses underline that the Kalman SMEP model fulfills basic requirements of a cognitive model.

Movement in Water

2018 ◽  
Keyword(s):  
Reynolds Number ◽  
Physical Properties ◽  
Swimming Performance ◽  
Water Interface ◽  
Water Influence ◽  
Air Water Interface ◽  
The Many

This chapter examines how the physical properties of water influence and explain the great diversity of swimming performance and mechanisms - from the scale of spermatozoa on up to whales. The key parameters of inertia, viscosity and their manifestation in the critically important Reynolds number are explained and placed in the context of a range of swimming mechanisms, including undulatory movement and fin-based, jet-based, flagellar and ciliary propulsion. The air-water interface also presents an intriguing mechanical challenge for the many organisms that move on top of the water’s surface. The chapter concludes with a brief overview of the burgeoning field of biorobotic swimmers.

Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment

2021 ◽  
Author(s):  
José M. Rodríguez-Flores ◽  
Jorge A. Valero-Fandiño ◽  
Spencer A. Cole ◽  
Keyvan Malek ◽  
Tina Karimi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Economic Model ◽  
Performance Metrics ◽  
Global Sensitivity Analysis ◽  
Crop Yields ◽  
Production Model ◽  
Groundwater Pumping ◽  
Global Sensitivity ◽  
Artificial Neural Network Ann ◽  
Price Uncertainties

Abstract The modeling of coupled food-water systems to represent the effect of water supply variability as well as shocks that may emerge from changes in policies, economic drivers, and productivity requires an understanding of dominant uncertainties. These uncertainties cascade into forecasts of impacts of water management policies, such as groundwater pumping restrictions. This paper assesses how parametric, crop price, crop yields, surface water price, and electricity price uncertainties shape hydro-economic model estimates for agricultural production through a diagnostic global sensitivity analysis (GSA).The diagnostic GSA explores how the uncertainties in combination with a candidate groundwater pumping restriction influence three metrics of concern: total economic revenue, total land use and groundwater depth change. The hydro-economic model integrates a Groundwater Response Function (GRF) by integrating an Artificial Neural Network (ANN) into a calibrated Positive Mathematical Programming (PMP) production model for the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California. Our results show that in addition to groundwater pumping restriction, performance metrics of the system are highly sensitive to prices and yields particularly of profitable crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater.

scholarly journals Ectoparasites increase swimming costs in a coral reef fish

2013 ◽  
Vol 9 (1) ◽  
pp. 20120927 ◽  
Author(s):  
Sandra A. Binning ◽  
Dominique G. Roche ◽  
Cayne Layton
Keyword(s):  
Performance Metrics ◽  
Swimming Performance ◽  
Metabolic Rates ◽  
Fish Swimming ◽  
Individual Fitness ◽  
High Speeds ◽  
Consumption Rates ◽  
Lower Maximum ◽  
Morphological And Physiological Traits ◽  
Host Physiology

Ectoparasites can reduce individual fitness by negatively affecting behavioural, morphological and physiological traits. In fishes, there are potential costs if ectoparasites decrease streamlining, thereby directly compromising swimming performance. Few studies have examined the effects of ectoparasites on fish swimming performance and none distinguish between energetic costs imposed by changes in streamlining and effects on host physiology. The bridled monocle bream ( Scolopsis bilineatus ) is parasitized by an isopod ( Anilocra nemipteri), which attaches above the eye. We show that parasitized fish have higher standard metabolic rates (SMRs), poorer aerobic capacities and lower maximum swimming speeds than non-parasitized fish. Adding a model parasite did not affect SMR, but reduced maximum swimming speed and elevated oxygen consumption rates at high speeds to levels observed in naturally parasitized fish. This demonstrates that ectoparasites create drag effects that are important at high speeds. The higher SMR of naturally parasitized fish does, however, reveal an effect of parasitism on host physiology. This effect was easily reversed: fish whose parasite was removed 24 h earlier did not differ from unparasitized fish in any performance metrics. In sum, the main cost of this ectoparasite is probably its direct effect on streamlining, reducing swimming performance at high speeds.

Simulation of Pressure Drop and Energy Dissipation for Blood Flow in a Human Fetal Bifurcation

1998 ◽  
Vol 120 (4) ◽  
pp. 455-462 ◽  
Author(s):  
L. R. Hellevik ◽  
T. Kiserud ◽  
F. Irgens ◽  
T. Ytrehus ◽  
S. H. Eik-Nes
Keyword(s):  
Reynolds Number ◽  
Energy Dissipation ◽  
Pressure Drop ◽  
Computational Models ◽  
Umbilical Vein ◽  
Three Dimensional ◽  
Stationary Flow ◽  
Ductus Venosus ◽  
Shape Factors ◽  
Curvature Ratio

The pressure drop from the umbilical vein to the heart plays a vital part in human fetal circulation. The bulk of the pressure drop is believed to take place at the inlet of the ductus venosus, a short narrow branch of the umbilical vein. In this study a generalized Bernoulli formulation was deduced to estimate this pressure drop. The model contains an energy dissipation term and flow-scaled velocities and pressures. The flow-scaled variables are related to their corresponding spatial mean velocities and pressures by certain shape factors. Further, based on physiological measurements, we established a simplified, rigid-walled, three-dimensional computational model of the umbilical vein and ductus venosus bifurcation for stationary flow conditions. Simulations were carried out for Reynolds numbers and umbilical vein curvature ratios in their respective physiological ranges. The shape factors in the Bernoulli formulation were then estimated for our computational models. They showed no significant Reynolds number or curvature ratio dependency. Further, the energy dissipation in our models was estimated to constitute 24 to 31 percent of the pressure drop, depending on the Reynolds number and the curvature ratio. The energy dissipation should therefore be taken into account in pressure drop estimates.

scholarly journals A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling

2012 ◽  
Vol 9 (74) ◽  
pp. 2156-2166 ◽  
Author(s):  
T. Sumner ◽  
E. Shephard ◽  
I. D. L. Bogle
Keyword(s):  
Sensitivity Analysis ◽  
Systems Biology ◽  
Computational Models ◽  
Global Sensitivity Analysis ◽  
Insulin Signalling ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Global Sensitivity ◽  
Insulin Signalling Pathway ◽  
Parameter Values

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

scholarly journals Kinematic arrangement optimization of a quadruped robot with genetic algorithms

2018 ◽  
Vol 51 (9-10) ◽  
pp. 406-416 ◽  
Author(s):  
Mehmet Mert Gülhan ◽  
Kemalettin Erbatur
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Metrics ◽  
Dynamic Performance ◽  
Performance Measure ◽  
Kinematic Parameter ◽  
Algorithm Optimization ◽  
Performance Improvements ◽  
Performance Specifications ◽  
Dynamics Simulations

Background: As research on quadruped robots grows, so does the variety of designs available. These designs are often inspired by nature and finalized around various technical, instrumentation-based constraints. However, no systematic methodology of kinematic parameter selection to reach performance specifications is reported so far. Kinematic design optimization with objective functions derived from performance metrics in dynamic tasks is an underexplored, yet promising area. Methods: This article proposes to use genetic algorithms to handle the designing process. Given the dynamic tasks of jumping and trotting, body and leg link dimensions are optimized. The performance of a design in genetic algorithm search iterations is evaluated via full-dynamics simulations of the task. Results: The article presents comparisons of design results optimized for jumping and trotting separately. Significant dimensional dissimilarities and associated performance differences are observed in this comparison. A combined performance measure for jumping and trotting tasks is studied too. It is discussed how significantly various structural lengths affect dynamic performances in these tasks. Results are compared to a relatively more conventional quadruped design too. Conclusions: The task-specific nature of this optimization process improves the performances dramatically. This is a significant advantage of the systematic kinematic parameter optimization over straight mimicking of nature in quadruped designs. The performance improvements obtained by the genetic algorithm optimization with dynamic performance indices indicate that the proposed approach can find application area in the design process of a variety of robots with dynamic tasks.

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