A white-box model of S-shaped and double S-shaped single-species population growth

Kalmykov, Lev V.; Kalmykov, Vyacheslav L.

doi:10.7717/peerj.948

Cited by 14 publications

(17 citation statements)

References 15 publications

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“…In other cases, supplemented by a probabilistic determination of the competitiveness the models are of the greybox type. Mathematical models of complex systems may be of three types in accordance with the degree of their mechanicalness -black-box, grey-box and white-box Kalmykov 2015c, Kalmykov andKalmykov 2016).…”

Section: Individual-based Mechanisms Of Coexistence Of Complete Compementioning

confidence: 99%

Axiomatic-deductive theory of competition of complete competitors: coexistence, exclusion and neutrality in one model

Kalmykov

2020

Preprint

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BackgroundThe long-standing contradiction between formulations of the competitive exclusion principle and natural diversity of trophically similar species is known as the biodiversity paradox. Earlier we found that coexistence of complete competitors is possible despite 100% difference in competitiveness, but only under certain conditions – at their moderate propagation and at the particular initial location of individuals. Here we verify a hypothesis that completely competing species with aggressive propagation may coexist with less than 100% difference in competitiveness regardless of random initial location of competing individuals in ecosystem.MethodsWe investigate a role of competitiveness differences in coexistence of two completely competing species by individual-based modeling based on a transparent artificial intelligence. We propose and investigate an individual-based model of ecosystem dynamics supplemented by a probabilistic determination of the competitiveness of competing individuals without cooperative effects and with cooperative effects based on the numerical superiority of individuals of the species.ResultsWe have found that two aggressively propagating complete competitors can stably coexist, despite one species has some advantage in competitiveness over the other and all other characteristics of the species are equal. The found competitive coexistence occurred regardless of the initial random location of individuals in the ecosystem. When colonization of a free habitat started from a single individual of each species, then the complete competitors coexisted up to 31% of their difference in competitiveness. And when on initial stage half of the territory was probabilistically occupied, the complete competitors coexisted up to 22% of their difference in competitiveness. In the experiments with cooperative dependence on the numerical superiority of individuals of the species complete competitors stably co-existed despite 10% difference in basic competitiveness.DiscussionThe results additionally support our earlier reformulation of the competitive exclusion principle. Besides that, we revealed classical cases of competitive exclusion and “neutrality”. Our approach unifies models of competitive exclusion (“niche”), neutrality and coexistence of complete competitors in one theory. Our individual-based modeling of a complex system based on a transparent artificial intelligence opens up great prospects for a variety of theoretical and applied fields.

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Section: Individual-based Mechanisms Of Coexistence Of Complete Compementioning

confidence: 99%

Axiomatic-deductive theory of competition of complete competitors: coexistence, exclusion and neutrality in one model

Kalmykov

2020

Preprint

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“…System identification (SI) is the core of the prediction of the system behaviour . In this work, an AI methodology named GP is employed for solving the problem.…”

Section: System Identificationmentioning

confidence: 99%

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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Summary In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.

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“…Computational fluid dynamics (CFD) simulations is used to generate parasitic power and battery temperature data for various discharge rates, operating time of fan, and inlet velocity. These collected data is used for establishment of a numerical model using artificial intelligence (AI) techniques for BTMS 31,32 . Inlet velocity, time at which fan is switched on, and discharge rate are the inputs of numerical model and outputs are parasitic energy.…”

Section: Introductionmentioning

confidence: 99%

A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

Jishnu

Garg

et al. 2020

Energy Storage

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Air‐cooling‐based battery thermal management system (BTMS) is a research hotspot for electric vehicles because of lower cost and simpler design. Past research works have immensely concentrated on the enhancement of heat removal from Li‐ion battery, but the minimum consideration has been given to minimize the parasitic power. In this paper, a novel procedure is proposed to predict the operating parameters (inlet velocity, working time of fan, and range of heat generation rates) of air‐cooling design for minimizing parasitic power and ensuring the battery temperature do not exceed the upper threshold limit simultaneously. Based on findings via computational fluid dynamics analysis, an empirical model of air‐cooling BTMS is further developed using an evolutionary approach of model selection criteria approximated genetic programming (MSC‐GP). The model is then optimized to determine operating parameters of air cooling which causes minimum parasitic power while keeping the average temperature of battery cells within the limit. Further, sensitivity analysis and parameter interaction (2D and 3D) analysis is also performed to study the effect and identify the contribution of various operating parameters on parasitic power. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only. Finally, the optimal values of operating parameters for various heat generation rate per cell (function of discharge rate) are obtained. The optimized parasitic power was observed to be nonlinearly increasing with heat generation rate. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However, parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only.

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A white-box model of S-shaped and double S-shaped single-species population growth

Cited by 14 publications

References 15 publications

Axiomatic-deductive theory of competition of complete competitors: coexistence, exclusion and neutrality in one model

Axiomatic-deductive theory of competition of complete competitors: coexistence, exclusion and neutrality in one model

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

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