Investigation of Black Box Modeling Approaches for Representation of Transient Gearshift Processes in Automotive Powertrains with Automatic Transmission

Thermodynamic Limits and Optimality of Microbial Growth

Entropy ◽

10.3390/e22030277 ◽

2020 ◽

Vol 22 (3) ◽

pp. 277

Author(s):

Nima Saadat ◽

Tim Nies ◽

Yvan Rousset ◽

Oliver Ebenhöh

Keyword(s):

Microbial Growth ◽

Black Box ◽

Cellular Growth ◽

Modeling Approaches ◽

Theoretical Approaches ◽

Growing Cell ◽

Limiting Nutrient ◽

Growth Laws ◽

Thermodynamic Limits ◽

Optimality Principles

Understanding microbial growth with the use of mathematical models has a long history that dates back to the pioneering work of Jacques Monod in the 1940s. Monod’s famous growth law expressed microbial growth rate as a simple function of the limiting nutrient concentration. However, to explain growth laws from underlying principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified the principal limitations of the growth process. Although these approaches ignored metabolic details and instead considered microbial metabolism as a black box, modern theories heavily rely on genomic resources to describe and model metabolism in great detail to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modeling approaches only in a rather superficial fashion, and it appears that recent modeling approaches and classical theories are rather disconnected fields. To stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamics and outline the resulting thermodynamic limits and optimality principles. We start with classical black box models of cellular growth, and continue with recent metabolic modeling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer key black box parameters, such as the energy of formation or the degree of reduction of biomass. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

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Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks

Canadian Journal of Civil Engineering ◽

10.1139/l00-016 ◽

2000 ◽

Vol 27 (4) ◽

pp. 671-682 ◽

Cited By ~ 8

Author(s):

N Lauzon ◽

J Rousselle ◽

S Birikundavyi ◽

H T Trung

Keyword(s):

Neural Network ◽

Neural Networks ◽

Diffusion Process ◽

Diffusion Processes ◽

Black Box ◽

Linear Type ◽

Modeling Approaches ◽

Box Models ◽

Almost All ◽

Black Box Models

The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.

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Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Scientific Reports ◽

10.1038/s41598-020-70295-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Ophélie Lo-Thong ◽

Philippe Charton ◽

Xavier F. Cadet ◽

Brigitte Grondin-Perez ◽

Emma Saavedra ◽

...

Keyword(s):

Metabolic Pathway ◽

Black Box ◽

Modeling Approaches

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An Empirically Derived Pressure Compensation Control System for a Variable Displacement Vane Pump

BATH/ASME 2018 Symposium on Fluid Power and Motion Control ◽

10.1115/fpmc2018-8857 ◽

2018 ◽

Author(s):

Ryan P. Jenkins ◽

Monika Ivantysynova

Keyword(s):

Control System ◽

System Performance ◽

Low Cost ◽

Automatic Transmission ◽

Black Box ◽

Test Rig ◽

Vane Pump ◽

Variable Displacement Vane Pump ◽

Variable Displacement ◽

Pressure Compensation

Pressure compensated vane pumps are well suited to applications such as automatic transmissions which require a low-cost, compact solution to provide the hydraulic power required for clutch control as well as the lubrication and cooling functions. This paper presents a black-box model of the series of valves providing the flowrate to control the motion of the pivoting cam in a variable displacement vane pump from an automatic transmission application. This series of valves consists of a pressure-reducing valve followed by a solenoid-operated valve that generates a pilot pressure acting on the main pressure regulator valve to adjust the commanded pump outlet pressure setting. Valves taken from a transmission control block were integrated into a custom unit and installed on a test rig with a modified vane pump. Measurements previously collected on this test rig were used to validate a lumped-parameter vane pump model and provide data containing the input-output relationships of the pressure compensation system valves. An analysis of the black-box description of this control system identifies limitations to the achievable system performance. This analysis reveals that the low-cost solenoid-operated valve and the arrangement of the valves within the control circuit both contribute to a controllable bandwidth less than 2Hz. Finally, the paper presents an alternate control system design capable of improved system performance.

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