A Thermodynamic Approach To Predict Black Box Model Parameters For Microbial Growth

2013 ◽  
pp. 465-496
Keyword(s):  
Microbial Growth ◽  
Black Box ◽  
Box Model ◽  
Thermodynamic Approach ◽  
Model Parameters

Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters

2021 ◽  
Author(s):  
Ankush Chakrabarty ◽  
Scott A. Bortoff ◽  
Christopher R. Laughman
Keyword(s):  
Black Box ◽  
Box Model ◽  
Bayesian Optimization ◽  
Model Parameters

An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification

2012 ◽  
Vol 9 (6) ◽  
Author(s):  
M. Meiler ◽  
E. P. Hofer ◽  
A. Nuhic ◽  
O. Schmid
Keyword(s):  
Experimental Data ◽  
Fuel Cells ◽  
Fuel Cell ◽  
Nonlinear Systems ◽  
Black Box ◽  
Box Model ◽  
Cathode Side ◽  
Model Parameters ◽  
Efficient Operation ◽  
Wide Range

New technologies for efficient operation of fuel cells require modern techniques in system modeling. Such fuel cell models do not require giving any information about physical mechanisms or internal states of the system. They must be rather precise and should consume less computing time. From the point of view of system theory, polymer electrolyte membrane fuel cells (PEMFC) are multiple input and single output (MISO) systems. The inputs of a fuel cell are the drawn current, the gas pressures at anode and cathode side, and the humidity of these gases which influence the system output, namely the cell voltage, in a nonlinear way. The state of the art in the industry is to describe such nonlinear systems by the usage of lookup tables with a large amount of data. An alternative way to model the input-output behavior of nonlinear systems is the usage of so called black-box and gray-box model approaches. In the last decade, artificial neuronal networks (ANN) became more popular in black-box modeling of nonlinear systems with multiple inputs. Further, if some of the internal processes of a nonlinear system can be mathematically described, a gray-box model is more preferred. In the first part of this paper, the suitability of ANN's in the form of a multilayer perceptron (MLP) network with different numbers of hidden neurons is investigated. A way to confirm the validity for the identified network was worked out. In the second part of this contribution, a gray-box model, valid for a large operating area, based on published semi-empirical models is introduced. Six experimental campaigns for parameter identification and model validation were carried out. The five inputs previously described were varied in a wide range to cover a large operating range. In the last part of this paper, both modeling approaches are investigated with respect to their ability to identify model parameters using a limited number of experimental data.

Simulation Model of the Dynamic Behavior of a Tire Running Over an Obstacle

1988 ◽  
Vol 16 (2) ◽  
pp. 62-77 ◽  
Author(s):  
P. Bandel ◽  
C. Monguzzi
Keyword(s):  
Simulation Model ◽  
Dynamic Behavior ◽  
Black Box ◽  
Box Model ◽  
Vehicle Suspension ◽  
Empirical Relationships ◽  
Static Tests ◽  
High Speeds ◽  
Dynamic Forces ◽  
Vehicle Suspension System

Abstract A “black box” model is described for simulating the dynamic forces transmitted to the vehicle hub by a tire running over an obstacle at high speeds. The tire is reduced to a damped one-degree-of-freedom oscillating system. The five parameters required can be obtained from a test at a given speed. The model input is composed of a series of empirical relationships between the obstacle dimensions and the displacement of the oscillating system. These relationships can be derived from a small number of static tests or by means of static models of the tire itself. The model can constitute the first part of a broader model for description of the tire and vehicle suspension system, as well as indicating the influence of tire parameters on dynamic behavior at low and medium frequencies (0–150 Hz).

scholarly journals MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations

2021 ◽  
Author(s):  
Qing Yang ◽  
Xia Zhu ◽  
Jong-Kae Fwu ◽  
Yun Ye ◽  
Ganmei You ◽  
...  
Keyword(s):  
Black Box ◽  
Box Model

scholarly journals Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

2021 ◽  
Vol 11 (10) ◽  
pp. 4631
Author(s):  
Yu Chen ◽  
Xiaoqing Ji ◽  
Zhongyong Zhao
Keyword(s):  
Equivalent Circuit ◽  
High Frequency ◽  
Equivalent Circuit Model ◽  
Black Box ◽  
Synchronous Machine ◽  
Circuit Model ◽  
Modeling Method ◽  
Experimental Results ◽  
Box Model ◽  
Impedance Curve

The accurate establishment of the equivalent circuit model of the synchronous machine windings’ broadband characteristics is the basis for the study of high-frequency machine problems, such as winding fault diagnosis and electromagnetic interference prediction. Therefore, this paper proposes a modeling method for synchronous machine winding based on broadband characteristics. Firstly, the single-phase high-frequency lumped parameter circuit model of synchronous machine winding is introduced, then the broadband characteristics of the port are analyzed by using the state space model, and then the equivalent circuit parameters are identified by using an optimization algorithm combined with the measured broadband impedance characteristics of port. Finally, experimental verification and comparison experiments are carried out on a 5-kW synchronous machine. The experimental results show that the proposed modeling method identifies the impedance curve of the circuit parameters with a high degree of agreement with the measured impedance curve, which indicates that the modeling method is feasible. In addition, the comparative experimental results show that, compared with the engineering exploratory calculation method, the proposed parameter identification method has stronger adaptability to the measured data and a certain robustness. Compared with the black box model, the parameters of the proposed model have a certain physical meaning, and the agreement with the actual impedance characteristic curve is higher than that of the black box model.

CYBERNETIC MODEL IN PLANNING AND FORECASTING

2020 ◽  
Author(s):  
D.S. Serebryanaya ◽  
Keyword(s):  
Higher Education ◽  
Building Materials ◽  
Black Box ◽  
Box Model ◽  
Sociological Research ◽  
Mathematical Approach ◽  
Cybernetic Model ◽  
Corrective Measures

This article analyzes the mathematical approach to the study of the motives of students to study in higher education. The possibility of using the “black box” model used in the production of building materials for sociological research is considered. This approach allows you to see the most significant causes of discrepancies and develop corrective measures for them.

Investigation of Black Box Model for Erbium-Doped Fiber Amplifiers in Space Radiation Environment

2012 ◽  
Vol 30 (23) ◽  
pp. 3667-3671 ◽  
Author(s):  
Mi Li ◽  
Jing Ma ◽  
Xuping Zhang ◽  
Yuejiang Song ◽  
Wenhe Du
Keyword(s):  
Space Radiation ◽  
Black Box ◽  
Fiber Amplifiers ◽  
Box Model ◽  
Radiation Environment ◽  
Erbium Doped ◽  
Space Radiation Environment

scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Model Development ◽  
Biogeochemical Cycling ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Glacier Forefield ◽  
Unconstrained Model

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

scholarly journals Sparse reduced-order modelling: sensor-based dynamics to full-state estimation

2018 ◽  
Vol 844 ◽  
pp. 459-490 ◽  
Author(s):  
Jean-Christophe Loiseau ◽  
Bernd R. Noack ◽  
Steven L. Brunton
Keyword(s):  
Coherent Structures ◽  
Feature Space ◽  
Black Box ◽  
Box Model ◽  
Sensor Data ◽  
Dynamic Feature ◽  
Time Resolved ◽  
Reduced Order ◽  
Reduced Order Modelling ◽  
Full State

We propose a general dynamic reduced-order modelling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved particle image velocimetry (PIV) snapshots. This framework can be decomposed into four building blocks. First, the sensor signals are lifted to a dynamic feature space without false neighbours. Second, we identify a sparse human-interpretable nonlinear dynamical system for the feature state based on the sparse identification of nonlinear dynamics (SINDy). Third, if PIV snapshots are available, a local linear mapping from the feature state to the velocity field is performed to reconstruct the full state of the system. Fourth, a generalized feature-based modal decomposition identifies coherent structures that are most dynamically correlated with the linear and nonlinear interaction terms in the sparse model, adding interpretability. Steps 1 and 2 define a black-box model. Optional steps 3 and 4 lift the black-box dynamics to a grey-box model in terms of the identified coherent structures, if non-time-resolved full-state data are available. This grey-box modelling strategy is successfully applied to the transient and post-transient laminar cylinder wake, and compares favourably with a proper orthogonal decomposition model. We foresee numerous applications of this highly flexible modelling strategy, including estimation, prediction and control. Moreover, the feature space may be based on intrinsic coordinates, which are unaffected by a key challenge of modal expansion: the slow change of low-dimensional coherent structures with changing geometry and varying parameters.

scholarly journals The effect of seasonal weather changes on the performance of databased models of the thermodynamic behaviour of buildings

2020 ◽  
Vol 172 ◽  
pp. 02005
Author(s):  
Thea Hauge Broholt ◽  
Louise Rævdal Lund Christensen ◽  
Michael Dahl Knudsen ◽  
Rasmus Elbæk Hedegaard ◽  
Steffen Petersen
Keyword(s):  
Residential Buildings ◽  
Model Performance ◽  
Black Box ◽  
Box Model ◽  
Weather Data ◽  
Thermal Mass ◽  
Heating Season ◽  
Satisfactory Model ◽  
Weather Changes ◽  
Seasonal Weather

Several studies have indicated that Model Predictive Control (MPC) of space heating systems can utilize the thermal mass of residential buildings as short-term thermal storage for various demand response purposes. Realization of this potential relies heavily on the accuracy of the model used to represent the thermodynamics of the building. Such models, whether they are grey box or black box, are calibrated using relevant data obtained from initial measurements, and the performance of the calibrated model is validated using data from a subsequent period. However, many studies use validation periods with weather conditions similar to those of the calibration period. Only a few studies investigate whether the calibrated model performs satisfactory when subjected to significantly different conditions. This paper presents data from a simulation-based study on the effect of seasonal weather changes on the performance of a black-box model. The study was conducted using 11 years of Danish weather data (2008-2018). The results indicate that the performance of the black-box model deteriorate as the weather data conditions become increasingly different from those used in the initial model calibration. Further, the results show that calibration in heating season leads to satisfactory model performance through the heating season, but lower performance in transitional seasons (especially spring). Results also show that calibration in February led to highest model performance through heating season, while calibration in March led to satisfactory model performance in the whole heating and fall season.

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