Online Data-Driven Prediction of Spatio-Temporal System Behavior Using High-Fidelity Simulations and Sparse Sensor Measurements

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
X. Zhao ◽  
S. Azarm ◽  
B. Balachandran
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Measurement Data ◽  
Data Driven ◽  
High Fidelity ◽  
Simulation Data ◽  
Online Data ◽  
Temporal Prediction ◽  
Spatio Temporal

Abstract Predicting the behavior or response for complicated dynamical systems during their operation may require high-fidelity and computationally costly simulations. Because of the high computational cost, such simulations are generally done offline. The offline simulation data can then be combined with sensors measurement data for online, operational prediction of the system's behavior. In this paper, a generic online data-driven approach is proposed for the prediction of spatio-temporal behavior of dynamical systems using their simulation data combined with sparse, noisy sensors measurement data. The approach relies on an offline–online approach and is based on an integration of dimension reduction, surrogate modeling, and data assimilation techniques. A step-by-step application of the proposed approach is demonstrated by a simple numerical example. The performance of the approach is also evaluated by a case study which involves predicting aeroelastic response of a joined-wing aircraft in which sensors are sparsely placed on its wing. Through this case study, it is shown that the results obtained from the proposed spatio-temporal prediction technique have comparable accuracy to those from the high-fidelity simulation, while at the same time significant reduction in computational expense is achieved. It is also shown that, for the case study, the proposed approach has a prediction accuracy that is relatively robust to the sensors’ locations.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Haoran Zhai ◽  
Jiaqi Yao ◽  
Guanghui Wang ◽  
Xinming Tang
Keyword(s):  
Particulate Matter ◽  
Measurement Data ◽  
Atmospheric Particulate Matter ◽  
Temporal Characteristics ◽  
Double Peaks ◽  
Spatio Temporal ◽  
Pm2.5 And Pm10 ◽  
Air Quality Monitoring Stations ◽  
Matter Concentration

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

scholarly journals Multidimensional Approximation of Nonlinear Dynamical Systems

2019 ◽  
Vol 14 (6) ◽  
Author(s):  
Patrick Gelß ◽  
Stefan Klus ◽  
Jens Eisert ◽  
Christof Schütte
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Measurement Data ◽  
Data Driven ◽  
High Dimensional ◽  
Data Sets ◽  
Nonlinear Dynamical ◽  
Tensor Network ◽  
Wide Range ◽  
Multidimensional Approximation

A key task in the field of modeling and analyzing nonlinear dynamical systems is the recovery of unknown governing equations from measurement data only. There is a wide range of application areas for this important instance of system identification, ranging from industrial engineering and acoustic signal processing to stock market models. In order to find appropriate representations of underlying dynamical systems, various data-driven methods have been proposed by different communities. However, if the given data sets are high-dimensional, then these methods typically suffer from the curse of dimensionality. To significantly reduce the computational costs and storage consumption, we propose the method multidimensional approximation of nonlinear dynamical systems (MANDy) which combines data-driven methods with tensor network decompositions. The efficiency of the introduced approach will be illustrated with the aid of several high-dimensional nonlinear dynamical systems.

Variable Fidelity Modeling in Closed Loop Dynamical Systems

2014 ◽  
Author(s):  
Matthew A. Williams ◽  
Andrew G. Alleyne
Keyword(s):  
Dynamical Systems ◽  
Closed Loop ◽  
System Development ◽  
Computational Cost ◽  
Computational Effort ◽  
High Fidelity ◽  
Vapor Compression System ◽  
Variable Fidelity ◽  
Time Required ◽  
Fidelity Model

In the early stages of control system development, designers often require multiple iterations for purposes of validating control designs in simulation. This has the potential to make high fidelity models undesirable due to increased computational complexity and time required for simulation. As a solution, lower fidelity or simplified models are used for initial designs before controllers are tested on higher fidelity models. In the event that unmodeled dynamics cause the controller to fail when applied on a higher fidelity model, an iterative approach involving designing and validating a controller’s performance may be required. In this paper, a switched-fidelity modeling formulation for closed loop dynamical systems is proposed to reduce computational effort while maintaining elevated accuracy levels of system outputs and control inputs. The effects on computational effort and accuracy are investigated by applying the formulation to a traditional vapor compression system with high and low fidelity models of the evaporator and condenser. This sample case showed the ability of the switched fidelity framework to closely match the outputs and inputs of the high fidelity model while decreasing computational cost by 32% from the high fidelity model. For contrast, the low fidelity model decreases computational cost by 48% relative to the high fidelity model.

scholarly journals Study of Thermal Comfort in Traditional House Buildings (Case Study: Simalungun Traditional House)

2021 ◽  
Vol 5 (1) ◽  
pp. 90-99
Author(s):  
Vinky Rahman ◽  
Luqman Hadi Wibowo
Keyword(s):  
Thermal Comfort ◽  
Measurement Data ◽  
Thermal Conditions ◽  
Research Problem ◽  
Simulation Data ◽  
Positive Potential ◽  
Traditional Architecture ◽  
Long Period ◽  
Surrounding Environment

Abstract. Traditional houses were formed over a long period and are believed to be hereditary responsive to the surrounding physical and socio-cultural environment. Traditional Architecture is a building whose shape, decoration and method of implementation are passed down from generation to generation. Traditional architecture is a reflection of the values and culture that the community has interpreted. The adaptation of residents in the house is carried out by optimizing the positive potential of the surrounding environment and minimizing disturbances related to the comfort of living. The research problem is how the level of thermal comfort in the traditional house of Simalungun. The purpose of this study is to analyze the thermal comfort of the study object of the Simalungun traditional house. To determine the thermal conditions inside and outside the building, Measurements of temperature and humidity were carried out. Measurement data were analyzed and compared with Ecotech simulation data. The results of research carried out directly and simulating using Ecotech. This Simalungun traditional house can be categorized as having optimal comfort in terms of its physical physiological aspects and simulate using Ecotech simulations. As for the benefits of the research, it is hoped that it can provide knowledge about the thermal comfort of traditional houses, especially the traditional houses of Simalungun.

scholarly journals Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality

2021 ◽  
Author(s):  
Namyong Park ◽  
MinHyeok Kim ◽  
Xuan Hoai Nguyen ◽  
Robert McKay ◽  
Dong-Kyun Kim
Keyword(s):  
Water Quality ◽  
River Water ◽  
Prior Knowledge ◽  
Dynamic Systems ◽  
Genetic Model ◽  
Computational Cost ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Data Driven

Modeling real-world phenomena is a focus of many science and engineering efforts, from ecological modeling to financial forecasting. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Knowledge-driven modeling builds a model based on human expertise, yet is often suboptimal. At the opposite extreme, data-driven modeling learns a model directly from data, requiring extensive data and potentially generating overfitting. We focus on an intermediate approach, model revision, in which prior knowledge and data are combined to achieve the best of both worlds. We propose a genetic model revision framework based on tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG formalism and GP operators in an effective mechanism making data-driven revisions while incorporating prior knowledge. Our framework is designed to address the high computational cost of evolutionary modeling of complex systems. Via a case study on the challenging problem of river water quality modeling, we show that the framework efficiently learns an interpretable model, with higher modeling accuracy than existing methods.

scholarly journals Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 5135-5148 ◽  
Author(s):  
Teng Zhao ◽  
Ziqiang Zhou ◽  
Yan Zhang ◽  
Ping Ling ◽  
Yingjie Tian
Keyword(s):  
Temporal Analysis ◽  
Data Driven ◽  
Pv Systems ◽  
Data Driven Approach ◽  
Spatio Temporal
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