DDDAS Research Articles

Estimation of real-time N load in surface water using dynamic data-driven application system

Agricultural, industrial, and urban activities are the major sources for eutrophication of surface water ecosystems. Currently, determination of nutrients in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 h. In other words, little to no effort has been devoted to monitoring real-time variations of nutrients in surface water ecosystems due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health or instantaneous outbreaks such as algal blooms, timely water-quality information is very critical. In this study, we developed a new paradigm of a dynamic data-driven application system (DDDAS) for estimating the real-time loads of nitrogen (N) in a surface water ecosystem. This DDDAS consisted of the following components: (1) a Visual Basic (VB) program for downloading US Geological Survey real-time chlorophyll and discharge data from the internet; (2) a STELLA model for evaluating real-time N loads based on the relationship between chlorophyll and N as well as on river discharge; (3) a batch file for linking the VB program and STELLA model; and (4) a Microsoft Windows Scheduled Task wizard for executing the model and displaying outputs on a computer screen at selected schedules. The DDDAS was validated using field measurements with a very good agreement prior to its applications. Results show that the real-time loads of TN (total N) and NO x (nitrate and nitrite) varied from positive to negative with the maximums of 1727 kg/h TN and 118 kg/h NO x and the minimums of −2483 kg/h TN and −168 kg/h NO x at the selected site. The negative loads occurred because of the back flow of the river in the estuarine environment. Our study suggests that the DDDAS developed in this study was feasible for estimating the real-time variations of TN and NO x in the surface water ecosystem.

Prediction Time Assessment in a DDDAS for Natural Hazard Management: Forest Fire Study CaseI

Abstract This work faces the problem of quality and prediction time assessment in a Dynamic Data Driven Application System (DDDAS) for predicting natural hazard evolution. In particular, we used forest fire spread prediction as acase study to show the applicability of the methodology. The improvement on the prediction quality when using a two-stage DDDAS prediction framework has been widely proved. The two-stages DDDAS has a first phase where an adjustment of the input data is performed in order to be applied in the second phase, the prediction. This paper is focused on defining a new methodology for prediction time assessment under this kind of prediction environments by evaluating, in advance, how a certain combination of simulator, computational resources, adjustment strategy, and frequency of data acquisition will perform, in terms of prediction time. Since the time incurred in the hazard simulation is a crucial part of the whole prediction time, we have defined a methodology to classify the simulator's execution time using Artificial Intelligence techniques allowing us to determine upper bounds for the DDDAS prediction time depending on the particular input parameter setting. This methodology can be extrapolated to any DDDAS for predicting natural hazards evolution, which uses the two-stage prediction scheme as a working framework.

Memristor as an archetype of dynamic data-driven systems and applications to sensor networks

Since its introduction a decade ago, DDDAS has been applied to a wide range of science and technology fields, with specific focus of areas requiring fast and reliable processing of massive data streams from diverse resources. It is crucial to explore architectures and systems which are naturally suited to the DDDAS framework. In this paper, we show that the memristor – the fourth fundamental two-terminal passive circuit element alongside the well-known resistor, capacitor, and inductor – affords the efficient implementation of the working principles of DDDAS. Hence memristors can be considered as an archetype of DDDAS nanoscale hardware embodiment, being the smallest and most basic dynamic data driven application system. Memristors are electrical components with inherent memory processes; they have been predicted about four decade ago and have been physically implemented recently. We discuss the role that DDDAS may play in the development of computing platforms and sensor networks based on memristors in the next few years.

Towards policies for data insertion in dynamic data driven application systems: a case study sudden changes in wildland fire

Abstract We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire propagation. Our goal is to build a system that dynamically adapts to sudden changes in environmental conditions. For this purpose, we are building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. This data-injection needs to be intelligent in order noy to disturb the simulation process outputs. In this paper, we propose a policy for data insertion using a statistical approach and we design a set of experiments based on California wildfire where Santa Ana winds generate the ideal conditions for sudden changes in fire behavior.

CyberInfrastructures of cyber-applications-systems

Abstract The paper discusses foundational notions and conditions in complex applications modeling and in software frameworks to support advanced CyberInfrastructure environments such as those implied and required for Dynamic Data Driven Applications Systems, as well as other classes of applications, collectively referred to here as Cyber-Applications-Systems. The paper discusses considerations of dynamic invocation of multi-scale models, uncertainty quantification, uncertainty propagation, and dynamic runtime systems support on heterogeneous, distributed, end-to-end dynamically integrated high-end, real-time and instrumentation and control systems.

Conceptual framework for dynamic trust monitoring and prediction

The dynamic and collaborative nature of mobile and sensor networks raises the issue of how connected mobile devices can be trusted. Despite the existing security paradigms such as cryptographic mechanisms, and reputation and trust models, the assurance of security remains a problem of such environments. These networks have been plagued with internal security issues such as the presence of untrusted nodes that misbehave. Depending on the proportion of misbehaving nodes and their strategies, attacks such as collusions may occur. By covering up malicious behaviour of one another from the remaining part of the network, two or more malicious nodes may collaborate to cause damage to or disrupt the network. The concept of the Dynamic Data-Driven Application Systems paradigm has been suggested for use in diverse fields. This paper proposes a novel framework that utilises the paradigm in the area of reputation and trust-based systems in mobile networks. The proposed framework is critically evaluated and compared with existing work. This framework, which is applicable to social and behavioural modelling in networks, has the advantage of monitoring and predicting the future trustworthiness of members and highlighting malicious regions within the network.

Prospect of Dynamic Data Driven Application System in agricultural and environmental applications

At present,most input data in system dynamic modeling are not able to synchronize with real-world conditions,which often results in inaccurate predictions and/or failure of system control.Dynamic Data Driven Application System(DDDAS) is an innovative paradigm in which real-time computer simulations and measurements,system automated feedback and applications/managements are dynamically integrated.It effectively overcomes setbacks in traditional simulation approaches.This paper is an overview of the emergence and basic concepts of DDDAS and describes the rationale and methodology of DDDAS application in agricultural and environmental sciences through case studies of greenhouse gas emission,irrigation control in crop production and monitoring water pollution in river system.Specific limitations of DDDAS are also discussed.

Application of data-driven design optimization methodology to a multi-objective design optimization problem

The data-driven design optimization methodology (DDDOM) is an application of the dynamic data-driven application system concept in the engineering design domain. The DDDOM combines experiments and simulations concurrently and tends to achieve better designs in less time with less effort than traditional methods. This paper presents the application of the DDDOM to a multi-objective design optimization problem—design of a cooling system for electronic elements. In the DDDOM approach, both simulation and experimental data are combined to generate good surrogate models for the objective functions. The ϵ-constraint optimization method is then performed on the surrogate models and the Pareto set is found. The presented approach provides a new methodology for design optimization.

An automatic statistical segmentation algorithm for extraction of fire and smoke regions

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic Data Driven Applications Systems (DDDAS) concepts that use data assimilation techniques which require error estimates or probabilities for the data parameters. The Hidden Markov Random Field (HMRF) model has been used widely in image segmentation, but it is assumed that each pixel has a particular class label belonging to a prescribed finite set. The mixed pixel problem can be addressed by modeling the fuzzy membership process as a continuous Multivariate Gaussian Markov Random Field. Techniques for estimating the class membership and model parameters are discussed. Experimental results obtained by applying this technique to two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images show that the proposed methodology is robust with regard to noise and variation in fire characteristics as well as background. The segmentation results of our algorithm are compared with the results of a K-means algorithm, an Expectation Maximization (EM) algorithm (which is very similar to the Fuzzy C-Means Clustering algorithm with entropy regularization), and an MRF-MAP algorithm. Our fuzzy algorithm achieves more consistent segmentation results than the comparison algorithms for these test images with the added advantage of simultaneously providing a proportion or error map needed for the data assimilation problem.

Automated extraction of fire line parameters from multispectral infrared images

Remotely sensed infrared images are often used to assess wildland fire conditions. Separately, fire propagation models are in use to forecast future conditions. In the Dynamic Data-Driven Application System (DDDAS) concept, the fire propagation model will react to the image data, which should produce more accurate predictions of fire propagation. In this study we describe a series of image processing tools that can be used to extract fire propagation parameters from multispectral infrared images so that the parameters can be used to drive a fire propagation model built upon the DDDAS concept. The method is capable of automatically determining the fire perimeter, active fire line, and fire propagation direction. A multi-band image gradient calculation, the Normalized Difference Vegetation Index, and the Normalized Difference Burn Ratio along with several standard image processing techniques are used to identify and constrain the fire propagation parameters. These fire propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment.

Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems

The advent of Grid computing has enhanced our capabilities to model and simulate complex systems arising in scientific, engineering, and commercial applications. The premise of Grid computing has been on-demand availability of computational resources to an application as needed, in the same manner as electricity is provided readily through electrical power grids. The computational grid (or simply the Grid) entails ubiquitous access to resources (local or remote), such as computation and communication resources, as well as access to storage systems and visualization systems. As Grid computing technologies mature, it behooves to look beyond the current capabilities, into more advanced future environments. The environments of interest here are the enhanced capabilities that can be created by the paradigm of dynamic data driven applications systems (DDDAS). DDDAS entails the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. The DDDAS concept offers the promise of improving application models and methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. Enabling this synergistic feedback and control loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms stable under dynamic data injection and steering conditions, and new systems software and computational infrastructure capabilities. Recent advances in complex applications and the advent of Grid computing and sensor systems are some of the technologies that make it timely to embark in developing DDDAS capabilities. DDDAS environments extend the current notion of Grid infrastructure to also include the measurement systems in an integrated and synergistic way. DDDAS environments require support and services that go beyond the current Grid services in terms of t