DDDAS Research Articles

Dynamic Data-Driven Application System for Flow Field Prediction with Autonomous Marine Vehicles

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments.

A dynamic data-driven response prediction method for thermal protection tiles and experimental validation

Intelligent thermal protection systems with self-perception and decision-making functions are preferable for future spacecraft. Online thermal response prediction is a key step to intelligent thermal protection systems. In this paper, the concept of Dynamic Data-Driven Application System between simulations and experiments is employed to achieve online thermal response prediction. In this framework, measured temperature data are injected into an online heat transfer model, and the real-time temperature at the bottom of the thermal protection system is calculated. A demonstration test system is built and experiments were carried out to validate the method. The effects of dynamic data and the number of sensors on the temperature prediction accuracy are analyzed. Dynamic data-driven temperature predictions under transient heat load are also carried out. The dynamic data-driven model can reduce the temperature prediction error from 25.9% to less than 10% based on the experimental results, and increasing the number of sensors can also improve the prediction accuracy. The model also has good adaptability under accidental overload and cyclic load. The Dynamic Data-Driven Application System framework presented in this work can be used for online safety assessment of thermal protection systems and active flight control.

Remaining useful life prediction method of EV power battery for DC fast charging condition

To realize the online rapid prediction of the remaining useful life (RUL) of electric vehicle (EV) power battery under direct current (DC) fast charging conditions and reduce the influence of complex health indicator (HI) on the prediction accuracy, an adaptive prediction method of the RUL of EV power battery based on whale swarm algorithm–long short-term memory (WSA-LSTM) algorithm is proposed in this paper. Firstly, a complete set of HI is built based on a dynamic data-driven application system (DDDAS) to ensure the real-time combination of condition monitoring data and simulation systems. Then, combined with the calculation of Pearson and Spearman correlation coefficients and the entropy weight method, the main features that affect the battery capacity are screened out to reduce the interference of secondary HI on RUL prediction. Thirdly, the whale swarm algorithm is used to globally optimize the hyperparameters of the long short-term memory to achieve rapid RUL prediction. Finally, we conduct simulation experiments based on real-time data under EV DC charging conditions. The simulation results show that the HI screening method proposed in this paper is effective, and the RUL prediction method can achieve a fast and accurate prediction of DC fast charging conditions.

A dynamic data driven application system for real-time simulation of resin transfer moulding processes

This paper presents a numerical solution to optimize RTM processes based on a DDDAS methodology, taking permeability as stochastic. The model proposed in this investigation allows real-time accurate prediction of the filling rate by adjusting the parameters with the on-line captured process data. The suitability of the proposed model was tested and validated under different process parameters variations like pressure and preform permeability.

Situational Awareness: Techniques, Challenges, and Prospects

Situational awareness (SA) is defined as the perception of entities in the environment, comprehension of their meaning, and projection of their status in near future. From an Air Force perspective, SA refers to the capability to comprehend and project the current and future disposition of red and blue aircraft and surface threats within an airspace. In this article, we propose a model for SA and dynamic decision-making that incorporates artificial intelligence and dynamic data-driven application systems to adapt measurements and resources in accordance with changing situations. We discuss measurement of SA and the challenges associated with quantification of SA. We then elaborate a plethora of techniques and technologies that help improve SA ranging from different modes of intelligence gathering to artificial intelligence to automated vision systems. We then present different application domains of SA including battlefield, gray zone warfare, military- and air-base, homeland security and defense, and critical infrastructure. Finally, we conclude the article with insights into the future of SA.

Efficient and Robust Online Trajectory Prediction for Non-Cooperative Unmanned Aerial Vehicles

As an important type of dynamic data-driven application system, unmanned aerial vehicles (UAVs) are widely used for civilian, commercial, and military applications across the globe. An increasing research effort has been devoted to trajectory prediction for non-cooperative UAVs to facilitate their collision avoidance and trajectory planning. Existing methods for UAV trajectory prediction typically suffer from two major drawbacks: inadequate uncertainty quantification of the impact of external factors (e.g., wind) and inability to perform online detection of abrupt flying pattern changes. This paper proposes a Gaussian process regression (GPR)-based trajectory prediction framework for UAVs featuring three novel components: 1) GPR with uniform confidence bounds for simultaneous predictive uncertainty quantification, 2) online trajectory change-point detection, and 3) adaptive training data pruning. The paper also demonstrates the superiority of the proposed framework to competing trajectory prediction methods via numerical studies using both simulation and real-world datasets.

Real-time data assimilation and control on mechanical systems under uncertainties

This research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

Connected and Shared X-in-the-Loop Technologies for Electric Vehicle Design

The presented paper introduces a new methodology of experimental testing procedures required by the complex systems of electric vehicles (EV). This methodology is based on real-time connection of test setups and platforms, which may be situated in different geographical locations, belong to various cyber-physical domains, and are united in a global X-in-the-loop (XIL) experimental environment. The proposed concept, called XILforEV, allows exploring interdependencies between various physical processes that can be identified or investigated in the process of EV development. The paper discusses the following relevant topics: global XILforEV architecture; realization of required high-confidence models using dynamic data driven application systems (DDDAS) and multi fidelity models (MFM) approaches; and formulation of case studies to illustrate XILforEV applications.

Fast Multipole Accelerated Unsteady Vortex Lattice Method Based Computations

The authors present an accelerated aerodynamic computational model derived from the integration of the fast multipole method (FMM) with the unsteady vortex lattice method (UVLM) based aerodynamic model. It is determined that the performance of this computational model depends on the tuning of some FMM parameters and that there is a tradeoff between the computational speed and the accuracy of the computed loads. This tradeoff is examined by varying the truncation number, the order of the Gauss–Legendre quadrature, and the clustering parameter for the wake velocity calculations. Results of the computational cost reduction study achieved through the accelerated aerodynamic simulator are reported for a planar, rectangular lifting surface with a high aspect ratio. The computational approach presented in this paper is the first in the literature wherein the FMM has been implemented for an UVLM-based nonlinear, unsteady aerodynamic simulator. The approach has broad applicability for the study of aerodynamic and aeroelastic responses of aircraft systems and related decision support systems in dynamic data-driven application systems.

Field Deployment of a Plume Monitoring UAV Flock

This letter describes and validates the robotic platform of an atmospheric plume monitoring system. The platform consists of a networked flock of unmanned aerial vehicles (UAVs) equipped with environmental sensors. The sensor flock forms an integral component of a dynamic data-driven application system (DDDAS) for plume monitoring. The goal of DDDAS is to dynamically incorporate data into a running simulation while simultaneously using the simulation to steer the measurement process. This letter takes a model-based approach to plume monitoring. From concentration measurements provided by the UAVs, a nonlinear online parameter estimator determines the model parameters. Based on the current knowledge of the model parameters, a hotspot identification routine directs the UAVs to information rich locations, or hotspots. The feasibility of deploying the testbed in uncontrolled outdoor environments is demonstrated with a flight test where three autonomous vehicles trace a simulated plume using simulated sensors.

Exploration of blockchain-enabled decentralized capability-based access control strategy for space situation awareness

Space situation awareness (SSA) includes tracking of active and inactive resident space objects and assessing the space environment through sensor data collection and processing. To enhance SSA, the dynamic data-driven application systems framework couples online data with offline models to enhance performance by using feedback control, sensor management, and communications reliability. For information management, there is a need for identity authentication and access control (AC) to ensure the integrity of exchanged data as well as to grant authorized entities access right to data and services. Due to decentralization and heterogeneity of SSA systems, it is challenging to build an efficient centralized AC system, which can either be a performance bottleneck or the single point of failure. Inspired by the blockchain and smart contract technology, we introduce blockchain-enabled, decentralized, capability-based access control (BlendCAC), a decentralized authentication, and capability-based AC mechanism to enable effective protection for devices, services, and information in SSA networks. To achieve secure identity authentication, the BlendCAC leverages the blockchain to create virtual trust zones, in which distributed components can identify and update each other in a trustless network environment. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry Pi nodes emulating satellites with sensor observations) and more powerful computing devices (i.e., laptops emulating a ground network) and is tested on a private Ethereum blockchain network. The experimental results demonstrate the feasibility of the BlendCAC scheme to offer a decentralized, scalable, lightweight, and fine-grained AC solution for space system toward SSA.

Transit-hub: a smart public transportation decision support system with multi-timescale analytical services

Public transit is a critical component of a smart and connected community. As such, citizens expect and require accurate information about real-time arrival/departures of transportation assets. As transit agencies enable large-scale integration of real-time sensors and support back-end data-driven decision support systems, the dynamic data-driven applications systems (DDDAS) paradigm becomes a promising approach to make the system smarter by providing online model learning and multi-time scale analytics as part of the decision support system that is used in the DDDAS feedback loop. In this paper, we describe a system in use in Nashville and illustrate the analytic methods developed by our team. These methods use both historical as well as real-time streaming data for online bus arrival prediction. The historical data is used to build classifiers that enable us to create expected performance models as well as identify anomalies. These classifiers can be used to provide schedule adjustment feedback to the metro transit authority. We also show how these analytics services can be packaged into modular, distributed and resilient micro-services that can be deployed on both cloud back ends as well as edge computing resources.

Scaling vectors of attoJoule per bit modulators

Electro-optic modulation performs the conversion between the electrical and optical domain with applications in data communication for optical interconnects, but also for novel optical computing algorithms such as providing nonlinearity at the output stage of optical perceptrons in neuromorphic analog optical computing. While resembling an optical transistor, the weak light–matter-interaction makes modulators 105 times larger compared to their electronic counterparts. Since the clock frequency for photonics on-chip has a power-overhead sweet-spot around tens of GHz, ultrafast modulation may only be required in long-distance communication, not for short on-chip links. Hence, the search is open for power-efficient on-chip modulators beyond the solutions offered by foundries to date. Here, we show scaling vectors towards atto-Joule per bit efficient modulators on-chip as well as some experimental demonstrations of novel plasmonic modulators with sub-fJ/bit efficiencies. Our parametric study of placing different actively modulated materials into plasmonic versus photonic optical modes shows that 2D materials overcompensate their miniscule modal overlap by their unity-high index change. Furthermore, we reveal that the metal used in plasmonic-based modulators not only serves as an electrical contact, but also enables low electrical series resistances leading to near-ideal capacitors. We then discuss the first experimental demonstration of a photon-plasmon-hybrid graphene-based electro-absorption modulator on silicon. The device shows a sub-1 V steep switching enabled by near-ideal electrostatics delivering a high 0.05 dB V−1 μm−1 performance requiring only 110 aJ/bit. Improving on this demonstration, we discuss a plasmonic slot-based graphene modulator design, where the polarization of the plasmonic mode aligns with graphene’s in-plane dimension; where a push–pull dual-gating scheme enables 2 dB V−1 μm−1 efficient modulation allowing the device to be just 770 nm short for 3 dB small signal modulation. Lastly, comparing the switching energy of transistors to modulators shows that modulators based on emerging materials and plasmonic-silicon hybrid integration perform on-par relative to their electronic counter parts. This in turn allows for a device-enabled two orders-of-magnitude improvement of electrical-optical co-integrated network-on-chips over electronic-only architectures. The latter opens technological opportunities in cognitive computing, dynamic data-driven applications systems, and optical analog computer engines including neuromorphic photonic computing.

Distributed behavior model orchestration in cognitive internet of things solution

ABSTRACTThe introduction of pervasive and ubiquitous instrumentation within Internet of Things (IoT) leads to unprecedented real-time visibility (instrumentation), optimization and fault-tolerance of the power grid, traffic, transportation, water, oil & gas, to give some examples. Interconnecting those distinct physical, people, and business worlds through ubiquitous instrumentation, even though still in its embryonic stage, has the potential to create intelligent IoT solutions that are much greener, more efficient, comfortable, and safer. An essential new direction to materialize this potential is to develop comprehensive models of such systems dynamically interacting with the instrumentation in a feed-back control loop. We describe here opportunities in applying cognitive computing on interconnected and instrumented worlds (Cognitive Internet of Things–CIoT) and call out the system-of-systems trend among distinct but interdependent worlds, and Dynamic Data-Driven Application System (DDDAS)-based methods for advanced understanding, analysis, and real-time decision support capabilities with the accuracy of full-scale models.

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

Dynamic data driven transportation systems

Congestion-induced delays and pollution in modern transportation systems remain formidable impediments to the sustainable growth of our cities. Next generation Intelligent Transportation Systems (ITS) will attack these problems by relying on extensive in-vehicle sensing, crowd-sourced data, ubiquitous computing, and communications to augment existing infrastructure-based deployments. Advances in wireless networking and mobile computing have made it possible to create dynamic, data driven application systems (DDDAS) that address many challenges in modern transportation systems. We outline a vision for future dynamic data-driven transportation systems, and focus on the effectiveness of an approach to real-time management based on online simulations. The online simulations are embedded in the traffic network where distributed simulators perform the modeling task individually but project the future states collectively. A real-time data driven arterial simulation methodology is proposed to assist such computations that are performed over a testbed in the midtown area of Atlanta, Georgia. Field results are presented that provide evidence to validate the proposed approach.

Static versus Dynamic Data Information Fusion analysis using DDDAS for Cyber Security Trust

Abstract Information fusion includes signals, features, and decision -level analysis over various types of data including imagery, te xt, and cyber security detection. With the maturity of data processing, the e xplosion of big data, and the need fo r user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we e xp lore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic -data driven updates. A use case is provided in determin ing trust for a cyber security scenario exp loring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.

Research on Dust Evolution Simulation System Based on Dynamic Data Driven

The dust evolution simulation provides an important way to analyze the impact of dust on the environment. Dynamic data driven application system framework is applied and dust evolution simulation model based on DDDAS framework is proposed. Compared with the simulation model of dust evolution without DDDAS, the functions of the inputs of real-time data and modification of simulation parameters ensure the improvement of the accuracy of dust evolution simulation. The experimental results show that the dust evolution simulation with DDDAS framework is more precise than the simulation without DDDAS, which proves the effectiveness of the dust evolution simulation system based on dynamic data driven.

A Container-Based Elastic Cloud Architecture for Pseudo Real-Time Exploitation of Wide Area Motion Imagery (WAMI) Stream

Real-time information fusion based on WAMI (Wide-Area Motion Imagery), FMV (Full Motion Video), and text data is highly desired for many mission critical emergency or military applications. However, due to the huge data rate, it is still infeasible to process streaming WAMI in a real-time manner and achieve the goal of online, uninterrupted target tracking. In this paper, a pseudo-real-time Dynamic Data Driven Applications System (DDDAS) WAMI data stream processing scheme is proposed. Taking advantage of the temporal and spatial locality properties, a divide-and-conquer strategy is adopted to overcome the challenge resulting from the large amount of dynamic data. In the Pseudo Real-time Exploitation of Sub-Area (PRESA) framework, each WAMI frame is divided into multiple sub-areas and specified sub-areas are assigned to the virtual machines in a container-based cloud computing architecture, which allows dynamic resource provisioning to meet the performance requirements. A prototype has been implemented and the experimental results validate the effectiveness of our approach.

Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking

Tracking vehicles through dense environments is an important and challenging task that is mostly tackled using visible and near IR wavelengths. Hyperspectral imaging is known to improve the robustness of target identification, but the massive increase in data created is usually prohibitive for tracking many targets. We present a persistent real-time aerial target tracking system, taking advantage of an adaptive, multimodal sensor concept and blending the hyperspectral likelihoods with kinematic likelihoods in a multidimensional assignment framework. The adaptive sensor is capable of providing wide field of view panchromatic images as well as the spectra of small number of pixels. The proposed system does not require large amount of hyperspectral data collection as we focus on tracking fewer number of targets with higher persistency. This overcomes the data challenge of hyperspectral tracking by following dynamic data-driven application systems (DDDAS) principles to control hyperspectral data collection where most beneficial. The DDDAS framework for controlling hyperspectral data collection is developed by incorporating prior information from the filter movement predictions and information from motion detection. The proposed multidimensional hyperspectral feature-aided tracker is compared to a 2-D hyperspectral feature-aided tracker and another cascaded hyperspectral data based tracker by generating a synthetic, realistic, aerial video on a dense scene.