Research paper accepted by Structural and Multidisciplinary Optimization
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.
Research paper accepted by Reliability Engineering and Systems Safety
In this paper, we develop a generic physics-informed neural network (PINN)-based framework to assess the reliability of multi-state systems (MSSs). The proposed framework follows a two-step procedure. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups is constructed to encode the initial condition and the state transitions governed by ordinary differential equations in MSS, respectively. Next, we tackle the problem of high imbalance in the magnitudes of back-propagated gradients from a multi-task learning perspective and establish a continuous latent function for system reliability assessment. Particularly, we regard each element of the loss function as an individual learning task and project a task’s gradient onto the norm plane of any other task with a conflicting gradient by taking the projecting conflicting gradients (PCGrad) method. We demonstrate the applications of the proposed framework for MSS reliability assessment in a variety of scenarios, including time-independent or dependent state transitions, where system scales increase from small to medium. The computational results indicate that PINN-based framework reveals a promising performance in MSS reliability assessment and incorporation of PCGrad into PINN substantially improves the solution quality and convergence speed of the algorithm.
Research paper accepted by Transportation Research Part C
Collisions during airport surface operations can create risk of injury to passengers, crew or airport personnel and damage to aircraft and ground equipment. A machine learning model that is able to predict the trajectories of ground objects can help to diminish the occurrences of such collision events. In this paper, we pursue this objective by building a spatial-temporal graph convolutional neural network (STG-CNN) model to predict the movement of objects/vehicles on the airport surface. The methodology adopted in this paper consists of three steps: (1) Raw data processing: leverage Apache Spark to parse a large volume of raw data in Flight Information Exchange Model (FIXM) format streamed from the Surface Movement Event Service (SMES) for the purpose of deriving historical trajectory associated with each object on the ground; (2.1) Graph-based representations of ground object movements: build graph-based representations to characterize the movements of ground objects over time, where graph edges are used capture the spatial relationships of ground objects with each other explicitly; (2.2) Trajectory forecasts of all ground objects: combine STG-CNN with Time-Extrapolator Convolution Neural Network (TXP-CNN) to forecast the future trajectories of all the ground objects as a whole; and (3) Separation distance-based safety assessment: define a probabilistic separation distance-based metric to assess the safety of airport surface movements. The performance of the developed model for trajectory prediction of ground objects is validated at two airports with varying scales: Hartsfield-Jackson Atlanta International Airport and LaGuardia airport, under two different scenarios (peak hour and off-peak hour). Two quantitative performance metrics — Average Displacement Error (ADE) and Final Displacement Error (FDE) are used to compare the prediction performance of the proposed model with an alternative method. The computational results indicate that the developed method has an ADE within the range [7.55, 9.33], and it significantly outperforms an alternative approach that combines a STG-CNN with Convolutional Long Short-Term Memory (ConvLSTM) neural network with an ADE of [15.79, 16.89] in airport surface movement prediction, thus facilitating more accurate safety assessment during airport surface operations.
