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1. Data-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process

   https://doi.org/10.1115/1.4048628  

   Wang, Liwei ; Tao, Siyu ; Zhu, Ping ; Chen, Wei ( March 2021 , Journal of Mechanical Design)

   null (Ed.)

   Abstract The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures. 

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2. In Silico Finite Element Analysis of the Foot Ankle Complex Biomechanics: A Literature Review

   https://doi.org/10.1115/1.4050667  

   Phan, P. K. ; Vo, A. T. ; Bakhtiarydavijani, A. ; Burch, R. ; Smith, B. ; Ball, J. E. ; Chander, H. ; Knight, A. ; Prabhu, R. K. ( September 2021 , Journal of Biomechanical Engineering)

   null (Ed.)

   Abstract Computational approaches, especially finite element analysis (FEA), have been rapidly growing in both academia and industry during the last few decades. FEA serves as a powerful and efficient approach for simulating real-life experiments, including industrial product development, machine design, and biomedical research, particularly in biomechanics and biomaterials. Accordingly, FEA has been a “go-to” high biofidelic software tool to simulate and quantify the biomechanics of the foot–ankle complex, as well as to predict the risk of foot and ankle injuries, which are one of the most common musculoskeletal injuries among physically active individuals. This paper provides a review of the in silico FEA of the foot–ankle complex. First, a brief history of computational modeling methods and finite element (FE) simulations for foot–ankle models is introduced. Second, a general approach to build an FE foot and ankle model is presented, including a detailed procedure to accurately construct, calibrate, verify, and validate an FE model in its appropriate simulation environment. Third, current applications, as well as future improvements of the foot and ankle FE models, especially in the biomedical field, are discussed. Finally, a conclusion is made on the efficiency and development of FEA as a computational approach in investigating the biomechanics of the foot–ankle complex. Overall, this review integrates insightful information for biomedical engineers, medical professionals, and researchers to conduct more accurate research on the foot–ankle FE models in the future. 

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3. Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

   https://doi.org/10.3390/g14020030  

   Niu, Luyao ; Ramasubramanian, Bhaskar ; Clark, Andrew ; Poovendran, Radha ( April 2023 , Games)

   This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex time-sensitive tasks in the presence of an adversary. The time-sensitive task is specified as a formula in the metric interval temporal logic (MITL). CPSs that operate in adversarial environments have typically been abstracted as stochastic games (SGs); however, because traditional SG models do not incorporate a notion of time, they cannot be used in a setting where the objective is time-sensitive. To address this, we introduce durational stochastic games (DSGs). DSGs generalize SGs to incorporate a notion of time and model the adversary’s abilities to tamper with the control input (actuator attack) and manipulate the timing information that is perceived by the CPS (timing attack). We define notions of spatial, temporal, and spatio-temporal robustness to quantify the amounts by which system trajectories under the synthesized policy can be perturbed in space and time without affecting satisfaction of the MITL objective. In the case of an actuator attack, we design computational procedures to synthesize controllers that will satisfy the MITL task along with a guarantee of its robustness. In the presence of a timing attack, we relax the robustness constraint to develop a value iteration-based procedure to compute the CPS policy as a finite-state controller to maximize the probability of satisfying the MITL task. A numerical evaluation of our approach is presented on a signalized traffic network to illustrate our results.

    

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4. Board 92: Using Mixed Reality and the Three Apprenticeships Framework to Design Head-, Hand- and Heart-focused Learning Experiences for Civil Engineering Students

   London, J. S. ; Ayer, S. K. ; Wu, W. ; Lam, C. K. ( June 2019 , 2019 ASEE Annual Conference & Exposition)

   The building industry has a major impact on the US economy and accounts for: $1 trillion in annual spending; 40% of the nation’s primary energy use; and 9 million jobs. Despite its massive impact, the industry has been criticized for poor productivity compared with other industries and billions of dollars in annual waste because of poor interoperability. Furthermore, the industry has been approaching a “labor cliff”: there are not enough new individuals entering the industry to offset the vacancies left by an aging, retiring workforce. To remain effective, this critical industry will need to do better with less. In order to prepare civil engineering students for careers in this industry, educators have aimed to replicate the processes associated with real-world projects through design/build educational activities like the Department of Energy’s (DOE) Solar Decathlon, Sacramento Municipal Utility District’s (SMUD) Tiny House Competition, and DOE’s Challenge Home Competition. These learning experiences help situate civil engineering concepts in an authentic learning environment. Unfortunately, not all universities have the financial resources necessary to fund this type of hands-on project. Technology has the potential to mitigate some of these inequities. Thus, the multi-faceted objective of this project is to: develop mixed reality (MR) technology aimed at sufficiently replicating physical design and construction learning environments to enable access to students at institutions without sufficient resources; and assess the impact of a MR-facilitated cyberlearning environment on promoting cognitive-, affective-, and skill-based learning that occurs during traditional (in-persona) design and construction activities. This research will explore a fundamental question: Can MR technology enable educators to simulate physical design and construction activities at low costs to enable students at all institutions to gain exposure to these types of hands-on learning environments? In order to address this question, we employ an iterative development approach according to Human Centered Design principles to support learning according to the Carnegie Foundation’s Three Apprenticeships Model (i.e., learning related to “Head”, “Hand”, and “Heart”). In order to achieve these aims, the research team uses MR technology (i.e., a Microsoft HoloLens®) to understand the extent to which this mode of education allows students to demonstrate knowledge similar to that which is gained through physical design and construction learning environments. This paper will presents highlights from the first year of this project. 

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5. Probabilistic forecasting of plausible debris flows from Nevado de Colima (Mexico) using data from the Atenquique debris flow, 1955

   https://doi.org/10.5194/nhess-19-791-2019  

   Bevilacqua, Andrea ; Patra, Abani K. ; Bursik, Marcus I. ; Pitman, E. Bruce ; Macías, José Luis ; Saucedo, Ricardo ; Hyman, David ( January 2019 , Natural Hazards and Earth System Sciences)

   Abstract. We detail a new prediction-oriented procedure aimed at volcanic hazardassessment based on geophysical mass flow models constrained withheterogeneous and poorly defined data. Our method relies on an itemizedapplication of the empirical falsification principle over an arbitrarily wideenvelope of possible input conditions. We thus provide a first step towards aobjective and partially automated experimental design construction. Inparticular, instead of fully calibrating model inputs on past observations,we create and explore more general requirements of consistency, and then weseparately use each piece of empirical data to remove those input values thatare not compatible with it. Hence, partial solutions are defined to the inverseproblem. This has several advantages compared to a traditionally posedinverse problem: (i) the potentially nonempty inverse images of partialsolutions of multiple possible forward models characterize the solutions tothe inverse problem; (ii) the partial solutions can provide hazard estimatesunder weaker constraints, potentially including extreme cases that areimportant for hazard analysis; (iii) if multiple models are applicable,specific performance scores against each piece of empirical information canbe calculated. We apply our procedure to the case study of the Atenquiquevolcaniclastic debris flow, which occurred on the flanks of Nevado de Colimavolcano (Mexico), 1955. We adopt and compare three depth-averaged modelscurrently implemented in the TITAN2D solver, available from https://vhub.org(Version 4.0.0 – last access: 23 June 2016). The associated inverse problemis not well-posed if approached in a traditional way. We show that our procedurecan extract valuable information for hazard assessment, allowing the explorationof the impact of synthetic flows that are similar to those that occurred in thepast but different in plausible ways. The implementation of multiple models isthus a crucial aspect of our approach, as they can allow the covering of otherplausible flows. We also observe that model selection is inherently linked tothe inversion problem.

    

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