Faculty of Design and Creative Technologies (Te Ara Auaha)

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https://hdl.handle.net/10292/10758
The Faculty of Design and Creative Technologies (Te Ara Auaha) is comprised of four school; Colab, the School of Art and Design, the School of Communication Studies and the School of Engineering, Computer and Mathematical Sciences. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.

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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Subject "0102 Applied Mathematics"

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    Measurement and Enhancing Prediction of EPBM Torque using Actual Machine Data
    (Elsevier BV, 2023-11) Koohsari, Ali; Kalatehjari, Roohollah; Moosazadeh, Sayfoddin; Hajihassani, Mohsen; Tarafrava, Mostafa
    The cutterhead torque of an Earth Pressure Balance Machine (EPBM) plays a critical role in determining the performance of mechanized tunneling in urban areas. However, as this parameter is not directly set by the operator but is a function of geological conditions, thrust force, screw conveyor revolution speed, and soil conditions, it is closely linked to geotechnical parameters and machine settings. Despite previous attempts to predict EPBM torque using Shi's physical model, accuracy has been lacking. This study aims to improve the accuracy of this prediction by utilizing actual data from an EPBM used in a metro line tunneling project to identify the primary factors influencing torque and to modify related equations accordingly. To evaluate the performance of existing models and the predictions made by the presented method, various metrics are utilized, including the correlation between all torque values, the relationship between torque and thrust values, and the connection between thrust pressure and penetration. The results indicate that in addition to geotechnical parameters, machine settings such as thrust force, cutterhead revolution speed, arching pressure, soil conditions, and chamber pressure significantly impact the torque value. The study found that the thrust force exerted by the EPBM is a key factor influencing torque.
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    On the Higher-Order Smallest Ring-Star Network of Chialvo Neurons Under Diffusive Couplings
    (AIP Publishing, 2024-07-18) Nair, Anjana S; Ghosh, Indranil; Fatoyinbo, Hammed O; Muni, Sishu S
    Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark–Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0–1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.
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