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In the background of the significant data era, green power represents the future development direction of ships. It is critical to establish fast-response marine power models to evaluate various green power-driven power components. However, when evaluating a finite model, the calculating often requires re-meshing of the model once the model parameters change, resulting in huge amount computing costs. The reduced-order model can address this issue by rapid calculation of the model responses; however, real-time computing of the reduced-order model is still a challenging task. In this paper, we study the basic principle of the digital twin reduced-order model and propose a digital twin reduced-order model. Firstly, the geometric model of a marine centrifugal pump is imported into ICEM meshing software for calculation domain meshing; Secondly, import the mesh file generated by ICEM software into Fluent for CFD numerical simulation and numerical simulation of the reduced-order model; finally, the reduced-order model is verified: under the rated working condition, the calculated head of reduced-order model is 31.98 m, the maximum speed of impeller edge is 24.4 m/s, and the minimum speed at the impeller shaft center is 0.0475 m/s. The errors are less than 1% compared to the theoretically calculated values. This study provides a proof basis for the high-accuracy model required by the fault detection technique based on the reduced-order model, and also proves that the fault detection method based on the reduced-order model is feasible. [ABSTRACT FROM AUTHOR]

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Aitia's Gemini Digital Twins leverage patient data, AI and simulation to identify molecules and the patients they are most likely to be successful in. [ABSTRACT FROM AUTHOR]

Lumen's inventory overhaul is part of the company's plan to create $1 billion in cost takeout by the end of 2027. [ABSTRACT FROM AUTHOR]

The internet as we know it is old and worn out, as are the ways that we think about it. [ABSTRACT FROM AUTHOR]

At Sensors Converge in Santa Clara, California, the Best of Sensors 2024 awards were presented on June 26th to recognize outstanding achievements in sensor technologies and the broader e [ABSTRACT FROM AUTHOR]

Health insurance executives are captivated by artificial intelligences' use cases. Here's where they see advancements on the horizon. [ABSTRACT FROM AUTHOR]

In this week's careers newsletter: tips on how to communicate with your boss when you've been disrespected, and how employees at Google are asking the tech giant to divest their 401(k)s from fossil fuels. [ABSTRACT FROM AUTHOR]

While in the past digital human models were dominated by I Realism i , a characteristic which expresses that the human model physically resembles a human but actually behaves non humanlike, at the Ergolab we believe that future DHM must embrace I Likeness i : this means that a potentially strange creature behaves like a human in motion, body language and expressions. The TC organizes an annual DHM symposium as a marketplace for basic and applied DHM research and promotes commercial DHM solutions and related technologies. The future will also overcome the human-manikin and manikin-manikin variability in simulation results. [Extracted from the article]

One can compute the final deformation of a known geometry under specific boundary conditions using the constitutive laws of mechanics that describe their stress strain behavior. In such cases the initial geometry is known, and all operators mapping the deformation are defined on the reference domain. However, there are situations in which the final configuration of a deformation might be known but not the initial. The inverse formulation allows one to determine the initial geometry of a domain, given its final deformation state, the material behavior law and a set of boundary conditions. In the present work we propose a method to reconstruct the mesoscale geometry of a textile based on its mechanical response during compaction. To do so, stress boundary conditions are acquired by means of a pressure-sensitive film. By adopting an appropriate material law, the thickness and width information of the yarns are deduced from the pressure field experienced by the compacted textile. Unlike 3D scanning techniques such as μ -CT, the proposed method can be applied on any domain size, allowing long-range variability to be captured. To the best of the authors' knowledge, there are no previous works that use a pressure-sensitive film on a large domain to capture the input data for a shape reconstruction. This example application serves as a demonstration of a methodology which could be applied to other classes of materials. [ABSTRACT FROM AUTHOR]

Echoing a previous $50 million fundraising, the new proceeds bring Unlearn's lifetime total to more than $130 million. It will be used to build out its staff, computing power and long-term R&D projects. [ABSTRACT FROM AUTHOR]

On the heels of new research affirming its technology's ability to help treat Type 2 diabetes, Twin Health has raked in another round of funding. [ABSTRACT FROM AUTHOR]

• The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. • The world climate institution has reported that the carbon emission from transportation system is one-fifth of global carbon dioxide with a sum of around 24% of energy. The communication between EV and Roadside Unit (RSU), and the continuous computational power required to support IoV system is also energy harvesting. • In this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon print and offload the computation power required. • Our solution resides in developing a Digital Twin of the vehicle on an intelligent edge environment to simulate the physical vehicle and process the required data processing. The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. With the fast and exponential growing rate of Internet of Things (IoT), the smart transportation field is ushering in a revolutionary advancement. Smart transportation systems facilitate better informed, more coordinated, and smarter use of transport networks, with the use of advanced information and communication technologies applied to vehicles to help improve traffic management, minimize congestion, improve safety, and ultimately provide a more intelligent use of transport networks. Smart transportation is an integral part of modern-day smart city projects. However, the world climate institution has reported that carbon emissions from the overall transportation system accounts for one-fifth of global carbon dioxide with a sum of around 24% of energy. Electric vehicles (EV) represent a solution for this issue, yet, it is not sustainable. The communication between EVs and a roadside unit (RSU), and the continuous computational power required to support an IoV system also requires a reliance on energy harvesting. With this in mind, in this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon footprint and offload the computation power required. Our solution resides in developing the digital twin of vehicles on an intelligent edge environment to simulate the physical vehicle and handle the required data processing. Also, we implement a smart contract model for fast, secure, and sustainable on-road battery recharging between EVs. [ABSTRACT FROM AUTHOR]

Charles River Labs will let Aitia access its AI drug solution platform to accelerate therapy programs in neurodegenerative diseases and oncology. [ABSTRACT FROM AUTHOR]

The public has regarded Edge-AI enabled Industrial Internet of Things (IIoT) as the crucial foundation in the intelligent digital factories in Industry 4.0. It can fully catch the massive production data derived from the complex production process, and provide efficient, intelligent services. However, the deployment of edge AI aggravates the complexity and security risks caused by the massive heterogeneous resource-constrained and vulnerable edge IIoT devices. Effective fault prevention is crucial to ensure the security and robustness of the IIoT with numerous vulnerable edge devices. Most existing solutions are based on the history log, which can hardly defend against attacks and is easy to cause excessive maintenance. To address this issue, we propose a trustworthy self-healing scheme based on the combination of distributed digital twin (DT) and blockchain, to ensure the security and robustness of the industrial system network. We first propose an implementation architecture of the distributed DT based self-healing IIoT to apply the distributed DT simulation capability fully. In addition, we provide a DT simulation operating mechanism for the controlled industrial devices, considering the requirement of users and constrained resources of edge servers. Moreover, this work proposes a blockchain-based decentralized trust management mechanism to ensure the reliability of self-healing. The security analysis and performance evaluation show the security and efficiency of our proposal. • A trustworthy self-healing scheme based on digital twin and blockchain effectively secures Industrial Internet of Things. • Distributed digital twin model is proposed to support many-to-many parallel simulations for industrial physical entities. • The blockchain-based trust sharing that supports simulation selection and self-healing process is proposed and evaluated. • The performance, security, and reliability of the trustworthy self-healing process are evaluated. [ABSTRACT FROM AUTHOR]

We explore the role of statistics for Big Data analysis arising from the emerging field of Data-Centric Engineering. Using examples related to sensor-instrumented bridges, we highlight a number of issues and challenges. These are broadly categorised as relating to uncertainty, latent-structure modelling, and the synthesis of statistical models and abstract physical models. [ABSTRACT FROM AUTHOR]

Walsin Lihwa corp., dedicated to the development of robot cables and smart cables, proposes a test method that combines the actual physical movement and digital multiple-axial model to find a suitable robot cable's material and structure. This method can assist the customer in selecting the right cable; simultaneously it also can help the manufacturer compress the overall time required for cable testing to an acceptable range (4 months or less). This research uses the USB 3.0 cable fixed on the "6-axis robot" as the desirable test subject. With real testing equipment and digital twin, we can verify whether this test model is referential to the actual robot cable's service life. [ABSTRACT FROM AUTHOR]

Experts at Fiber Connect convened this week to address the need for new approaches to fiber deployments in challenging environments. [ABSTRACT FROM AUTHOR]

Consumers continue to set a high bar on the advanced features they expect to align with their non-stop lifestyles. [ABSTRACT FROM AUTHOR]

Digital twins are nothing new but have reached the point of wider industry adoption. [ABSTRACT FROM AUTHOR]

Unlearn's goal is to help drugmakers and academic researchers complete clinical trials while requiring fewer overall participants. [ABSTRACT FROM AUTHOR]

In this work, we present a hybrid partitioned deep learning framework for the reduced-order modeling of moving interfaces and predicting fluid–structure interaction. Using the discretized Navier–Stokes in the arbitrary Lagrangian–Eulerian reference frame, we generate the full-order flow snapshots and point cloud displacements as target physical data for the learning and inference of coupled fluid–structure dynamics. The hybrid operation of this methodology comes by combining two separate data-driven models for fluid and solid subdomains via deep learning-based reduced-order models (DL-ROMs). The proposed multi-level framework comprises the partitioned data-driven drivers for unsteady flow and the moving point cloud displacements. At the fluid–structure interface, the force information is exchanged synchronously between the two partitioned subdomain solvers. The first component of our proposed framework relies on the proper orthogonal decomposition-based recurrent neural network (POD-RNN) as a DL-ROM procedure to infer the point cloud with a moving interface. This model utilizes the POD basis modes to reduce dimensionality and evolve them in time via long short-term memory-based recurrent neural networks (LSTM-RNNs). The second component employs the convolution-based recurrent autoencoder network (CRAN) as a self-supervised DL-ROM procedure to infer the nonlinear flow dynamics at static Eulerian probes. We introduce these probes as spatially structured query nodes in the moving point cloud to treat the Lagrangian-to-Eulerian conflict together with convenience in training the CRAN driver. To determine these Eulerian probes, we construct a novel snapshot-field transfer and load recovery algorithm. They are chosen in such a way that the two components (i.e., POD-RNN and CRAN) are constrained at the interface to recover the bulk force quantities. These DL-ROM-based data-driven drivers rely on the LSTM-RNNs to evolve the low-dimensional states. A popular prototypical fluid–structure interaction problem of flow past a freely oscillating cylinder is considered to assess the efficacy of the proposed methodology for a different set of reduced velocities that lead to vortex-induced vibrations. The proposed framework tracks the interface description with acceptable accuracy and predicts the nonlinear wake dynamics over the chosen test data range. The proposed framework aligns with the development of partitioned digital twin of engineering systems, especially those involving moving boundaries and fluid–structure interactions. • Novel recursive hybrid deep learning (DL) for moving interface and FSI. • Iterative low-resolution DL grid search by preserving interface loads. • Quantification and analysis of force errors along fluid–solid interface. • Robust and accurate recovery and filtering of forces on low-resolution DL grid. • Digital twin demonstration of a prototypical FSI system. [ABSTRACT FROM AUTHOR]

Cityzenith and Terbine partner with Sin City on collecting IoT data and providing analysis on better building efficiency, among other goals [ABSTRACT FROM AUTHOR]

Combining the two partners’ engineering and construction solutions with project digital twinning for greater automation and visibility. [ABSTRACT FROM AUTHOR]