Browse Topic: Digital twin
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
Researchers at the Johns Hopkins Applied Physics Laboratory have developed a machine learning method that could have a huge impact on understanding how material is formed during the additive manufacturing process. John Hopkins Applied Physics Laboratory, Laurel, MD Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin. “We anticipate that this new approach will be extremely impactful in helping design and understand material formation during additive manufacturing processes, and this fits into our overarching strategy focused on accelerating materials development for national security,” said
Virtualization features such as digital twins and virtual patching can accelerate development and make commercial vehicles more agile and secure. There is one sure-fire way to secure commercial vehicles from cyber-attacks. “You just remove the connectivity,” quipped Brandon Barry, CEO of Block Harbor Cybersecurity and the moderator of a panel session on “cybersecurity of virtual machines” at the SAE COMVEC 2024 conference in Schaumburg, Illinois. Obviously, that train has left the station - commercial vehicles of all types, including trains, are only becoming more automated and connected, which increases the risks for cyber-attacks. “We have very connected vehicles, so attacks can be posed not just through powertrain solutions but also through telemetry, infotainment systems connected to different applications and services, and also through cloud platforms,” said Trisha Chatterjee, current product support and data specialist for fuel cell and hydrogen technology at Accelera by Cummins.
Northrop Grumman San Diego, CA jacqueline.rainey@ngc.com
Researchers have developed a new method for predicting what data wireless computing users will need before they need it, making wireless networks faster and more reliable. The new method makes use of a technique called a “digital twin,” which effectively clones the network it is supporting.
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin.
Effective thermal management is crucial for vehicles, impacting both passenger comfort and safety, as well as overall energy efficiency. Electric vehicles (EVs) are particularly sensitive to thermal considerations, as customers often experience range anxiety. Improving efficiency not only benefits customers by extending vehicle range and reducing operational costs but also provides manufacturers with a competitive edge and potential revenue growth. Additionally, efficient thermal management contributes to minimizing the environmental impact of the vehicle throughout its lifespan. Digital twins have gained prominence across various industries due to their ability to accelerate development while minimizing testing costs. Some applications have transitioned to comprehensive three-dimensional models, while others employ model reduction techniques or hybrid approaches that combine different modeling methods. The discovery of unknown working mechanisms, more efficient and effective control
A company says that its digital twin alignment system, incorporating a sophisticated AI algorithm and an off-the-shelf camera, has the potential to revolutionize the auto industry, potentially saving it up to a staggering $20 billion in the effort to detect defects on the manufacturing line. Generally, such inspections of spot welds, bolt holes and the like are handled one of three ways: Slow manual inspections that can have high error rates. Even slower inspection with coordinate-measuring machines (CMMs) that can take hours to inspect 150 spot welds. Tremendously expensive technology, such as lasers, that still aren't perfect.
VI-grade introduced a Driver-in-Motion Full-Spectrum Dynamic Simulator for multi-attribute virtual tests. Despite rainy skies above northeastern Italy in mid-May, the mood at VI-grade's 2024 Zero Prototype Summit (ZPS) was decidedly sunny. VI-grade's partners from around the world were on hand to see the world premiere of the company's new Driver-in-Motion Full-Spectrum Dynamic Simulator (DiM FSS) that allows for multi-attribute applications. An update to VI-grade's advanced DiM units, the DiM FSS is a carbon fiber cockpit with shakers that can be mounted on top of VI-grade's existing dynamic simulators to provide NVH simulations at the same time as dynamic simulations.
In the increasingly connected and digital world, businesses are sprinting to integrate technological advancements into their corporate fabric. This is evident with the emerging concept of “digital twinning.” Digital twins are virtual representations of real-world objects or systems used to digitally model performance, identify inefficiencies, and design solutions. This helps improve the “real world” product, reduces costs, and increases efficiency. However, this replication of a physical entity in the digital space is not without its challenges. One of the challenges that will become increasingly prevalent is the processing, storing, and transmitting of Controlled Unclassified Information (CUI). If CUI is not protected properly, an idea to save time, money, and effort could result in the loss of critical data. The Department of Defense's (DoD) CUI Program website defines CUI as “government-created or owned unclassified information that allows for, or requires, safeguarding and
Thin cylindrical shells are ubiquitous structural elements in aerospace structures, and they experience catastrophic buckling under axial compression. The recent advancements in theoretical and numerical studies aided in realising the role of localisation in shell buckling. However, the instantaneous buckling made it unfeasible for the experimental observations to corroborate the numerical results. This necessitates high-fidelity shell buckling experiments using full-filed measurement techniques. Cutouts are deliberate and inevitable geometrical imperfections in actual structures that could dictate the buckling response. Additive manufacturing makes fabricating shells with tailored imperfections and studying various conceivable designs feasible. Consequently, to comprehend the effect of circular cutout on the buckling response, cylindrical shells are 3D printed in thermoplastic polyurethane (TPU) with a circular cutout of a specific size that could significantly shorten the buckling
Better digital twins powered by more powerful AI are going to change not just how car commercials are created in the future, but could open the door to entirely new design and engineering methods. That was one message delivered at a panel discussion on “Generative AI and Industrial Digitalization in the Automotive Industry” at NVIDIA's GTC 2024 event in San Jose, California, in March. Jaguar Land Rover's chief data and AI officer, Chrissie Kemp, said JLR was able to leverage the digital twin capability in NVIDIA's Omniverse platform, including the Edify and Picasso microservices for generative AI, to render high-fidelity images of a Defender in its appropriate environments just by using conversational prompts. Saying, “take me to the mountains,” for example, transforms the background of the photorealistic video, making it look like the car is driving there.
To learn about the use of digital twins for machining operations in industry, I interviewed Gisbert Ledvon, VP of Marketing at HEIDENHAIN Corporation, Schaumburg, Illinois.
Accelerated adoption of electric propulsion system in mobility industry has stressed the time and iterations of product development cycle which was traditionally known to go over multiple iterations and phases. Current market demands a timely introduction of compelling products that brings high value to end user. Further, a growing emphasis over reducing mineral content using sustainable options and process, adds further complexity to multi-objective-optimization of electric drive systems. At BorgWarner our engineers use Digital-Twins, physics-based models which closely represent BorgWarner products in greater dept (physics) thus allowing an improved assessment of product design (components and systems) to target application at very early stage in product development. The spring success with Digital-Twin, BorgWarner furthered enhanced the model through introducing Artificial Intelligent (AI) and Machine Learning (ML) technologies in both modelling and virtual sensing. This paper will
The context for real-world emissions compliance has widened with the anticipated implementation of EU7 emissions regulations. The more stringent emissions limits and deeper real-world driving test fields of EU7 make compliance more challenging. While EU6 emissions legislation provided clear boundaries by which vehicle and powertrain Original Equipment Manufacturers (OEMs) could develop and calibrate against, EU7 creates additional challenges. To ensure that emissions produced during any real-world driving comply with legal limits, physical testing conducted in-house and in-field to evaluate emissions compliance of a vehicle and powertrain will not be sufficient. Given this, OEMs will likely need to incorporate some type of virtual engineering to supplement physical testing. In this respect, the HORIBA Intelligent Lab virtual engineering toolset has been created and deployed to produce empirical digital twins of a modern light-duty electrified gasoline Internal Combustion Engine (ICE
With the advent of this new era of electric-driven automobiles, the simulation and virtual digital twin modeling world is now embarking on new sets of challenges. Getting key insights into electric motor behavior has a significant impact on the net output and range of electric vehicles. In this paper, a complete 3D CFD model of an Electric Motor is developed to understand its churning losses at different operating speeds. The simulation study details how the flow field develops inside this electric motor at different operating speeds and oil temperatures. The contributions of the crown and weld endrings, crown and weld end-windings, and airgap to the net churning loss are also analyzed. The oil distribution patterns on the end-windings show the effect of the centrifugal effect in scrapping oil from the inner structures at higher speeds. Also, the effect of the sump height with higher operating speeds are also analyzed. The net churning losses obtained from the simulations are compared
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