HYCET Transmission Technology Hebei Co. Ltd (HYCET), a comprehensive enterprise focusing on E-drive systems, faced a significant challenge in troubleshooting gearbox mechanical failures. These failures, including pitting, erosion, and peeling, were primarily caused by insufficient lubrication. Given that E-drive speed can reach up to 20,000 rpm, HYCET needed a solution that could accurately calculate churning losses. The company also needed to consider factors such as windage effects, oil volume, and the amount of air bubbles (aeration) present in oil. The challenge was to find a solution that could provide detailed insights into these complex flow phenomena and help the team answer vital questions related to oil flow and potential leaks.

Argonne National Laboratory, a U.S. Department of Energy multidisciplinary science and engineering research center, was faced with the challenge of tracking the rapidly evolving SARS-CoV-2 virus and its variants during the COVID-19 pandemic. The rapid evolution of the virus, sometimes becoming deadlier and more transmissible, necessitated the quick identification of variants of concern (VOCs). The early discovery of VOCs is crucial in saving lives by providing scientists with the time to develop effective vaccines and treatments. However, the existing methods of tracking these variants were slow and inefficient, posing a significant challenge to the research team.

Subaru, a global automobile and aircraft manufacturer, is committed to achieving zero fatal traffic accidents by 2030. To reach this goal, the company needs to continually innovate and ensure high collision safety, which requires conducting Computer-Aided Engineering (CAE) simulations using High-Performance Computing (HPC). Subaru had been maintaining its own HPC environment near its main manufacturing facility in Japan’s Gunma prefecture. However, as the computational processing requirements for simulation increased, the team faced a shortage of power and space for expansion. They started using a private cloud located in a remote data center in Tokyo, which required a dedicated line for user access. Due to the high cost, they decided to evaluate public cloud options and sought recommendations from the Japan Automobile Manufacturers Association’s cloud working group.

SS Global, a firm offering consultancy and IoT integration services, faced a challenge in developing a real-time, detailed picture of the Air Quality Index (AQI) throughout a metropolitan region. They utilized IoT sensor data on temperature, humidity, and concentrations of particulate matter and atmospheric gases. The engineers needed the ability to play back historical data in real time or faster to examine and understand trends and causal relationships between weather, AQI, and other factors. It was also critical for the system to flag irregularities and anomalies in AQI and atmospheric changes that could indicate future problems affecting the local population.

Patrone and Mongiello, a leading tier-one automotive supplier based in Italy, was seeking a solution to enhance the monitoring and control of its sheet metal forming process. The company aimed to improve product quality and reduce production waste. The solution needed to account for sheet metal properties such as stress, strain, and elasticity, and cover equipment operating conditions such as pad force and die friction. The challenge was to find a solution that could accurately simulate the company's existing sheet metal forming process, including machine press and sheet-metal behavior, system variables, and operating conditions.

ZT Innovations, a motor control consulting firm with over 45 years of experience, faced a significant challenge in maintaining their reputation for delivering high-quality results in a short amount of time. The firm's business primarily came from repeat customers and referrals, making their reputation crucial. However, they found that the software tools available in the market were not providing a comprehensive solution for their needs. The firm had to use multiple tools for different steps in the process, which led to a sequential and time-consuming workflow. The tools they used required substantial simulation time to accurately represent the physical system, further extending the time to completion for their clients.

NVIDIA, a pioneer in accelerated computing, built a massive 750,000 sq. ft. building named Voyager. To accompany the architectural innovation, NVIDIA wanted an equally impressive, private 5G network to support multi-access edge computing (MEC) applications and leverage the unlicensed Citizens Broadband Radio Service (CBRS) band. The first MEC application required intelligent video analytics with 5G cameras in the lobby area. A network development challenge was the 150 MHz limit within the CBRS spectrum. To handle this, NVIDIA decided to use 100 MHz minimum bandwidth to maintain the desired throughput levels and use the same frequency carrier for all radio units. This made the 5G network’s needed throughput challenging. NVIDIA also wanted to compare two different vendor radio units, one with directional transmission and one with omni-directional transmission, each with 4 downlink (DL) multiple-input multiple-output (MIMO) layers and 2 uplink (UL) MIMO layers.

TEN TECH AERO, a provider of multi-discipline engineering services, was facing significant challenges with their existing computer-aided engineering (CAE) tools. The company, known for its speed and accuracy, was experiencing consistent crashing and long processing times with their previous tool, with processing sometimes taking up to two weeks. These issues were classified as 'known bugs', and the recommended solution was to recreate the models, a time-consuming and inefficient process. The company, which often works with large designs of more than 200 million elements, needed a more effective solution for every step of their CAE process, from preprocessing to solving and postprocessing. They also needed a tool that could work with their existing legacy data, as they could not afford to lose the models they had created and stored over many years. Additionally, processing time was a significant issue, as TEN TECH AERO does not charge their customers for solving and processing time, meaning that extra time spent on processing was time not spent on billable services.

Stanley Robotics, a deep tech company, aimed to revolutionize the vehicle logistics industry by introducing autonomous robots to move cars in storage compounds. The challenge was to develop a robot that was fast, reliable, and efficient to meet the demands of the car logistics industry. The robot needed to be designed with mechanical optimization in mind to compete effectively with traditional car logistics companies. Stanley Robotics needed to prove that its robotic vehicle could achieve a significant number of moves per year and demonstrate its durability. To achieve this, the company needed a partner to help develop a digital twin of their robot to calculate all the demands placed upon it and validate their product through durability calculations.

Assystem, an international engineering and digital services group, was contracted by the United Kingdom Atomic Energy Authority (UKAEA) to develop physics-based digital twins for their operational fusion powerplants. The challenge was that fusion powerplants required complex digital simulation models during the design assessment phase. The inspection and maintenance intervals and total life of these powerplants were defined based on the expected loading on the as-designed model, which often differed from the actual loads the plant was subjected to. This discrepancy provided a scope for programs aimed at improving the plant’s lifetime value or quantifying the effects of higher-than-expected usage. Assystem wanted to leverage the expensive design models to create a digital twin by inputting the sensor data that was livestreamed from the plant. This would help engineers understand the plant’s structural integrity and further optimize inspection and maintenance schedules.

C-TEC, a Germany-based company specializing in the development and production of intelligent devices, machines, and systems, faced a significant challenge in meeting the new E.U. standards for passenger-vehicle fuel efficiency and emissions that came into effect in 2017. The Worldwide Harmonised Light Vehicle Test Procedure (WLTP) demanded more stringent compliance, pushing C-TEC to improve and optimize the aerodynamics of box utility vehicles while retaining most of the same components. This optimization required GPU-accelerated high-performance computing (HPC). However, C-TEC lacked experience in CFD simulations and did not have access to the necessary hardware to run expensive, compute-intensive multi-GPU workloads.

Accuride Corporation, a leading global commercial and passenger vehicle component supplier, faced a significant challenge in their product development process. The creation of a solid hexahedral mesh, a crucial step in developing truck and passenger wheels, was a complex and time-consuming task. This process required an in-depth understanding of advanced meshing techniques and component quality standards. Moreover, the task was so specialized that only a few engineers at Accuride could handle it, leading to potential delays in time-critical projects. The company also struggled to share this meshing knowledge beyond department boundaries, making it difficult to include everyone in the process, especially simulation beginners.

Nanyang Technological University's High Performance Computing Centre (HPCC) was facing a significant challenge. With over 4,500 CPU cores, 40 NVIDIA Tesla GPGPU cards, 2,700TB storage, 100GB InfiniBand interconnect, and 40G/100G Ethernet backbone with technical support, HPCC was producing nearly 19 million core CPU-hours and nearly 300,000 GPU-hours in 2021 to support more than 160 NTU researchers. The HPCC digital community had grown to nearly 800 NTU members, and as its ranks continued to increase, the number of HPC and AI applications was growing rapidly. The small, four-engineer team at HPCC needed cutting-edge tools to support their growing user community and evaluate scaling up to a hybrid cloud environment. They required job-level insights to understand runtime issues, metrics on I/O, CPU, and memory to identify bottlenecks, and the ability to detect problematic applications and rogue jobs with bad I/O patterns that could overload shared storage.

Prodrive, a leading motorsport technology company, faced a significant challenge with its legacy analytics system. The system collected sensor data from its cars, but struggled with large datasets collected over long periods of time. Analyzing engine data over a car’s lifetime was crucial for Prodrive as it could provide valuable insight into design and manufacturing tweaks that could improve vehicle performance. Furthermore, accurately predicting when critical components are likely to fail would help racing teams optimize pit stop timing during races. Given the number of sensors in Prodrive-built cars and their sampling frequencies, the amount of data collected over a car’s lifetime was significant. Each car could produce about half a terabyte of data during an average race weekend and five to ten terabytes of data every week during test runs. Prodrive needed data analytics software that could manage very large volumes of data, provide better management capabilities, and support fast development and implementation cycles.

Ford Motor Company, a global automotive giant, was facing challenges in the realm of Additive Manufacturing (AM), a process that involves adding material layer by layer to manufacture components. This technology, while promising significant benefits such as cost reduction, tooling improvements, and the ability to create complex designs, is relatively new. As a result, the industry's expertise in AM is not on par with traditional manufacturing. Ford found it challenging to set printer parameters correctly, as incorrect settings could lead to structural failures, performance deficiencies, and aesthetic issues in the printed components. The pursuit of mechanical design efficiency often led to overlooking critical considerations.

Elisava Racing Team, a project of final year students at ELISAVA Barcelona School of Design and Engineering, had the challenge of designing and developing an electric motorcycle to compete in the Barcelona Smart Moto Challenge. The team had already optimized important structural parts of their previous designs, “ERAY” and “Dayna,” but wanted to take their latest design “Dayna EVO” a step further. The team aimed to create a 100% electric off-road motorcycle with an IoT connection and a medical service capability. They sought to improve the bike’s safety, comfort, and structural behavior, aiming for a lighter, simpler bike. To achieve this, they needed simulation tools to generate shapes, predict material behavior, and optimize manufacturing processes for the fully electrified motorbike.

PUNCH Torino S.P.A., a company specializing in designing and developing innovative propulsion systems and control solutions, faced a significant challenge after joining the PUNCH Group. The team needed to build a new High-Performance Computing (HPC) infrastructure from scratch to accommodate their users’ technology needs. They decided to avoid the expense and maintenance issues inherent in on-premises computing and instead opted for a complex, multi-vendor cloud architecture. However, setting up such an architecture required an expert partner with a deep understanding of a range of technical knowledge, including high-performance computing (HPC).

TokyoFlash Japan, a leading designer and seller of unique wristwatches, faced the challenge of designing unique and stylish wristwatches that would appeal to their target audience. The company's design philosophy is to create watches that are not only unique but also fashionable. The designers at TokyoFlash believe in working on wild projects and their mantra is 'the crazier, the better'. However, the challenge was to bring these wild and crazy ideas to life and present them to the target audience in a way that they could visualize and appreciate the designs.

XOX Audio Tools, a company that brings high design and advanced technologies to the musical field, was faced with the challenge of creating a completely new electric guitar that not only sounded good but also looked aesthetically pleasing. This was a significant challenge considering the lack of new ideas and features in the musical instrument field. The company wanted to create a unique design that would stand out in the market, while also ensuring that the guitar was functional and performed well. The goal was to find the ideal shape that would meet these requirements.

Pininfarina Extra, a company known for its elegance, essentiality, and innovation in the automotive industry, faced the challenge of extending these values to sectors outside the automotive industry. The company aimed to bring its unique design philosophy to everyday products, focusing on an elegant, essential style that places human needs at the center. The challenge was not only to understand the continuous evolution of modern life but also to interpret different cultures and social paradigms. This required a multicultural and cross-disciplinary team capable of comprehending and adapting to the changing dynamics of the world.

Automotive Lighting, a global leader in exterior automotive lighting, faced a challenge in delivering brand-specific styling and rendering of rear lamps. The rear lamps are not only crucial safety components but also serve as strong brand indicators, incorporating important styling elements that define the appearance and identity of a particular vehicle model. The company needed to ensure these safety and brand elements received precise attention to detail. The complexity of the components, including metallic reflectors, light bulb or LED sources, embossed and transparent elements such as polycarbonate lenses, made the rendering process challenging. Furthermore, the rendering process had to consider the dual usage of automotive lamps, which are switched off during the day and switched on during the night.

Ron Mendell, a renowned concept artist in the Hollywood motion picture industry, faced the challenge of assisting filmmakers in constructing the reality inhabited by their characters. His role was to transport the audience into the characters’ world, a key element of effective storytelling in any motion picture. This task fell under the purview of the Art Department, requiring a significant amount of effort and creativity. Prior to the implementation of a new solution, Mendell's process was manual. He would start with pencil and paper, developing his ideas until a final design was approved. This process involved sketching, using color markers, paint, and other traditional art tools. The final steps included drafting, dimensioning, and sectioning until enough 2D drawings were generated to hand off to a craftsman. This manual process was time-consuming and lacked the flexibility for quick changes.

The Weizmann Institute of Science's Chemistry Faculty faced a significant challenge in managing their high-performance computing (HPC) facility. The HPC cluster, consisting of 1242 cores, served hundreds of faculty members and was in the process of expanding to 3096 cores. The cluster was used for a wide range of research fields, including quantum mechanics, protein folding, DNA recognition, turbulence physics, and climate modeling. The installed software was a mix of advanced C and FORTRAN compilers, mathematical libraries, and a variety of free, academic, commercial, and homegrown dedicated software tools. The workload types varied widely, and different teams had different priorities and needs. The computing environment was a complex system of users, resources, requirements, and policies that needed to be carefully managed. The Institute sought a workload management software vendor that could provide consistently high performance, support for complexity in user priorities and profiles, a proactive, collaborative approach to solution delivery, and reliable user support.

The Deep Orange Program of the Clemson University International Center for Automotive Research (CUICAR) was tasked with designing a six-seat sports car using innovative sheet-folding technology. The goal was to develop a vehicle based on the architecture of a mainstream hybrid concept marketed toward Generation Y. The design had to accommodate four 95th percentile male occupants in the outboard seats and two 50th percentile male occupants on the middle seats using a 2-row, 3+3 seating configuration. The body-in-white (BIW) structural design concept was chosen to explore the Industrial Origami® patented technology that enables the folding of lighter gauge material into complex shapes for the body structural members. The forming is completed with simple, low-cost fixtures, at the assembly location. The challenge was to balance design requirements for BIW stiffness, packaging space, cost, and weight.

The Queensland Institute of Medical Research (QIMR), one of Australia's largest and most successful medical research institutes, faced a significant challenge in providing shared High-Performance Computing (HPC) resources to its hundreds of scientists, students, and support staff. The institute, which is home to over 50 separate laboratories supporting six research departments, needed advanced facilities to support its scientists' cutting-edge projects and attract the best researchers. To meet this need, an HPC cluster was established to be shared as a service among the scientific labs at QIMR. However, managing job scheduling and optimizing throughput on this shared resource was a complex task that required a reliable workload management system.

The Aarhus School of Architecture in Denmark was keen on exploring the potential of applying simulation-based topology optimization, a technique commonly used in the automotive, aeronautical, and naval industries, to architectural concrete structures. The challenge was to combine this with robotic fabrication of polystyrene formwork for concrete casting. The Unikabeton Prototype project was created for this purpose, involving collaboration among the eight largest institutions and corporations in the Danish building industry. However, the use of computerized optimization tools was largely foreign to the field of architecture. There was a reluctance to lose design control to the optimization software, and this conservatism in the architectural industry posed a significant challenge. The Unikabeton project was one of the first academic research projects to address the use of topology optimization in architectural design. The potential payoff was significant, considering that CO2 emissions from the production of concrete account for 5 percent of total global emissions.

The Scripps Research Institute (TSRI), the world’s largest private non-profit biomedical research facility, faced a significant challenge in fulfilling compute cycles for its scientists. The institute's research, which spans across immunology, molecular biology, cell biology, chemistry, neurosciences, autoimmune diseases, cardiovascular disorders, and cancer, is highly compute-intensive. The Research Computing Department at TSRI operates three High-Performance Computing (HPC) platforms to deliver the compute cycles needed by the scientists. However, managing the workload across these platforms and providing a seamless interface for the scientists was a significant challenge. The institute needed a solution that could handle workload management for up to 500 account-holding users, of which 75-100 are most active, without requiring a lot of support.

The aluminum extrusion industry has been facing significant challenges due to the shorter life and frequent failures of dies used in the extrusion of hard alloys. These issues have a direct impact on productivity and increase production costs. The situation is further complicated by the increasing use of aluminum extrusions in various industries such as automotive, aerospace, railway, medical, architectural, and others. These applications have stringent strength and surface quality requirements, often necessitating the use of newer and harder alloys. Traditional die design practices have proven inadequate for these new demands, resulting in dies with shorter lifespans. The Conglin Group, a leader in Chinese aluminum fabrication technology, sought to address these challenges and meet the growing demands from both domestic and international markets.

VTT, a leading research and technology centre in Finland, partnered with Nurmi Cylinders, a Finnish manufacturer of hydraulic cylinder products, to optimise a valve block for demanding industrial applications using Additive Manufacturing (AM). The challenge was to design a valve block that would fully benefit from the AM process, reducing its size and the amount of material needed, and optimising its internal channels to produce a better component for the customer. Traditional manufacturing methods for valve blocks involve forming a block of metal into the desired shape and drilling internal channels to accommodate hydraulic fluid flow. This process is often cumbersome and prone to alignment issues and potential leakage. Furthermore, not every component or product is suitable for AM, depending on its size, form, design, and the quantity needed.

The Tokyo National Museum (TNM), founded in 1872, holds over 113,000 cultural assets including paintings, sculptures, ceramics, and more. These priceless artifacts often need to be transported between locations, making their packaging and transportation a serious business. The TNM discovered an unexpected and unacceptable vibration loading to these precious artifacts during transportation. The museum had little control over the vehicle dynamics of the shipping trucks, making it clear that the packaging system design needed to be re-evaluated. The TNM had been using coil spring type “vibration isolators” for shipments of cultural assets. These isolators were positioned at the bottom of a shipping box, which contained the art objects. However, the results from both a random lab test and a trial truck shipment indicated a resonance frequency between 10 Hz to 20 Hz, which was within the truck’s frequency range of excitation (10 Hz to 20 Hz), leading to potential damage to the artifacts.