The Wellcome Sanger Institute, a leading center for genomic discovery, was facing a significant challenge in managing the immense amount of data generated by their cancer genome projects. Each cancer sample produced approximately 250GB of data after initial processing, necessitating efficient data storage solutions. The team needed to make one of their cancer pipelines portable and optimize it for cloud deployment. Most pipelines were written and tested on local machines and then run in parallel on compute clusters with shared storage. However, the I/O behavior on clusters was very different, and without comprehensive I/O profiling tools, inefficient I/O patterns could negatively impact storage performance and hinder other work processes.

The client, a global bank with operations in over 60 countries, was facing challenges with its order management system (OMS) used for managing trade execution. The OMS provided some visibility into the trading workflow, but it lacked the ability to display aggregated information or make real-time comparisons between current activity and historical trading data. It also did not support filtering, which was crucial for traders to answer questions like the impact of new product offerings on business in specific markets. The OMS maintained multi-day open orders and data completed orders for a few weeks to allow rebooking, but it did not store and display large amounts of historical data that would enable traders to analyze trends and spot patterns in trading activity.

Hatch Ltd., a global supplier of engineering, project, and construction management services to the mining, metallurgical, energy, and infrastructure sectors, was contracted to upgrade the conveyor transfer point at an existing coal preparation plant. The client required upgrades to accommodate a scheduled increase in production. The conveyor carried coarse product coal, topped by a layer of filter cake with a high moisture content. The existing chute was prone to material build-up of this “sticky” filter cake, often plugging during surges in flow rate and causing costly downtime. Despite being a relatively small conveyor, at 1,200 t/h, it was critical to ensuring constant product throughput to truck-loading bins.

The increasing trend in wind power technology is to enhance power output through an increase in rotor diameter. However, as the rotor diameter increases, aeroelastic effects become increasingly important in the design of an efficient blade. A detailed understanding of the fluid elastic coupling can lead to improved designs, yielding more power, reduced maintenance, and ultimately leading to an overall reduction in the cost of electricity. Current wind turbine design practices use desktop engineering tools such as FAST and ADAMS to provide information about the aero-elastic behavior of the turbines. However, each of these techniques has its own advantages and disadvantages. An evolving approach for generating performance data on wind turbine rotors is through the use of Computational Fluid Dynamics (CFD).

In the competitive world of bicycle racing, the design and aerodynamics of the bike play a crucial role in a cyclist's performance. Manufacturers constantly strive to reduce the weight and aerodynamic drag of bikes by experimenting with different materials and shapes for the frames, wheels, and tires. However, evaluating the drag force at the system level and isolating the impact of hundreds of design variables at the component level has been a challenge. Traditional methods like wind tunnel testing are expensive, and computational fluid dynamics (CFD) analysis workflows have been too lengthy to be practical. The challenge was to develop a highly automated, repeatable workflow methodology to accelerate the entire CFD process and provide new insights into the role various components play in bike performance.

INESPASA, a Spanish engineering company specializing in the aerospace and automotive sectors, faced a significant challenge in maintaining competitiveness in a demanding market. The company needed to deliver high-quality, complex products and components that complied with stringent standards and regulations, all while adhering to tight deadlines and limited budgets. A major challenge was the vertical integration of aerostructures, where adjustments were often required during the production of the first units, despite careful development. The time-consuming and expensive process of developing and testing aircraft component prototypes posed a significant hurdle. INESPASA needed to find faster, more efficient methods, such as multiphysics simulation, to evaluate its aero-structure designs, identify potential issues before they occurred, and ensure the viability of the implemented solutions.

RF2B was tasked with developing a phased array antenna proof of concept design for small cell Citizens Broadband Radio Service (CBRS) base stations at 3.5GHz for its customer Menlo Micro, a developer of high-performance RF MEMS switch integrated circuits. The challenge was to achieve enough azimuth and elevation beam steering range with enough grating lobe suppression and higher efficiency. This required high-level design analysis to decide the number of columns and rows, element type, element spacing, angular step size, and amplitude tapering for the antenna array. Another challenge was the overall complexity of the design, requiring a time-efficient antenna array simulation and design methodology that included the feed network. The antenna array also needed to be optimized in its mechanical environment, including the enclosure and radome.

The École de Technologie Supérieure (ÉTS) Team Rafale faced the challenge of building a high-speed, lightweight catamaran for the 'Little America’s Cup'. The class rules stipulated that the catamaran should be less than 25ft long with a maximum width of 14ft and less than 300sq ft. sail area. This presented a challenging opportunity to drive innovation and use the best materials possible. The catamaran needed to be built in less than 18 months. The hydrofoils, despite being less than two square feet in surface area, needed to be able to lift the entire boat and its two-man crew out of the water. The 30ft mast at the heart of the rigid wingsail carries almost 4000 lb. of compression while weighing less than 30lbs.

Inphi Corporation, a leader in high-speed data movement interconnects, was facing a challenge in the competitive semiconductor design and electronic design automation (EDA) industry. The company needed to leverage high-performance computing (HPC) and high-end design software to stay ahead of the competition. However, these tools are often expensive, and maximizing their utilization to ensure the design and development process runs quickly and efficiently was crucial. The company needed to deliver innovative technologies to market faster than competitors without increasing team size. Inphi, being the first-to-market with a 64Gbaud dual-channel linear TIA amplifier, a silicon photonics 100G DWDM platform in a QSFP28 form factor, and a 400G (4x100G PAM) Porrima™ Optics DSP platform, understood the importance of HPC and EDA software performance optimization.

Whirlpool, a renowned home appliance manufacturer, was facing a significant challenge in scaling up the capacity of its Indian design center. The company wanted to commit more to virtual design, testing, and evaluation, but the high licensing costs of technical solutions were a major deterrent. The challenge was to find a cost-effective solution that would allow them to increase their virtual design capacity without incurring prohibitive costs. This was crucial for Whirlpool to maintain its competitive edge in the market and continue delivering high-quality appliances to its customers.

In the space industry, the design, building, and testing of rover prototypes is a costly and time-consuming process. System testing typically does not occur until late in the design/testing process, leading to a long development time. The challenge was to find a way to test components within a simulation loop before a full rover prototype is available. This would create a virtual testing environment for the component under test, tricking it into thinking it is operating within a full prototype. The goal was to progressively add hardware components to the simulation loop as they become available, allowing system testing to take place even without all the hardware components, thereby bridging the gap between the design and testing phases.

The Ford Focus STREET collaborative team faced a significant challenge: they had only six weeks to complete a design for a specialty vehicle based on the Ford Focus platform. The project was a collaboration between Ford Racing, Altair Engineering, Bayer MaterialScience LLC, and PPG Industries. The design had to be unique and stylish, particularly above the belt line of the vehicle, which was critical for the Specialty Equipment Market Association (SEMA) show. The team also had to overcome logistical challenges such as sourcing the right materials in the appropriate color and developing inexpensive tooling for the molding of the parts. PPG had to check its inventory for the raw stock of Solextra, a blue glass that imparts a distinctive look, and adjust its manufacturing processes to fabricate the specialty glass. They also had to formulate and deliver a complementary color for the vehicle’s exterior finish within the tight timeframe.

HBPO GmbH, a joint venture of global leaders in automotive components, specializes in the development, assembly, and logistics of complete front-end modules for vehicle manufacturers. The challenge lies in the complexity of these modules, which require numerous components like headlights, radiator grille, bumper, front-end carrier, and components of the vehicle’s air conditioning, engine cooling, and crash management system. Depending on customer requests and preset specifications, HBPO covers assembly, development, and systems integration projects. The development and assembly of a front-end module require a wide variety of simulation applications, such as structural analysis, molding simulation, virtual crash tests, material data management, and more. Numerous varying software tools are required, some of which are only rarely used. To cover all these disciplines, it is crucial for HBPO to keep the development costs as low as possible and to have access to the required tools whenever they are needed.

RUAG Space, a leading provider of products for the space industry, was faced with the challenge of combining the powerful meshing and post-processing capabilities of Altair HyperWorks with the advanced composite failure analysis methods provided by ESAComp software from Altair. The company needed to streamline and accelerate the analysis of composite structures by significantly reducing unnecessary breaks in the data flow. The challenge was to create a seamless integration between the two software tools to provide a unified work environment for producing results needed in the design verification of satellite structures.

BMW, a leading manufacturer of premium automobiles and motorcycles, faced a significant challenge in ensuring passenger safety during crash events. The complexity of orchestrating the chain of events during a crash, including the timing of discrete events such as bolts breaking or parts coming into contact, was a daunting task. The key performance indicators, including energy absorption, peak force level before failure, local displacements, and weight, were either too complex or resulted in over-constrained optimization problems. This made it difficult to confidently validate crashworthiness within the fast-paced product development process. The challenge was to find a solution that could simplify this process, reduce the number of design iterations required, and ensure the highest level of safety for passengers.

The challenge lies in the efficient and accurate simulation of smart materials, specifically initially curved and twisted anisotropic beams such as those found on wind turbines and helicopters. These materials are complex due to their arbitrary sectional topology and the coupling effects between multiple physical domains. Direct analysis of these composite beam structures is computationally expensive, even with advanced supercomputers. The use of beam theory reduces computational cost significantly, but the lack of reliable mathematical modeling and analysis for smart systems presents a major barrier. This is due to the difficulty in modeling coupling effects and the anisotropic and heterogeneous nature of composite materials.

Skaigh Engineering, a UK-based company specializing in high-quality aluminum gravity die castings, faced several challenges in its manufacturing process. The casting industry is undergoing significant transformations due to increased customer demands and the growing complexity of parts. To stay competitive and grow in the global market, casting manufacturers like Skaigh needed to address issues such as reducing development and manufacturing costs. The company was dealing with wasted time spent on improving customers’ poor manufacturing techniques and long lead times due to trial-and-error development methods involving inherited dies. To improve the development process and address these challenges, Skaigh began exploring the use of simulation.

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.