Unilever, a consumer goods giant, was faced with the challenge of reducing the environmental impact of their products. The company needed to find a way to minimize the material used in its packaging while ensuring that it remained strong enough to withstand transportation loads and a variety of use conditions. The challenge was to optimize Unilever’s packaging designs using advanced virtual simulation technology. However, at the time, Unilever did not employ many computer aided engineering (CAE) users, instead having an extremely talented team of CAD engineers at their disposal.

Boyacá, a company with 35 years of experience in newspaper delivery, was facing a significant challenge. The company needed to increase the productivity of its drivers and reduce delivery costs. The fulfillment of time schedules was crucial for customer satisfaction, and late deliveries could result in additional costs. Boyacá needed real-time information on arrival and departure times at each hub of the distribution chain to control costs with the lowest possible investment. The company uses several hundred trucks for delivery every day, making the task of tracking and optimizing delivery times a complex one.

Scania, a European heavy vehicles manufacturer, is renowned for its ability to deliver highly customized products. This unique selling proposition, however, presents a significant challenge for the company's computer aided engineering (CAE) departments. The engineers are tasked with rapidly verifying a multitude of different variants using finite element simulations. The entire virtual model assembly process, which includes positioning hundreds of components, creating contact definitions, and building part connections with pre-strained bolts, was a major goal for Scania to automate. This process was not only time-consuming but also prone to errors, thereby necessitating a solution that could streamline and automate the process.

In the aerospace industry, the overall weight of an aircraft is a critical design requirement due to the impact just a few kilograms can have on fuel efficiency and CO2 emissions. Heavier aircraft use more fuel during flight which leads to increased running costs for the airline carriers. Airbus, while designing the world’s largest passenger aircraft, the A380, aimed to ensure the design was as lightweight as possible while maintaining all performance standards. The challenge was to reduce the weight of the aircraft without compromising on the performance standards.

Unilever, a global leader in the consumer goods industry, was seeking ways to maintain its innovative edge in the male grooming market. The company was particularly focused on differentiating its Lynx (Axe) brand from competitors. The challenge was to design a new deodorant packaging concept that would stand out in the market. However, Unilever lacked the necessary in-house expertise to adopt a simulation and analysis approach for the design and testing of the new can. They needed a development partner to assist with the design and testing of the new packaging concept.

The utility industry is faced with the challenge of managing vast amounts of operational data. This data, if properly analyzed and interpreted, can provide valuable insights that can enhance operational efficiency and customer service. However, the sheer volume of data and the complexity of utility operations make it difficult for utilities to effectively leverage this data. Furthermore, each utility has unique business needs, requiring a tailored approach to data analysis and reporting. The challenge, therefore, was to develop a solution that could integrate with the existing database schema of utilities, provide dynamic reports and dashboards, and be customizable to suit the unique needs of each utility.

Force Protection, Inc. (FPI), a South Carolina-based company, had developed a new class of military vehicle, the Buffalo, designed for clearing out land mines and Improvised Explosive Devices (IEDs). The Buffalo's pioneering design, with a monocoque hull designed to deflect blast away from personnel inside, became the basis of a new U.S. military vehicle design standard known as MRAP (Mine Resistant, Ambush Protected). However, as U.S. military involvement in the Middle East increased, the need for personnel transports that could withstand the kind of anti-insurgency war U.S. soldiers were fighting became apparent. The Army requested FPI to produce a smaller, more versatile version of the Buffalo, which was named the Cougar. This new vehicle had to meet a series of military, SAE, and NTSA standards, requiring documentable analyses to ensure compliance. FPI, however, lacked a formal in-house analysis capability.

Duco, a major subsea engineering design, manufacturing, and testing facility, faced significant challenges in improving their Computer-Aided Engineering (CAE) model generation and analysis procedures for their bespoke products. These products, known as subsea 'umbilicals', are complex structures designed to withstand extreme pressure, temperature conditions, and adverse weather in hydrocarbon fields at water depths beyond 3,000 meters. The umbilicals connect the topside platform or vessel with seabed equipment, including pumping stations with electricity and hydraulic pressure. Duco's challenge was to speed up the analysis time for these structures, which had to endure demanding fatigue load cases. They were also experiencing issues with mesh quality with their incumbent pre-processor, prompting the need for a change.

B/E Aerospace, a leading manufacturer of cabin interior products for commercial and private passenger aircraft, was faced with the challenge of designing lighter and safer seats for airline passengers. The rising fuel prices were forcing airlines to find inventive ways to reduce weight within an aircraft, which in turn would lower fuel costs and ultimately ticket prices. B/E Aerospace was tasked with not only reducing the weight of the seats but also ensuring that the components provide better passenger protection in emergencies. The design of these seats was completely market-driven, with airlines demanding comfort and features for their passengers, while also asking for lighter seats to combat the relentless increase in fuel costs. The challenge was to accommodate these demands within the constraints of safety certification.

Chrysler, a renowned automotive company, was facing a challenge in managing its growing Computer-Aided Engineering (CAE) server farm. Since the 1980s, Chrysler has been using computer simulation tools for designing cars and trucks. Over the years, the use of computer-aided engineering and finite element analysis expanded, leading to a significant increase in the processor count at Chrysler’s High-Performance Computing (HPC) center. By the late 1990s, Chrysler was building up its HPC capacity to meet the growing demand for CAE simulations by installing a number of servers. Initially, Chrysler used a competitor's product for workload management. However, in 2003, they acquired 384 PBS Professional licenses to manage clusters used exclusively for computational fluid dynamics (CFD) jobs. By 2008, the HPC center was operating 10 servers from a mix of vendors using a variety of Intel and AMD chips running Red Hat or SUSE Linux. The core count on the server farm had increased to 1,544.

CILEA, a technical consortium formed by ten universities in Italy’s Lombardy region, was faced with the challenge of managing a steep growth curve in demand for its high-performance computing (HPC) facilities. The consortium, which provides computing cycles for research, had to cope with a wide variety of disciplines, projects, and applications from its 400 registered users. These users, coming from government, industry, and academia, were driving the demand for CILEA’s facilities into a steep growth curve. The consortium responded by doubling its staff over three years and building up its HPC resources. However, managing these resources, which included a 208-blade HP cluster named Lagrange, two AMD Opteron clusters, and a 64-CPU HP Superdome SMP system, was a significant challenge. The consortium needed a workload manager that was robust, flexible, and had an excellent price/performance ratio.

The Scripps Clinic's Shiley Center for Orthopedic Research and Education (SCORE) was faced with the challenge of improving the understanding and design of orthopedic implants, specifically for shoulder arthroplasty. The existing implants, typically made of titanium alloy and lined with plastics, were not expected to last more than 20 years, making them unsuitable for patients under 65. Furthermore, patients often had concerns about the range of motion, strength recovery, and longevity of the new joint. The process of modeling replacements for the meniscus, a crescent-shaped knee cartilage, was also a complex and time-consuming task, requiring the team to start the modeling process from scratch each time they wanted to change the curvature or thickness.

Motorola, a global communications leader, is faced with the challenge of developing increasingly complex cell phones that support multiple data protocols and are built to withstand rugged use. The company is driven by shortened design cycles, increasing product complexity, and reduced profit margins. Design cycles that took 18 months in 1998 are now pushing toward a six-month goal to meet business-critical product launches and fixed ship dates. To maintain a healthy profit margin, Motorola must keep down the overall cost of producing the product. The company also needs to understand 'real-world' reliability well before next-generation cell phones hit the shelves. This requires capturing product-life behavior, predicting areas needing improvement, and generating high-quality products within a greatly reduced time frame.

Ford's Numerically Intensive Computing Department (NIC) had built a substantial heterogeneous High-Performance Computing (HPC) environment over the years, combining both capacity and capability. This environment included Beowulf clusters based on Xeon, Itanium, and Alpha processors, SGI Origin and Altix servers, IBMP650 capacity, and large SMP Cray systems. While this infrastructure enabled NIC to process compute-intensive jobs in a timely manner, it also resulted in a complex infrastructure of platforms and applications. Additionally, NIC faced complexity on the solver side, running many application versions, none of which ran on all architectures. The challenge was to find a solution that could efficiently manage this complex infrastructure and provide a simple tool for users.

Viessmann Werke GmbH, a leading manufacturer of household and industrial heating technology, was faced with acoustical issues during the development of a new generation of gas-fired heating boilers. The challenge was to reduce the noise levels by increasing the structural stiffness of the boiler cover. The initial approach involved integrating additional parts to increase the stiffness. However, this method was not optimal as it resulted in increased mass and more parts, leading to higher costs. An alternative approach was sought, which led to the exploration of optimum bead patterns using Altair OptiStruct. The bead pattern concept was preferred as it resulted in less mass, fewer parts, and ultimately, a lower total cost.

The Boeing Information Technology group, based in Bellevue, Washington, provides a wide range of computing services to the entire corporation. The Data Center, which houses the high-performance computing (HPC) systems, is the core of this campus. These HPC systems are accessed by all Boeing engineering departments, with the highest demand coming from engineers running aerodynamics and structural models, especially during the early stages of aircraft design. The HPC resources are primarily used for engineering studies and alterations to existing designs. The challenge was to find a reliable workload manager that could handle Boeing's distinct clusters and optimize several resources under one queue.

The Translational Genomics Research Institute (TGen) was in need of a vendor-supported solution that could provide flexible job scheduling on Saguaro, their 512-node production cluster. TGen's mission is to translate knowledge of the human genome into therapeutics and diagnostics, a task that requires massive computational resources. The institute had established its High Performance Biocomputing Center (HPBC) to provide these resources, but they needed a solution that could handle the scheduling of large jobs across multiple nodes while also running thousands of small serial jobs on a single node. The solution also needed to be easy to install and require little to no support.

Trelleborg, a Swedish corporation with a century-long history in engineering innovation, specializes in creating molded rubber components that enhance the comfort and performance of vehicles. The design and finite element analysis (FEA) group at Trelleborg's Engineering Center in Michigan, a small but highly productive team, was facing a significant challenge. They were trying to increase their throughput without adding more personnel or hardware. The team was attempting to utilize off-hour CPU cycles from the CAD workstations on an ad hoc basis. However, they were struggling with estimating how long each job would take on a specific machine so that another job could be set up to run behind it. The team needed a way to increase available CPU time to improve their productivity and efficiency.

GE's Oil & Gas business was facing a significant challenge in job scheduling for their applications. The gas turbine engineering group was upgrading its High-Performance Computing (HPC) resources, moving from an 8-CPU UNIX server to a 20-CPU HP ProLiant 380 cluster running Red Hat Linux. However, they encountered unexpected difficulties in job scheduling using open source code. The situation was becoming increasingly difficult to manage, causing delays in production. The group leader, Alessandro Ciani, was tasked with getting the cluster into production but was frustrated with the delays. The situation was urgent, and a solution was needed to ensure the system could be put into production.

Scania, a leading global manufacturer of heavy trucks, buses, and other heavy-duty engine applications, is known for its ability to highly customize its products. This customization process, however, posed a significant challenge for Scania's computer-aided engineering (CAE) departments. The engineers had to rapidly verify a large number of different variants with finite-element (FE) simulations. Automating the entire virtual model assembly process was a major goal for Scania. The process, which included tasks such as positioning hundreds of components, creating contact definitions, and building part connections with pre-strained bolts, was time-consuming and prone to error. In the past, Scania analysts used fully automated solutions, without the capability for user interaction during execution. This lack of automation interactivity often required Scania CAE analysts to manually modify and improve the truck input models, further adding to the time-consuming process.

Boeing's Integrated Defense Systems (IDS) business unit, the world's largest military aircraft manufacturer, was faced with the challenge of refining rotorcraft designs to reduce weight and enhance affordability, reliability, and manufacturing efficiency. The design of rotorcraft, aircraft kept airborne by rotating airfoils, is a complex balance of form and function. The primary goal is to provide maximum functionality at the lowest overall weight. Weight affects the rotor's capacity for vertical lift, which in turn affects the aircraft's range and ability to fly at safe altitudes. However, reducing weight can also make the aircraft a more survivable target for ground fire. Additionally, the stiffness of the airframe needs to be tuned to reduce vibration, which affects passenger stamina, weapons use, airframe durability, and onboard electronics operation. This is also a weight-related issue. Every pound saved is an opportunity for additional improvements in efficiency, performance, ballistic tolerance, soldier survivability, maintenance, and repairability.

TRW Automotive, a global technology company and a Tier One automotive industry supplier, was facing a challenge in its European engineering centers. The company's Chassis group, responsible for designing safety systems, was operating exclusively on desktop workstations and was seeking to increase compute power and streamline workflow. The group was also keen on not committing its limited internal IT resources to the development of a new CAE computing infrastructure. The challenge was to find a solution that could provide more compute power, improve workflow, and be implemented without straining the company's IT resources.