Whether it’s putting error bars on the strength of a part, expressing a level of confidence in complex simulations, or ensuring that mission critical hardware works regardless of where it’s deployed, uncertainty quantification and analytics play a key role in ensuring success across a wide range of industries:
The aerospace industry faces challenges of greater competition, increasing passenger populations, shifting routes, environmental concerns, and volatile fuel prices. There are also many opportunities for innovative designs which take advantage of new fuels and materials. Meeting standards and ensuring performance means accounting for uncertainty in all aspects of new designs. SmartUQ allows deterministic models to be used to quantify, trace, and analyze the results and impacts of uncertainty throughout the design process. From turbine blades to control systems, cutting edge UQ and analytics has never been easier.
When tight tolerances and decreasing variability in component performance can yield large returns, uncertainty quantification becomes a competitive necessity: reducing costs, improving durability and uptime, and maximizing efficiency across variable operating ranges. SmartUQ helps turbomachinery designers understand the sources of variability, their impacts on performance, and the most cost effective ways to increase design robustness.
Building better vehicles is tough: meeting user requirements, industry demands, cost targets, and government standards while maintaining quality poses unique challenges. Traditional methods are best suited to optimizing and ensuring performance under very specific conditions. Previously, taking into account the full range of variation in materials, manufacturing, and usage when designing new vehicles was a monumental challenge. SmartUQ provides the tools to handle variability and uncertainty. Now it’s possible to rapidly explore design spaces, trace uncertainty through vehicle systems to get probabilistic performance estimates, and quickly and accurately calibrate any model to test data.
The competitive effort to keep up with Moore’s law demands ever higher levels of precision and consistency. SmartUQ provides cutting edge technologies which can accelerate design for variation programs, help trace problems to their source, and improve process control through robust design. Understanding and reducing uncertainty can lead to improved product performance, faster design cycles, and lower costs.
Uncertainty is prevalent in supply chains, manufacturing, and process design. SmartUQ offers a host of unique uncertainty quantification and analytics tools to help. Quantifying uncertainty can result in increased supply chain robustness, improved machine use efficiency, decreased scrap rates, and improved reliability.
Modern chemical producers face challenges from global competition as well as increased safety and environmental requirements. Combined with new and less uniform feed stocks from unconventional petrochemicals and the emerging biochemicals, the challenges and potential of mastering uncertainty have never been greater. By combining SmartUQ’s analytics with state of the art simulations, we are making robust reaction engineering, the design of variable processes, and the tracing of uncertainty through complex production systems easier than ever before.
Precision and process control is critical to initial product success and to maintaining production quality. SmartUQ’s unique software makes it easier to implement Quality by Design and to plan production scale up by allowing researchers and engineers to account for variation in all of the inputs and by highlighting sensitive aspects. With SmartUQ, it’s possible to quickly find not just optimal solutions, but stable optimal solutions.
The electronics market is pushing towards shorter design cycles and novel form factors, necessitating greater reliance on simulations for planning circuits, PCB layout, RF systems, thermal management, and enclosure systems. SmartUQ helps designers move beyond deterministic design when handling uncertainty in components, manufacturing processes, usage environment, and operating voltage/current. SmartUQ provides cutting edge tools for simulation analytics and uncertainty quantification, that can accelerate simulation and design cycles, reduce costs, and shorten time to market.
The energy sector is changing fast: new technologies, new regulations, and new markets. Unfortunately the role of uncertainty is increasing with the inclusion of greater fractions of renewable energy, new generation technologies, fuel price volatility, and fractious regulatory environments. SmartUQ possesses the technology to support simulations and decision making in the face of uncertainty. From equipment level to power grid robustness and stability, demand forecasts, and system wide simulations, uncertainty quantification can help improve reliability, efficiency, and safety.
With the constant strive for improvement, innovation, and reduced costs, SmartUQ’s software provides the ability to account for uncertainty such as person to person variation and precision manufacturing tolerances when improving product performance and reducing risk. SmartUQ also provides built in tools for refining and calibrating models with research data.
There is always uncertainty about what’s actually under the ground. SmartUQ provides statistical tools to clarify the probability of outcomes and quantify formation uncertainty from noisy data. It also enables engineers to make equipment designs more reliable given ranges of requirements in addition to variable materials and manufacturing processes. The sensitivity of complex systems to stochastic inputs like weather and wave loading can also be determined. SmartUQ helps make operations more profitable, more consistent, and safer.
Financial modeling has become more and more sophisticated. But the ability to act with confidence when many inputs, from commodity prices to interest rates, are uncertain is invaluable. SmartUQ provides faster analytics and uncertainty quantification technologies which can enable planners to make probabilistic predictions about model outputs. Quickly being able to determine probabilities can increase confidence and minimize risk.