Automated Optimization of Residual Reduction Algorithm Parameters in OpenSim

The residual reduction algorithm (RRA) in OpenSim improves dynamic consistency of movement simulations of musculoskeletal models. RRA requires the user to select numerous tracking weights for the joint kinematics to reduce residual errors. Selection is often performed manually, which can be time-consuming and is unlikely to yield optimal tracking weights. A multi-heuristic optimization algorithm was employed to expedite tracking weight decision making to reduce residual errors. This method produced more rigorous results than manual iterations and although the total computation time was not significantly reduced, this method does not require the user to monitor the algorithm's progress to find a solution, thereby reducing manual tuning. Supporting documentation and code to implement this optimization is freely provided to assist the community with developing movement simulations.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Optimization of a High Pressure Industrial Fan

Fans are used in industrial refineries, power generation, petrochemistry, pollution control, etc. These fans can perform in sometimes extreme, mission-critical conditions. The design of fans has historically relied on turbomachinery affinity laws, resulting in oversized machines that are expensive to manufacture and transport. With the increasingly lower CPU cost of fluid modeling, designers can now turn to CFD optimization to produce the necessary machine performance and flow conditions while respecting manufacturing constraints. The objective of this study is to maximize the pressure rise across an industrial fan while respecting manufacturing constraints. First, a 3D scan of the baseline impeller is used to create the CFD model and validated against experimental data. The baseline impeller geometry is then parameterized with 21 free parameters driving the shape of the hub, shroud, blade lean and camber. A fully automated optimization process is conducted using Numeca’s Fine™/Design3D software, allowing for a CPU-efficient Design Of Experiment (DOE) database generation and a surrogate model using the powerful Minamo optimization kernel and data-mining tool. The optimized impeller coupled with a CFD-aided redesigned volute showed an increase in overall pressure rise over the whole performance line, up to 24% at higher mass flow rates compared to the baseline geometry.

3D Multidisciplinary Automated Design Optimization Toolbox for Wind Turbine Blades

This paper presents two novel automated optimization approaches. The first one proposes a framework to optimize wind turbine blades by integrating multidisciplinary 3D parametric modeling, a physics-based optimization scheme, the Inverse Blade Element Momentum (IBEM) method, and 3D Reynolds-averaged Navier–Stokes (RANS) simulation; the second method introduces a framework combining 3D parametric modeling and an integrated goal-driven optimization together with a 4D Unsteady Reynolds-averaged Navier–Stokes (URANS) solver. In the first approach, the optimization toolbox operates concurrently with the other software packages through scripts. The automated optimization process modifies the parametric model of the blade by decreasing the twist angle and increasing the local angle of attack (AoA) across the blade at locations with lower than maximum 3D lift/drag ratio until a maximum mean lift/drag ratio for the whole blade is found. This process exploits the 3D stall delay, which is often ignored in the regular 2D BEM approach. The second approach focuses on the shape optimization of individual cross-sections where the shape near the trailing edge is adjusted to achieve high power output, using a goal-driven optimization toolbox verified by 4D URANS Computational Fluid Dynamics (CFD) simulation for the whole rotor. The results obtained from the case study indicate that (1) the 4D URANS whole rotor simulation in the second approach generates more accurate results than the 3D RANS single blade simulation with periodic boundary conditions; (2) the second approach of the framework can automatically produce the blade geometry that satisfies the optimization objective, while the first approach is less desirable as the 3D stall delay is not prominent enough to be fruitfully exploited for this particular case study.