• Dr. Robert Meakin
  • CREATE-AV
  • 108 Surge Building
  • 4:00 p.m.
  • Faculty Host: Dr. Eric Paterson

The spectrum of engineering processes that span the design, development, deployment, and sustainment of air vehicles is vast and can usefully be referred to collectively as “aircraft acquisition”. The potential for multi-disciplinary, physics-based simulation modeling and High Performance Computing (HPC) to positively impact the engineering processes associated with aircraft acquisition is large. The argument put forward in this work is not the pitting of the traditional ground-based and flight test paradigm for generating needed engineering data against the capacity of physics-based simulation. Rather, it is to assert that multi-disciplinary, physics-based simulation and HPC, in combination with traditional means of generating engineering data, represent an opportunity to fundamentally change the paradigm for aircraft acquisition. The addition of physics-based simulation as a means to generate actionable engineering data enables increased capacity of the engineering workforce, reduced workloads through streamlined and more efficient workflows, and minimization of the need for rework due to an ability for early detection of design faults and aircraft performance anomalies.

Regardless of the source, in order for engineering data to be actionable, the data must be available when it is needed, correctly represent the governing physics, and be of quantifiable quality. While the preceding may appear to be a statement of the obvious, it implies a number of fundamental principles, the realization of which is nontrivial. Timeliness, physical accuracy, and uncertainty quantification are general headings. Each of these is essential to making effective programmatic decisions, but if one had to choose the most important, perhaps it would be timeliness. When the time comes to make a decision, the fact that “exact data might be available next week” is irrelevant. The decision is being made now. For example, a countdown in the launch of a spacecraft generally includes scheduled pauses to accommodate go/no go decision points. If system readiness data is not available at the scheduled time, the decision
maker will make a decision anyway – launch or delay. Launch if the decision maker deems the risk associated with the missing data to be small, or delay to await analysis and possibly the next launch window. A lack of timeliness associated with engineering data always has the effect of increasing risk and causing programmatic delays.

Decisions associated with aircraft acquisition, span engineering processes beginning with conceptual design and continuing through all subsequent phases of development, deployment, and sustainment of the final fleet of aircraft fielded. Paradoxically, decisions made at the earliest phases of acquisition are the most significant, setting overall development and life-cycle costs, yet they are currently supported by the lowest fidelity engineering data and highest uncertainties. The conundrum is compounded by the fact that design cycle-time trends inversely with position across the aircraft acquisition spectrum – meaning that not only do early-phase engineering decisions have long-term impacts, but the time available to generate needed engineering data to support the decisions is minimum. Herein lies the opportunity for physicsbased simulation and HPC to enable a paradigm change. Multi-disciplinary, physics-based simulation and HPC represent a capacity, through virtual testing, to generate needed engineering data at required levels of physical accuracy. Accordingly, the critical path to enabling a paradigm change depends most acutely on timeliness.

The present paper describes the role and technical details of a novel mesh paradigm that bridges a key technology gap on the path to realization of this vision. The paradigm is referred to as “strand mesh”. The paper introduces the strand mesh paradigm in context of a major multi-disciplinary, physics-based simulation software development project and an outline of the planned progression of capability development. Attributes of the mesh paradigm that warrant critical path designation are described along with key technology dependencies. The paper includes examples and a brief set of applications.

 

1 CREATE-AV Project Manager / IPA (University of Alabama at Birmingham), Associate Fellow AIAA