On the suitability of the connection machine for direct particle simulation

The algorithmic structure was examined of the vectorizable Stanford particle simulation (SPS) method and the structure is reformulated in data parallel form. Some of the SPS algorithms can be directly translated to data parallel, but several of the vectorizable algorithms have no direct data parallel equivalent. This requires the development of new, strictly data parallel algorithms. In particular, a new sorting algorithm is developed to identify collision candidates in the simulation and a master/slave algorithm is developed to minimize communication cost in large table look up. Validation of the method is undertaken through test calculations for thermal relaxation of a gas, shock wave profiles, and shock reflection from a stationary wall. A qualitative measure is provided of the performance of the Connection Machine for direct particle simulation. The massively parallel architecture of the Connection Machine is found quite suitable for this type of calculation. However, there are difficulties in taking full advantage of this architecture because of lack of a broad based tradition of data parallel programming. An important outcome of this work has been new data parallel algorithms specifically of use for direct particle simulation but which also expand the data parallel diction

Aeronautical engineering: A continuing bibliography with indexes (supplement 204)

This bibliography lists 419 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1986

Accelerating Motion Planning via Optimal Transport

Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficient decision-making. A class of methods striving to provide smooth solutions is gradient-based trajectory optimization. However, those methods usually suffer from bad local minima, while for many settings, they may be inapplicable due to the absence of easy-to-access gradients of the optimization objectives. In response to these issues, we introduce Motion Planning via Optimal Transport (MPOT) -- a \textit{gradient-free} method that optimizes a batch of smooth trajectories over highly nonlinear costs, even for high-dimensional tasks, while imposing smoothness through a Gaussian Process dynamics prior via the planning-as-inference perspective. To facilitate batch trajectory optimization, we introduce an original zero-order and highly-parallelizable update rule: the Sinkhorn Step, which uses the regular polytope family for its search directions. Each regular polytope, centered on trajectory waypoints, serves as a local cost-probing neighborhood, acting as a \textit{trust region} where the Sinkhorn Step "transports" local waypoints toward low-cost regions. We theoretically show that Sinkhorn Step guides the optimizing parameters toward local minima regions of non-convex objective functions. We then show the efficiency of MPOT in a range of problems from low-dimensional point-mass navigation to high-dimensional whole-body robot motion planning, evincing its superiority compared to popular motion planners, paving the way for new applications of optimal transport in motion planning.Comment: Published as a conference paper at NeurIPS 2023. Project website: https://sites.google.com/view/sinkhorn-step

A high-performance open-source framework for multiphysics simulation and adjoint-based shape and topology optimization

The first part of this thesis presents the advances made in the Open-Source software SU2, towards transforming it into a high-performance framework for design and optimization of multiphysics problems. Through this work, and in collaboration with other authors, a tenfold performance improvement was achieved for some problems. More importantly, problems that had previously been impossible to solve in SU2, can now be used in numerical optimization with shape or topology variables. Furthermore, it is now exponentially simpler to study new multiphysics applications, and to develop new numerical schemes taking advantage of modern high-performance-computing systems. In the second part of this thesis, these capabilities allowed the application of topology optimiza- tion to medium scale fluid-structure interaction problems, using high-fidelity models (nonlinear elasticity and Reynolds-averaged Navier-Stokes equations), which had not been done before in the literature. This showed that topology optimization can be used to target aerodynamic objectives, by tailoring the interaction between fluid and structure. However, it also made ev- ident the limitations of density-based methods for this type of problem, in particular, reliably converging to discrete solutions. This was overcome with new strategies to both guarantee and accelerate (i.e. reduce the overall computational cost) the convergence to discrete solutions in fluid-structure interaction problems.Open Acces

Aeronautical engineering: A continuing bibliography with indexes (supplement 212)

This bibliography lists 493 reports, articles and other documents introduced into the NASA scientific and technical information system in March, 1987

A compendium of computational fluid dynamics at the Langley Research Center

Through numerous summary examples, the scope and general nature of the computational fluid dynamics (CFD) effort at Langley is identified. These summaries will help inform researchers in CFD and line management at Langley of the overall effort. In addition to the inhouse efforts, out of house CFD work supported by Langley through industrial contracts and university grants are included. Researchers were encouraged to include summaries of work in preliminary and tentative states of development as well as current research approaching definitive results

Competition between transport phenomena in a Reaction-Diffusion-Convection system

This doctoral dissertation consists of three main parts. In part one, a general overview of the basic concepts of nonlinear science, nonlinear analysis and non-equilibrium thermodynamics is presented. Kinetics of chemical oscillations and the well known Belousov-Zhabotinsky reaction are also illustrated. In part two, a Reaction-Diffusion-Convection (RDC) model is introduced as a convenient framework for studying instability scenarios by which chemical oscillators are driven to chaos, along with its translation to an opportune code for numerical simulations. In part three, we report the methods and the data obtained. We observe that distinct bifurcation points are found in the oscillating patterns as Diu-sion coecients (di) or Grashof numbers (Gri) vary. Singularly there emerge peculiar bifurcation paths, inscribed in a general scenario of the RTN type, in which quasi{periodicity transmutes into a period-doubling sequence to chemical chaos. The opposite influence exhibited by the two parameters in these transitions clearly indicate that diusion of active species and natural convection are in `competition` for the stability of ordered dynamics. Moreover, a mirrored behavior between chemical oscillations and spatio-temporal dynamics is observed, suggesting that the emergence of the two observables are a manifestation of the same phenomenon. The interplay between chemical and transport phenomena instabilities is at the general origin of chaos for these systems. Further, a molecular dynamics study has been carried out for the calculation of diusion coecients of active species in the Belousov-Zhabotinsky reaction, namely HBrO2 and Ce(III), by means of mean square displacement and velocity autocorrelation function. These data have been used for a deeper comprehension of the hydrodynamic competition observed between diusion and convective motions for the stability of the system.</br