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Results for “design automation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Automated Design of Noise-Minimal, Safe Rotorcraft Trajectories

NASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and aircraft such as a 40-passenger civil tilt rotors. Rotorcraft have a number of advantages over fixed wing aircraft, primarily in not requiring direct access to the primary fixed wing runways. As such they can operate at an airport without directly interfering with major air carrier and commuter aircraft operations. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. In this paper we propose to address the rotorcraft noise problem by exploiting powerful search techniques coming from artificial intelligence, coupled with simulation and field tests, to design trajectories that are expected to improve on the amount of ground noise generated. This paper investigates the use of simulation based on predictive physical models to facilitate the search for low-noise trajectories using a class of automated search algorithms called local search. A novel feature of this approach is the ability to incorporate constraints into the problem formulation that addresses passenger safety and comfort.

Morris, Robert A.↗
DOE OSTI2024

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein–protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein–protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning–based methods to unlock the widespread application of designed protein–protein interfaces and self-assembling protein nanomaterials in biotechnology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Automated Design of Neuromorphic Networks for Scientific Applications at the Edge

Designing spiking neural networks for neuromorphic deployment is a non-trivial task. It is further complicated when there are resource constraints for the neuromorphic implementation, such as size or power constraints, that may be present in edge applications. In this work, we utilize a previously presented approach, EONS, to design spiking neural networks for a memristive neuromorphic implementation for scientific data applications. We specifically use a multi-objective approach in EONS to maximize network accuracy on the scientific data application task, but also to minimize network size and energy. We illustrate that EONS determines both the network structure and the parameters, removing the burden from the user on determining the appropriate spiking neural network structure, and we show that the resulting networks are very different from the layered structure of typical neural networks. Finally, we show that the multi-objective approach produces smaller, more energy efficient networks than the original EONS approach and produces comparable accuracy to a back-propagation style training approach.

Schuman, Catherine↗
DOE OSTI2021

Semi-automated Design of Artificial Intelligence Earth Systems Models

Prediction and observation of water cycles at various scales involve not only patterns isolated in space and time, but also modeling of complex spatio-temporal relationships across multiple domains. For instance, evapotranspiration (ET) and leaf area indexes (LAI) are two parameters that are needed to accurately model and understand land-atmosphere processes. Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFTs), which fail to capture fine-scale variations because of changes in vegetation type across the globe. Similarly LAI estimates have typically been derived from vegetation indices at global scales or estimated locally using physical models, both of which suffer from a range of uncertainties that impact model sensitivity. The new era of AI model development for Earth systems (ES) calls for data-driven methods that provide domain scientists with uncertainty-aware estimations of biophysical parameters such as ET and LAI in a generalizable, interpretable, and discoverable manner.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards Automated Design of Polymer Precursors for Rational Design of Porous Carbon-Materials [Slides]

Overview: Developed capability enables easy building of realistic polymer structures and a straightforward evaluation of polymer properties using different computational approaches. Topics of relevance to LANL mission: fundamental understanding of gas, vapor, ion, and liquid transport in polymer materials with applications in carbon capture, clean energy, hydrogen production (e.g., fuel cells and electrolyzes); Toxic element removal; Design of polymer binders in explosives. Workforce Development: Project provided a valuable opportunity to for a PI to manage a LANL research effort, expand collaborations (e.g., MPA division), and establish research topic that aligns with the LANL mission; The capability built was highlighted in the proposal "Versatile Polyolefins-Based Ionomer Platform for Next Generation Polymer Electrolyte Membrane and Hydrogen Production" lead by InnoSense LLC in collaboration with MPA-1 and T-1 as a response to DOE's call DE-FOA-0003003: Science Foundations for Energy Earthshots.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Automated design and optimization of flexible booster autopilots via linear programming. Volume 2: User's manual

COEBRA, the Computerized Optimization of Elastic Booster Autopilots, is an autopilot design program. The bulk of the design criteria is presented in the form of minimum allowed gain/phase stability margins. COEBRA has two optimization phases: (1) a phase to maximize stability margins; and (2) a phase to optimize structural bending moment load relief capability in the presence of minimum requirements on gain/phase stability margins.

Hauser, F. D.↗
NASA NTRS1979

Automated design using numerical optimization

Numerical optimization concepts are described with limited technical detail. The purpose is to provide the nonspecialist with sufficient information to judge the applicability of these methods to his particular design problem. The concepts are first described in physical terms to give a basic understanding of the iterative procedure employed by these methods. Next, the typical engineering task is presented and converted to a form ammenable to solution by numerical optimization. Basic algorithms for solving this problem are identified. Numerous applications are referenced, emphasizing the structural design discipline. The state of the art allows for the routine solution of nonlinear design problems of approximately 20 independent variables subject to 100 or more constraints. In many applications, much larger design problems may be solved. Selected references are provided which describe the methods and applications in more detail.

Vanderplaats, G. N.↗
NASA NTRS1982

Automated design of minimum drag light aircraft fuselages and nacelles

The constrained minimization algorithm of Vanderplaats is applied to the problem of designing minimum drag faired bodies such as fuselages and nacelles. Body drag is computed by a variation of the Hess-Smith code. This variation includes a boundary layer computation. The encased payload provides arbitrary geometric constraints, specified a priori by the designer, below which the fairing cannot shrink. The optimization may include engine cooling air flows entering and exhausting through specific port locations on the body.

Smetana, F. O.↗