Incompressible Navier-Stokes computations for a round-edged double-delta wing
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Hsu, C.-H.↗
SEARCH · Engineering Papers
Results for “Edge Computing”
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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Hierarchical approach to reconstruct surfaces by using iteratively rectified imagery
A new approach to reconstruct the 3D surface of the object space from digital images is described. All the object points obtained by an automatic orientation procedure lead to a first approximation of the surface. Edges are computed for one image and matched to the other image by grey level correlation or least-squares matching through the scale space. To every discrete step in the scale space there exists the digital stereopair (image pyramid), the corresponding surface digital elevation model (DEM), and the warped images. The warped images in this discrete scale space representation correspond to digital orthophotos obtained from the DEMs that result from matching the image pyramid. The warped images will be used on every successive level in the image pyramid in order to reduce the foreshortening problems associated with any area-based matching method.
Schenk, Toni↗
An Integrated Tool for the Coupled Thermal and Mechanical Analysis of Pyrolyzing Heatshield Materials
Materials that pyrolyze at elevated temperature have been commonly used as thermal protection materials in hypersonic flight, and advanced pyrolyzing materials for this purpose continue to be developed. Because of the large temperature gradients that can arise in thermal protection materials, significant thermal stresses can develop. Advanced applications of pyrolytic materials are calling for more complex heatshield configurations, making accurate thermal stress analysis more important, and more challenging. For non-pyrolyzing materials, many finite element codes are available and capable of performing coupled thermal-mechanical analyses. These codes do not, however, have a built-in capability to perform analyses that include pyrolysis effects. When a pyrolyzing material is heated, one or more components of the original virgin material pyrolyze and create a gas. This gas flows away from the pyrolysis zone to the surface, resulting in a reduction in surface heating. A porous residue, referred to as char, remains in place of the virgin material. While the processes involved can be complex, it has been found that a simple physical model in which virgin material reacts to form char and pyrolysis gas, will yield satisfactory analytical results. Specifically, the effects that must be modeled include: (1) Variation of thermal properties (density, specific heat, thermal conductivity) as the material composition changes; (2) Energy released or absorbed by the pyrolysis reactions; (3) Energy convected by the flow of pyrolysis gas from the interior to the surface; (4) The reduction in surface heating due to surface blowing; and (5) Chemical and mass diffusion effects at the surface between the pyrolysis gas and edge gas Computational tools for the one-dimensional thermal analysis these materials exist and have proven to be reliable design tools. The objective of the present work is to extend the analysis capabilities of pyrolyzing materials to axisymmetric configurations, and to couple thermal and mechanical analyses so that thermal stresses may be efficiently and accurately calculated.
Pronchick, Stephen W.↗
Arteriosclerosis Detection
The Veterans Administration Hospital used computer image-processing techniques to detect arteriosclerosis. Originally developed by Goddard Space Flight Center engineers to enhance spacecraft pictures, this device replaced the previous testing for this disease which was extremely painful and time consuming. With this instrument, computer detected edges are shown along with an estimate of location of pre-arteriosclerosis vessel wall. The difference between the two represents the relative amount of disease in the blood vessel. Instrumentation will be expanded again in 1976 to analyze the coronary arteries and the blood vessels of the retina.
Source record↗
DRF: A Software Architecture for a Data Marketplace to Support Advanced Air Mobility
Advanced Air Mobility is a new aviation vision, where unmanned aerial systems will trans- port passengers and cargo across urban and rural areas. Critical to the realization of this vision is the development of a digital marketplace, which allows service providers and consumers operating in the airspace ecosystem to securely exchange data and reasoning insights. In this paper, we present the architecture of a decentralized data marketplace that connects data and reasoning service providers to vehicles and other service consumers along the cloud-to-edge continuum. We also present two example use cases to demonstrate the value of our approach.
Autonomy↗
How efficiently can AI recognize Wireless Devices?
This poster presents a hardware benchmarking methodology for a 3-layer CNN waveform classifier deployed using ONNX Runtime on an NVIDIA Jetson AGX Orin. The dataset consist of 9 signal types, -30 to +30 dB SNR with 5dB increments. Benchmarking on the Jetson AGX Orin gave an accuracy of 91.9% and GPU throughput of 107,120 predictions/sec (23× faster than CPU). The Jetson GPU reached approximately 27M samples/sec with stable performance but fell below the 40 MHz rate needed for real-time radio feeds. Sustained testing of 5 minutes confirmed stable performance with no memory leaks, establishing a reproducible benchmarking baseline for future edge-deployment optimization.
99 - GENERAL AND MISCELLANEOUS↗
Sensing Electrical Networks Securely & Economically (SENSE)
The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility
The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.
Edge computing↗
Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility
The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.
Edge computing↗
Improving CAP-TSD steady pressure solutions through airfoil slope modification
A modification of airfoil section geometry is examined for improvement of the leading edge pressures predicted by the Computational Aeroelasticity Program - Transonic Small Disturbance (CAP-TSD). Results are compared with Eppler solutions to assess improvement. Preliminary results indicate that a fading function modification of section slopes is capable of significant improvements in the pressures near the leading edge computed by CAP-TSD. Application of this modification to airfoil geometry before use in CAP-TSD is shown to reduce the nonphysical pressure peak predicted by the transonic small disturbance solver. A second advantage of the slope modification is the substantial reduction in sensitivity of CAP-TSD steady pressure solutions to the computational mesh.
Mitterer, Kent F.↗
Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design
Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.
Kim, Yoon G.↗
A Computer-Aided Approach for Designing Edge-Slot Waveguide Arrays
Traditional techniques for designing resonant edge-slot waveguide arrays have required an iterative trial-and-error process of measuring slot data from several prototypes. Since very little meaningful data has been published, this technology remains relatively immature and prohibitive for many smaller programs that could benefit from some advantages this antenna has to offer. A new Computer-Aided Design technique for designing resonant edge-slot waveguide arrays was used to successfuliy design such an X-band radiometer antenna for the NASA Light Rainfall Radiometer (LRR) instrument. Having the ability to rapidly create such an extremely accurate and efficient antenna design without the need to manufacture prototypes has also enabled inexpensive research that promises to improve the system-level performance of microwave radiometers for upcoming space-flight missions. This paper will present details of the LRR antenna design and describe some other current edge-slot array accomplishments at Goddard Space Flight Center.
Gosselin, Renee Brian↗
Subsonic aerodynamic characteristics of interacting lifting surfaces with separated flow around sharp edges predicted by a vortex-lattice method
Because the potential flow suction along the leading and side edges of a planform can be used to determine both leading- and side-edge vortex lift, the present investigation was undertaken to apply the vortex-lattice method to computing side-edge suction force for isolated or interacting planforms. Although there is a small effect of bound vortex sweep on the computation of the side-edge suction force, the results obtained for a number of different isolated planforms produced acceptable agreement with results obtained from a method employing continuous induced-velocity distributions. By using the method outlined, better agreement between theory and experiment was noted for a wing in the presence of a canard than was previously obtained.
Lamar, J. E.↗
Comparison of measured and computed pitot pressures in a leading edge vortex from a delta wing
Calculations are presented for a 75-deg swept flat plate wing tested at a freestream Mach number of 1.95 and 10 degrees angle of attack. Good agreement is found between computational data and previous experimental pitot pressure measurements in the core of the vortex, suggesting that the total pressure losses predicted by the Euler equation solvers are not errors, but realistic predictions. Data suggest that the magnitude of the total pressure loss is related to the circumferential velocity field through the vortex, and that it increases with angle of attack and varies with Mach number and sweep angle.
Murman, Earll M.↗
Ab Initio Methods for L-edge X-ray Absorption Spectroscopy
The theoretical prediction of X-ray absorption spectra (XAS) has become common- place in electronic structure theory. The ability to better model and understand L-edge spectra is of great interest in the study of transition metal complexes and a wide variety of solid state materials. However, until recently few rst-principles works have mod- eled L-edge XAS due to the presence of strong spin-orbit coupling in the 2p orbitals which splits the observed peaks into multiple groups of features. Therefore, a proper description of spin-orbit coupling is vital for the successful prediction of L-edge spectra. A number of new approaches that incorporate spin-orbit coupling have recently made advances in the computation of L-edge spectra. In this review, we describe recent work in computational L-edge XAS and how these methods may continue to improve in the future. Comparison of the advantages and disadvantages of the various approaches are considered, with special attention to not only the computational cost of the level of theory, but also the various approaches that can be used to compute the absorption spectra with a large number of high energy excited states.
Kasper, Joseph M.↗
VORCOR: A computer program for calculating characteristics of wings with edge vortex separation by using a vortex-filament and-core model
A computer code base on an improved vortex filament/vortex core method for predicting aerodynamic characteristics of slender wings with edge vortex separations is developed. The code is applicable to camber wings, straked wings or wings with leading edge vortex flaps at subsonic speeds. The prediction of lifting pressure distribution and the computer time are improved by using a pair of concentrated vortex cores above the wing surface. The main features of this computer program are: (1) arbitrary camber shape may be defined and an option for exactly defining leading edge flap geometry is also provided; (2) the side edge vortex system is incorporated.
Pao, J. L.↗
Towards FAIR Workflows for Federated Experimental Sciences
A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning pipelines spanning edge, High Performance Computing, and cloud environments. With a specific example of end-to-end microscopy algorithm and datasets, the proposed method shows how to enable reproducibility, audit trail, provenance of metadata artifacts. The emerging needs of automation in experimental sciences, ML-centric workflows, and FAIR metadata management across federated compute environments is addressed.
machine learning↗
Editorial: Advances in computational molecular biophysics
This special issue of BBA is dedicated to Dr. Jeremy C. Smith on the occasion of his 60th birthday and his more than three decades as a practitioner of computational molecular biophysics. The special issue showcases cutting-edge work in computational molecular biophysics. The senior authors of the manuscripts in this issue are Dr. Smith’s previous or present collaborators. Their articles reflect Dr. Smith’s distinguished career integrating experimental and molecular simulation approaches into a unified description of the function, structure, and dynamics of biomolecules. Each of the articles presented here illustrates one aspect of the broad nature of computational molecular biophysics.
59 BASIC BIOLOGICAL SCIENCES↗