Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PEREGRINE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2025 Peregrine in-situ monitoring and training dataset for laser powder bed fusion and binder jet printers

Peregrine, a software tool developed at Oak Ridge National Laboratory (ORNL), was used to collect and analyze in-situ monitoring (ISM) data from a Concept Laser M2 (Colibrium Additive) laser powder bed fusion (L-PBF) printer and an ExOne Innovent (Desktop Metal) binder jet printer. Data for four builds (print jobs) were saved to HDF5 (high performance data) files for release. Additionally, process anomalies were annotated by the authors across 37 image stacks (i.e., print layers) and are also provided as HDF5 files.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Intraspecific evolutionary relationships among peregrine falcons in western North American high latitudes

Subspecies relationships within the peregrine falcon (Falco peregrinus) have been long debated because of the polytypic nature of melanin-based plumage characteristics used in subspecies designations and potential differentiation of local subpopulations due to philopatry. In North America, understanding the evolutionary relationships among subspecies may have been further complicated by the introduction of captive bred peregrines originating from non-native stock, as part of recovery efforts associated with mid 20th century population declines resulting from organochloride pollution. Alaska hosts all three nominal subspecies of North American peregrine falcons–F. p. tundrius, anatum, and pealei–for which distributions in Alaska are broadly associated with nesting locales within Arctic, boreal, and south coastal maritime habitats, respectively. Unlike elsewhere, populations of peregrine falcon in Alaska were not augmented by captive-bred birds during the late 20th century recovery efforts. Population genetic differentiation analyses of peregrine populations in Alaska, based on sequence data from the mitochondrial DNA control region and fragment data from microsatellite loci, failed to uncover genetic distinction between populations of peregrines occupying Arctic and boreal Alaskan locales. However, the maritime subspecies, pealei, was genetically differentiated from Arctic and boreal populations, and substructured into eastern and western populations. Levels of interpopulational gene flow between anatum and tundrius were generally higher than between pealei and either anatum or tundrius. Estimates based on both marker types revealed gene flow between augmented Canadian populations and unaugmented Alaskan populations. While we make no attempt at formal taxonomic revision, our data suggest that peregrine falcons occupying habitats in Alaska and the North Pacific coast of North America belong to two distinct regional groupings–a coastal grouping (pealei) and a boreal/Arctic grouping (currently anatum and tundrius)– each comprised of discrete populations that are variously intra-regionally connected.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Supplementary Feeding, Plumage Documentation and Early Season Prey of Peregrine Falcons at the New Mexico Alpha Eyrie

A review of what is known about avian physiology and the biological effects of DDE suggests that some benefit to peregrine falcon egg condition could be attained by artificially feeding DDE free prey to the female from the time of her arrival on the nesting grounds until completion of egg laying; the magnitude of potential benefit is unknown. Sporadic efforts in the past demonstrated the need for precision methods of prey delivery. Two methods were developed and tried; providing dead prey items by dropping them in a day perch, and delivery of live prey by remotely controlled release from compartments positioned at the top of the cliff occupied by the falcons. Maintaining quail in the day perch for 21 days resulted in at least one and probably two meals for the female peregrine. Of 16 live birds released (mostly pigeons) 13 were pursued and three caught. Blinding the pigeons with tape proved to be necessary to enable capture. Also, some reluctance of the male peregrine to attack pigeons was observed, and problems with equipment, visibility, and the proximity of the falcons to the release box were encountered. Manpower was the most significant resource requirement. Baiting of great-horned owls, possibly leading to owl attack on the falcons, is judged to be the largest detrimental effect of supplemental feeding. It is recommended that supplemental feeding be reserved for falcons or eyries where complete reproductive failure is expected. Plumage documentation photography was successfully conducted by a remotely controlled camera as an aid to identification of individual falcons. American robin, red-winged blackbird, starling, white-throated swift, bluebird, and mourning dove were among natural prey consumed by the peregrines before completion of egg laying. All activities in close proximity to the cliff were conducted at night to preclude direct disturbance of the falcons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Peregrine Software Development: Report on the Code Conversion From Python to C++

This work package seeks to convert the Peregrine software tool from its original Python implementation to a production version based on the C++ language. Peregrine is a powerful research platform with a multitude of advanced data analytics and data visualization functionalities. Developed by scientists to explore multimodal and multidimensional data related to the production of components using powder bed additive manufacturing processes, the tool implements state-of-the-art algorithms to assist machine users in making build or part quality determinations. Given that Peregrine is data-intensive, the goal of this conversion is to enhance the tool’s flexibility and interactivity and reduce the number of code dependencies to facilitate its deployment as part of the ongoing technology transfer campaign. This brief document provides an overview of Peregrine’s functionalities and capabilities, along with a detailed description of the core functionalities that have been implemented to date in the new C++ version. This document serves as a development update at the end of the first year of the ongoing conversion and will be regularly updated as progress continues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Advanced Pellet Cladding Interaction Modeling Using the US DOE CASL Fuel Performance Code: Peregrine

The US DOE’s Consortium for Advanced Simulation of LWRs (CASL) program has undertaken an effort to enhance and develop modeling and simulation tools for a virtual reactor application, including high fidelity neutronics, fluid flow/thermal hydraulics, and fuel and material behavior. The fuel performance analysis efforts aim to provide 3-dimensional capabilities for single and multiple rods to assess safety margins and the impact of plant operation and fuel rod design on the fuel thermomechanical- chemical behavior, including Pellet-Cladding Interaction (PCI) failures and CRUD-Induced Localized Corrosion (CILC) failures in PWRs. [1-3] The CASL fuel performance code, Peregrine, is an engineering scale code that is built upon the MOOSE/ELK/FOX computational FEM framework, which is also common to the fuel modeling framework, BISON [4,5]. Peregrine uses both 2-D and 3-D geometric fuel rod representations and contains a materials properties and fuel behavior model library for the UO2 and Zircaloy system common to PWR fuel derived from both open literature sources and the FALCON code [6]. The primary purpose of Peregrine is to accurately calculate the thermal, mechanical, and chemical processes active throughout a single fuel rod during operation in a reactor, for both steady state and off-normal conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Advanced Pellet-Cladding Interaction Modeling using the US DOE CASL Fuel Performance Code: Peregrine

The US DOE’s Consortium for Advanced Simulation of LWRs (CASL) program has undertaken an effort to enhance and develop modeling and simulation tools for a virtual reactor application, including high fidelity neutronics, fluid flow/thermal hydraulics, and fuel and material behavior. The fuel performance analysis efforts aim to provide 3-dimensional capabilities for single and multiple rods to assess safety margins and the impact of plant operation and fuel rod design on the fuel thermo-mechanical-chemical behavior, including Pellet-Cladding Interaction (PCI) failures and CRUD-Induced Localized Corrosion (CILC) failures in PWRs. [1-3] The CASL fuel performance code, Peregrine, is an engineering scale code that is built upon the MOOSE/ELK/FOX computational FEM framework, which is also common to the fuel modeling framework, BISON [4,5]. Peregrine uses both 2-D and 3-D geometric fuel rod representations and contains a materials properties and fuel behavior model library for the UO2 and Zircaloy system common to PWR fuel derived from both open literature sources and the FALCON code [6]. The primary purpose of Peregrine is to accurately calculate the thermal, mechanical, and chemical processes active throughout a single fuel rod during operation in a reactor, for both steady state and off-normal conditions.

CASL, Consortium for Advanced Simulation of LWRs, ↗
DOE OSTI2023

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

This dataset contains layer-wise powder bed images from three different powder bed printing technologies – laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-09)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Tensile Properties Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-11)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from five Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L builds containing 6,299 SS-J3 individually tracked tensile coupons. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, room-temperature static tensile test results, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-10)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, X-Ray Computed Tomography (X-CT) scans, pycnometry and tensile test results, etched micrographs from selected locations, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Peregrine

The purpose of “Peregrine” is to perform in-situ quality control of the additive manufacturing process in powder bed systems. The software is an AI-based method to automatically detect and map multiple classes of defects present in the top layer of a powder bed system, either after material deposition, i.e. raking, or after printing the layer. Relying on image data or sensing modalities that can be converted into image data, the code allows for: (1) various steps of pre-processing of the data (registration, distortion correction, etc), (2) annotation of training datasets, (3) processing of data inline or offline, (4) creating 3D rendering of the defect map, and (5) implementation of corrective actions if allowed by the manufacturing platform.

Paquit, Vincent C↗
DOE OSTI2022

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)

This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, DMREF, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)

This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Modulational instability and associated rogue structures of slow magnetosonic wave in Hall magnetohydrodynamic plasmas

The modulational instability and associated rogue structures of a slow magnetosonic wave are investigated for a Hall magnetohydrodynamic plasma. Nonlinear Schrodinger equation is obtained by using the multiple scale method, which shows a modulationally unstable slow magnetosonic mode evolving into bright wavepackets. The dispersive effects induced by the Hall electron current increase with the increase in plasma β and become weaker as the angle of propagation increases. The growth rate of the modulational instability also increases with the increase in plasma β. The growth rate is greatest for the parallel propagation and drops to zero for perpendicular propagation. The envelope wavepacket of a slow magnetosonic is widened with less oscillations as plasma β increases. But the wavepacket becomes slightly narrower and more oscillatory as the angle of propagation increases. Further a non-stationary envelope solution of the Peregrine soliton is analyzed for rogue waves. The Peregrine soliton contracts temporally and expands spatially with increase in plasma β. However, the width of a slow magnetosonic Peregrine soliton decreases both temporally and spatially with increase of the propagation angle.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗