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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

SPOT Program

A Spacecraft Position Optimal Tracking (SPOT) program was developed to process Global Positioning System (GPS) data, sent via telemetry from a spacecraft, to generate accurate navigation estimates of the vehicle position and velocity (state vector) using a Kalman filter. This program uses the GPS onboard receiver measurements to sequentially calculate the vehicle state vectors and provide this information to ground flight controllers. It is the first real-time ground-based shuttle navigation application using onboard sensors. The program is compact, portable, self-contained, and can run on a variety of UNIX or Linux computers. The program has a modular objec-toriented design that supports application-specific plugins such as data corruption remediation pre-processing and remote graphics display. The Kalman filter is extensible to additional sensor types or force models. The Kalman filter design is also strong against data dropouts because it uses physical models from state and covariance propagation in the absence of data. The design of this program separates the functionalities of SPOT into six different executable processes. This allows for the individual processes to be connected in an a la carte manner, making the feature set and executable complexity of SPOT adaptable to the needs of the user. Also, these processes need not be executed on the same workstation. This allows for communications between SPOT processes executing on the same Local Area Network (LAN). Thus, SPOT can be executed in a distributed sense with the capability for a team of flight controllers to efficiently share the same trajectory information currently being computed by the program. SPOT is used in the Mission Control Center (MCC) for Space Shuttle Program (SSP) and International Space Station Program (ISSP) operations, and can also be used as a post -flight analysis tool. It is primarily used for situational awareness, and for contingency situations.

Smith, Jason T.↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2010

Request-Driven Schedule Automation for the Deep Space Network

The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.

Johnston, Mark D.↗
NASA NTRS2016

Simulation of Mission Phases

This position with the Simulation and Graphics Branch (ER7) at Johnson Space Center (JSC) provided an introduction to vehicle hardware, mission planning, and simulation design. ER7 supports engineering analysis and flight crew training by providing high-fidelity, real-time graphical simulations in the Systems Engineering Simulator (SES) lab. The primary project assigned by NASA mentor and SES lab manager, Meghan Daley, was to develop a graphical simulation of the rendezvous, proximity operations, and docking (RPOD) phases of flight. The simulation is to include a generic crew/cargo transportation vehicle and a target object in low-Earth orbit (LEO). Various capsule, winged, and lifting body vehicles as well as historical RPOD methods were evaluated during the project analysis phase. JSC core mission to support the International Space Station (ISS), Commercial Crew Program (CCP), and Human Space Flight (HSF) influenced the project specifications. The simulation is characterized as a 30 meter +V Bar and/or -R Bar approach to the target object's docking station. The ISS was selected as the target object and the international Low Impact Docking System (iLIDS) was selected as the docking mechanism. The location of the target object's docking station corresponds with the RPOD methods identified. The simulation design focuses on Guidance, Navigation, and Control (GNC) system architecture models with station keeping and telemetry data processing capabilities. The optical and inertial sensors, reaction control system thrusters, and the docking mechanism selected were based on CCP vehicle manufacturer's current and proposed technologies. A significant amount of independent study and tutorial completion was required for this project. Multiple primary source materials were accessed using the NASA Technical Report Server (NTRS) and reference textbooks were borrowed from the JSC Main Library and International Space Station Library. The Trick Simulation Environment and User Training Materials version 2013.0 release was used to complete the Trick tutorial. Multiple network privilege and repository permission requests were required in order to access previous simulation models. The project was also an introduction to computer programming and the Linux operating system. Basic C++ and Python syntax was used during the completion of the Trick tutorial. Trick's engineering analysis and Monte Carlo simulation capabilities were observed and basic space mission planning procedures were applied in the conceptual design phase. Multiple professional development opportunities were completed in addition to project duties during this internship through the System for Administration, Training, and Education Resources for NASA (SATERN). Topics include: JSC Risk Management Workshop, CCP Risk Management, Basic Radiation Safety Training, X-Ray Radiation Safety, Basic Laser Safety, JSC Export Control, ISS RISE Ambassador, Basic SharePoint 2013, Space Nutrition and Biochemistry, and JSC Personal Protective Equipment. Additionally, this internship afforded the opportunity for formal project presentation and public speaking practice. This was my first experience at a NASA center. After completing this internship I have a much clearer understanding of certain aspects of the agency's processes and procedures, as well as a deeper appreciation from spaceflight simulation design and testing. I will continue to improve my technical skills so that I may have another opportunity to return to NASA and Johnson Space Center.

Carlstrom, Nicholas Mercury↗
DOE OSTI2017

Order priors for Bayesian network discovery with an application to malware phylogeny

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges) in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded operator models that comprise MIDAS. Following the initial settings, the model progresses to begin the first tasks required of the two flight deck operators, the Captain (CA) and the First Officer (FO). The task sets will initialize operator specific settings prior to loading all of the alerts, probes, and other events that occur in the scenario. As a note, the CA and FO were terms used in developing this model but the CA can also be thought of as the Pilot Flying (PF), while the FO can be considered the Pilot-Not-Flying (PNF)or Pilot Monitoring (PM). As such, the document refers to the operators as PFCA and PNFFO respectively.

task network model↗
NASA NTRS2013

Communications Dashboard (Control Rooms Take a Cue from Facebook), Chapter 1

Papers published via IEEE and AIAA conferences have presented an overview of how social media could benefit NASA working environments in general and proposed three specific social applications to benefit space flight control operations. One of them, Communications Dashboard, would help a real time flight controller keep up with both the "big picture" and significant details of operations via a cohesive interface similar to those of social networking services (SNS). Instead of recreational social features, "CommDash" would support functions like console logging, categorized and threaded text chat streams with enhanced accountability and graphics display features, high-level status displays driven by telemetry or other events, and an on-screen hailing function for requesting voice or text stream conversation. Moving certain voice conversations to text streams would reduce confusion and stress in two ways. Within text conversations, there would be far less repetition of content since text conversations have visual persistence and are reviewable instantly, e.g., there s no need to brief new participants to a discussion -- they just read what s already there. Remaining voice traffic would stand out more clearly, and quieter voice loops means fewer "say again" calls and less distraction from visual and mental tasks, thus less stress. (Most flight controllers monitor 4 or 5 voice loops at once.) Links could be created from console log entries to chat selections so that underlying details are readily available yet unobtrusive. This would reduce the confusion that rises from having multiple and sometimes divergent copies of the same information due to cut/copy and paste operations, attachments, and asynchronous editing. This concept could apply to a plethora of real time control environments and to other settings with lots of information juggling. This paper explores the dashboard concept in further detail and chronicles the first phase of a NASA IT Labs (Information Technology) project that could lead to a working system

Scott, David w.↗
NASA NTRS1995

3-D Reconstruction of Macular Type II Cell Innervation Patterns in Space-Flight and Control Rats

A semiautomated method for reconstructing objects from serial thin sections has been developed in the Biocomputation Center. The method is being used to completely, for the first time, type II hair cells and their innervations. The purposes are to learn more about the fundamental circuitry of the macula on Earth and to determine whether changes in connectivities occur under space flight conditions. Data captured directly from a transmission electron microscope via a video camera are sent to a graphics workstation. There, the digitized micrographs are mosaicked into sections and contours are traced, registered and displayed by semiautomated methods. Current reconstructions are of type II cells from the medial part of rat maculas collected in-flight on the Space Life Sciences-2 mission, 4.5 hrs post-flight, and from a ground control. Results show that typical type II cells receive processes from tip to six nearby calyces or afferents. Nearly all processes are elongated and have bouton-like enlargements; some have numerous vesicles. Multiple (2 to 4) processes from a single calyx to a type II cell are common, and approximately 1/3 of the processes innervale 2 or 3 type II cells or a neighboring cluster. From 2% to 6% of the cells resemble type I cells morphologically but have demi-calyces. Thus far, increments in synaptic number in type II cells of flight rats are prominent along processes that supply two hair cells. It is clear that reconstruction methods provide insights into details of macular circuitry not obtainable by other techniques. The results demonstrate a morphological basis for interactions between adjacent receptive fields through feed back-feed forward connections, and for dynamic alterations in receptive field range and activity during preprocessing of linear acceleratory information by the maculas. The reconstruction method we have developed will find further applications in the study of the details of neuronal architecture of more complex systems, to seek out shared organizational properties or neuronal networks and to understand better localization of synaptic changes in altered environments.

Ross, Muriel Dorothy↗
DOE OSTI2015

In situ ligand generation for novel Mn(II) and Ni(II) coordination polymers with disulfide ligand: Solvothermal syntheses, structures and magnetic properties

Two coordination polymers, ([Mn{sub 2}(L1){sub 2}(μ{sub 2}-H{sub 2}O)(H{sub 2}O){sub 4}]·5H{sub 2}O){sub n}1 and ([Ni(L1)(H{sub 2}O){sub 2}]·2H{sub 2}O){sub n}2 (H{sub 2}L1=2,2′-dithiobisnicotinic acid), were prepared by the solvothermal reactions of the Mn(II) or Ni(II) ions with 2-mercaptonanicotinic acid. In 1, the [Mn{sub 2}(COO){sub 4}] units are connected by the 2,2′-dithiobisnicotinic dianion to form a two-dimensional (4,4)-connected network. In 2, the adjacent Ni(II) ions are connected by the carboxyl groups of the 2,2′-dithiobisnicotinic dianion to form an one-dimensional inorganic rod-shaped chain [Ni(COO){sub 2}]{sub n}, which are further interconnected by the 2,2′-dithiobisnicotinic ligand, giving rise to a two-dimensional framework. Variable-temperature magnetic susceptibilities of 1 and 2 exhibit overall weak antiferromagnetic coupling between the adjacent metal ions. - Graphical abstract: Two 2D coordination polymers were synthesized by transition-metal/in-situ oxidation of 2-mercaptonicotinic acid. The compounds pack into 2D frameworks by the carboxyl groups of 2,2′-dithiobisnicotinic dianion and exhibit overall weak antiferromagnetic coupling. - Highlights: • Two 2D coordination polymers containing 2,2′-dithiobisnicotinic dianion. • In situ oxidation and dehydro coupling reaction of 2-mercaptonbenzoic acid. • Two compounds display weak antiferromagnetic exchanges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Al{sub 15}Ge{sub 4}Ni{sub 3}: A new intergrowth structure with Cu{sub 3}Au- and CaF{sub 2}-type building blocks

The new ternary compound Al{sub 15}Ge{sub 4}Ni{sub 3} (τ{sub 2} in the system Al–Ge–Ni) was synthesized in single crystalline form by a special annealing procedure from samples located in the three phase fields [L+Al+τ{sub 2}] and [L+Ge+τ{sub 2}]. The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} was determined by single-crystal X-ray diffraction. The compound crystallizes in a new structure type in space group I4-bar3m, Pearson Symbol cI88, cubic lattice parameter a=11.405(1) Å. Phase diagram investigations indicate stoichiometric composition without considerable homogeneity range; τ{sub 2} melts peritectically at T=444 °C. The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} shows a unique combination of simple Cu{sub 3}Au- and CaF{sub 2}-type building blocks: a three dimensional network of CaF{sub 2}-type units, formed by Ni and Al atoms, is interspaced by clusters (Al{sub 6}Ge{sub 8}) resembling unit cells of the Cu{sub 3}Au-type. Both structural motifs are connected by Al–Ge bonds. The ground state energy of the compound was obtained by DFT calculations and the densities of states were analyzed in detail. In addition, electron density maps were calculated in four different sections through the unit cell using the full potential linearized augmented plane-wave (FLAPW) method. The bonding situation in Al{sub 15}Ge{sub 4}Ni{sub 3} was discussed combining results from electronic calculations with the analysis of the coordination of atoms. - Graphical abstract: The new compound Al{sub 15}Ge{sub 4}Ni{sub 3} shows a unique combination of simple Cu{sub 3}Au- and CaF{sub 2}-type building blocks. - Highlights: • The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} (space group I4-bar3m) was determined. • It shows a unique combination of CaF{sub 2}- and Cu{sub 3}Au-type building blocks. • Electronic (DFT) calculations were performed to gain insight to chemical bonding.

36 MATERIALS SCIENCE↗
NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗
DOE OSTI2013

Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} and Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63}: The diversity of pseudo 5-fold symmetries in the Na–Au–Ga system

The Na-rich part (∼30% Na) of the Na–Au–Ga system between NaAu{sub 2}, NaGa{sub 4}, and Na{sub 22}Ga{sub 39} has been found to contain the ternary phases Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} (I) and Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63} (II), according to the results of single crystal X-ray diffraction measurements. I is orthorhombic, Cmcm, a=5.3040(1), b=24.519(5), c=14.573(3) Å, and contains a network of clusters with local 5-fold symmetry along the a-axis. Such clusters are frequent building units in decagonal quasicrystals and their approximants. II is rhombohedral, R3{sup ¯}m, a=16.325(2), c=35.242(7) Å, and contains building blocks that are structurally identical to the Bergman-type clusters as well as fused icosahedral units known with active metals, triels and late transition elements. II also contains a polycationic network with elements of the clathrate V type structure. Tight-binding electronic structure calculations using linear muffin–tin-orbital (LMTO) methods on idealized models of I and II indicate that both compounds are metallic with evident pseudogaps at the corresponding Fermi levels. The overall Hamilton bond populations are generally dominated by Au–Ga and Au–Au bonds in I and by Ga–Ga bonds in II; moreover, the Na–Au and Na–Ga contributions in I are unexpectedly large, ∼20% of the total. A similar involvement of sodium in covalent bonding has also been found in the electron-richer i-Na{sub 13}Au{sub 12}Ga{sub 15} quasicrystal approximant. - Graphical abstract: Multiply-endohedral Bergman-related clusters in the structure of Na{sub 17}Au{sub 5.9(1)}Ga{sub 46.6.} Display Omitted - Highlights: • Two new compounds with the local 5-fold symmetry have been investigated. • Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} is an orthorhombic approximant of the Na{sub 13}Au{sub 12}Ga{sub 15} quasicrystal. • Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63} represents a rhombohedral distortion of the Bergman-type phases.

36 MATERIALS SCIENCE↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

The structural influence of aluminium ions on emission characteristics of Sm{sup 3+} ions in lead aluminium silicate glass system

Graphical abstract: The optical absorption and photoluminescence spectra of Sm ions in PbO-Al{sub 2}O{sub 3}-SiO{sub 2} glasses mixed with different concentrations of Al{sub 2}O{sub 3} have been investigated. From these spectra, the emission probabilities and also fluorescence lifetime principal transition viz., {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} of Sm{sup 3+} ions of has been evaluated. The analysis of results of these studies has indicated that there is a less radiative trapping and enhanced fluorescence lifetime and high quantum efficiency in the glasses mixed with 8.0 mol% of Al{sub 2}O{sub 3}. Highlights: Black-Right-Pointing-Pointer Glasses of the composition (40 - x)PbO-(5 + x)Al{sub 2}O{sub 3}-54SiO{sub 2}:1.0Sm{sub 2}O{sub 3} with x = 5-10 mol% were prepared. Black-Right-Pointing-Pointer Spectroscopic properties (OA, PL and IR) were investigated. Black-Right-Pointing-Pointer Emission probability, lifetime, branching ratio of {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} transition of Sm{sup 3+} have been evaluated. Black-Right-Pointing-Pointer Analysis of the results indicated that glass mixed with 8.0 mol% exhibits high luminescence efficiency. -- Abstract: Optical absorption and photoluminescence characteristics of Sm{sup 3+} ions in lead silicate glasses mixed with different concentrations of Al{sub 2}O{sub 3} (5-10 mol%) have been investigated. From these studies, the radiative properties viz., spontaneous emission probability A, the total emission probability, the radiative lifetime {tau}{sub R}, the fluorescent branching ratio {beta} of emission transition of {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} along with other transitions for Sm{sup 3+} have been evaluated and found to be the highest for the glass mixed with 8.0 mol% of Al{sub 2}O{sub 3}.The IR spectral studies have indicated that Al{sup 3+} ions do participate in the glass network with AlO{sub 4} and AlO{sub 6} structural units and further revealed that the concentration of octahedral aluminium ions induce bonding defects in the glass network. Such bonding defects are assumed to be responsible for low phonon losses in these glasses and lead to higher values of radiative parameters for the glass mixed with 8.0 mol% of Al{sub 2}O{sub 3}.

36 MATERIALS SCIENCE↗
DOE OSTI2014

One-pot occurrence of two polymorphs of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F and their structural, spectroscopic and computational characterization

Single-crystal growth experiments in the system RbF–Sc{sub 2}O{sub 3}–SiO{sub 2} resulted in the crystallization of two forms of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F within the same run. Basic crystallographic data of the two concomitant polymorphs at ambient conditions are as follows: phase I: space group I4/m, a=11.2619(3) Å, c=8.3053(4) Å, V=1053.36(6) Å{sup 3}, Z=4; phase II: space group P2{sub 1}/m, a=11.5085(6) Å, b=8.3805(3) Å, c=11.6750(6) Å, β=111.147(6)°, V=1050.19(9) Å{sup 3}, Z=4. The structures were determined by direct methods and refined to residuals of R(|F|)=0.0231 (phase I) for 516 and 0.0249 (phase II) for 2050 independent observed reflections with I>2σ(I). Phase I showed twinning by merohedry which was accounted for during the refinement process. Both compounds are based on multiple chains of [SiO{sub 4}]-tetrahedra forming closed columns or tubes. The multiplicity of the unbranched fundamental chains is two (for phase I) and four (for phase II), respectively. The periodicity of the multiple chains has a value of four in both modifications which is also reflected in similar translation periods along the chain directions (phase I: t{sub [001]}=8.31 Å; phase II: t{sub [010]}=8.38 Å). Alternatively, both polymorphs can be described as mixed octahedral–tetrahedral frameworks, which can be classified according to their polyhedral microensembles. A topological analysis of both networks is presented. Structural investigations have been supplemented by micro-Raman spectroscopy. The interpretation of the spectroscopic data including the allocation of the bands to certain vibrational species has been aided by DFT-calculations. - Graphical abstract: Mixed tetrahedral–octahedral framework of the tetragonal polymorph of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F. - Highlights: • We report the crystal structures of two polymorphs of Rb{sub 2}ScSi{sub 4}O{sub 10}F. • Both phases crystallized concomitantly in the same flux-growth experiment. • The mixed tetrahedral–octahedral nets are compared using a topological approach. • Interpretation of the Raman spectra has been aided by DFT-calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-D-206-02: Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform

Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons’ trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. Methods: We propose an optimized implementation of the Siddon’s algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of the system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time. Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %. Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach. This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).

60 APPLIED LIFE SCIENCES↗