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DOE OSTI2015

Rethinking the Default Construction of Multimodel Climate Ensembles

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process. If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

Processing Ocean Images to Detect Large Drift Nets

A computer program processes the digitized outputs of a set of downward-looking video cameras aboard an aircraft flying over the ocean. The purpose served by this software is to facilitate the detection of large drift nets that have been lost, abandoned, or jettisoned. The development of this software and of the associated imaging hardware is part of a larger effort to develop means of detecting and removing large drift nets before they cause further environmental damage to the ocean and to shores on which they sometimes impinge. The software is capable of near-realtime processing of as many as three video feeds at a rate of 30 frames per second. After a user sets the parameters of an adjustable algorithm, the software analyzes each video stream, detects any anomaly, issues a command to point a high-resolution camera toward the location of the anomaly, and, once the camera has been so aimed, issues a command to trigger the camera shutter. The resulting high-resolution image is digitized, and the resulting data are automatically uploaded to the operator s computer for analysis.

Veenstra, Tim↗

NASA NTRS2012

Materials Science Research Rack Onboard the International Space Station Hardware and Operations

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009, and is currently installed in the U.S. Destiny Laboratory Module. Since that time, MSRR has performed virtually flawlessly, logging more than 620 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. Currently the NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA developed Materials Science Laboratory (MSL) which accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample-Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400 C. Once an SCA is installed, the experiment can be run by automatic command or science conducted via telemetry commands from the ground. Initially, 12 SCAs were processed in the first furnace insert for a team of European and US investigators. After these samples were processed the Furnaces Inserts were exchanged and an additional single sample was processed. The processed samples have been returned to Earth for evaluation and comparison of their properties to samples similarly processed on the ground. A preliminary examination of the samples indicates that the majority of the desired science objectives have been successfully met leading to significant improvements in the understanding of alloy solidification processes. Six SCAs were launched on Space Shuttle Mission STS-135 in July 2011 for processing during the Fall of 2011. Additional batches are planned for future processing. This facility is available to support additional materials science investigations through programs such as the US National Laboratory, Technology Development, NASA Research Announcements, and others.

Lehman, John R.↗

NASA NTRS2012

Quality Assessment of Landsat Surface Reflectance Products Using MODIS Data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.

Landsat↗

NASA NTRS2017

MSRR Rack Materials Science Research Rack

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and the European Space Agency (ESA) for materials science investigations on the International Space Station (ISS). The MSRR is managed at the Marshall Space Flight Center (MSFC) in Huntsville, AL. The MSRR facility subsystems were manufactured by Teledyne Brown Engineering (TBE) and integrated with the ESA/EADS-Astrium developed Materials Science Laboratory (MSL) at the MSFC Space Station Integration and Test Facility (SSITF) as part of the Systems Development Operations Support (SDOS) contract. MSRR was launched on STS-128 in August 2009, and is currently installed in the U. S. Destiny Laboratory Module on the ISS. Materials science is an integral part of developing new, safer, stronger, more durable materials for use throughout everyday life. The goal of studying materials processing in space is to develop a better understanding of the chemical and physical mechanisms involved, and how they differ in the microgravity environment of space. To that end, the MSRR accommodates advanced investigations in the microgravity environment of the ISS for basic materials science research in areas such as solidification of metals and alloys. MSRR allows for the study of a variety of materials including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. Currently the NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA developed Materials Science Laboratory (MSL) which accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample-Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400 C. Once an SCA is installed, the experiment can be run by automatic command or science conducted via telemetry commands from the ground. This facility is available to support materials science investigations through programs such as the US National Laboratory, Technology Development, NASA Research Announcements, and others. TBE and MSFC are currently developing NASA Sample Cartridge Assemblies (SCA's) with a planned availability for launch in 2017.

Reagan, Shawn↗

NASA NTRS1972

Automatic classification of soils and vegetation with ERTS-1 data

Preliminary results of a test of a computerized analysis method using ERTS 1 data are presented. The method consisted of a four-spectral-band supervised, maximum likelihood, Gaussian classifier with training statistics derived through a combination of clustering and manual methods. The multivariate analysis method leads to the assignment of each resolution element of the data to one of a preselected set of discrete classes. The data frame was an area over the Texas-Oklahoma border including Lake Texoma. The study suggests that multispectral scanner data coupled with machine processing shows promise for earth surface cover surveys. Futhermore, the processing time is short and consequently the costs are low; a full frame can be analyzed completely within 48 hours.

Landgrebe, D. A.↗

DOE OSTI2015

Development of a 3D optical scanning-based automatic quality assurance system for proton range compensators

Purpose: A new automatic quality assurance (AutoRCQA) system using a three-dimensional scanner (3DS) with system automation was developed to improve the accuracy and efficiency of the quality assurance (QA) procedure for proton range compensators (RCs). The system performance was evaluated for clinical implementation. Methods: The AutoRCQA system consists of a three-dimensional measurement system (3DMS) based on 3DS and in-house developed verification software (3DVS). To verify the geometrical accuracy, the planned RC data (PRC), calculated with the treatment planning system (TPS), were reconstructed and coregistered with the measured RC data (MRC) based on the beam isocenter. The PRC and MRC inner surfaces were compared with composite analysis (CA) using 3DVS, using the CA pass rate for quantitative analysis. To evaluate the detection accuracy of the system, the authors designed a fake PRC by artificially adding small cubic islands with side lengths of 1.5, 2.5, and 3.5 mm on the inner surface of the PRC and performed CA with the depth difference and distance-to-agreement tolerances of [1 mm, 1 mm], [2 mm, 2 mm], and [3 mm, 3 mm]. In addition, the authors performed clinical tests using seven RCs [computerized milling machine (CMM)-RCs] manufactured by CMM, which were designed for treating various disease sites. The systematic offsets of the seven CMM-RCs were evaluated through the automatic registration function of AutoRCQA. For comparison with conventional technique, the authors measured the thickness at three points in each of the seven CMM-RCs using a manual depth measurement device and calculated thickness difference based on the TPS data (TPS-manual measurement). These results were compared with data obtained from 3DVS. The geometrical accuracy of each CMM-RC inner surface was investigated using the TPS data by performing CA with the same criteria. The authors also measured the net processing time, including the scan and analysis time. Results: The AutoRCQA system accurately detected all fake objects in accordance with the given criteria. The median systematic offset of the seven CMM-RCs was 0.08 mm (interquartile range: −0.25 to 0.37 mm) and −0.08 mm (−0.58 to 0.01 mm) in the X- and Y-directions, respectively, while the median distance difference was 0.37 mm (0.23–0.94 mm). The median thickness difference of the TPS-manual measurement at points 1, 2, and 3 was −0.4 mm (−0.4 to −0.2 mm), −0.2 mm (−0.3 to 0.0 mm), and −0.3 mm (−0.6 to −0.1 mm), respectively, while the median difference of 3DMS was 0.0 mm (−0.1 to 0.2 mm), 0.0 mm (−0.1 to 0.3 mm), and 0.1 mm (−0.1 to 0.2 mm), respectively. Thus, 3DMS showed slightly better values compared to the manual measurements for points 1 and 3 in statistical analysis (p < 0.05). The average pass rate of the seven CMM-RCs was 97.97% ± 1.68% for 1-mm CA conditions, increasing to 99.98% ± 0.03% and 100% ± 0.00% for 2- and 3-mm CA conditions, respectively. The average net analysis time was 18.01 ± 1.65 min. Conclusions: The authors have developed an automated 3DS-based proton RC QA system and verified its performance. The AutoRCQA system may improve the accuracy and efficiency of QA for RCs.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

Portfolio-Scale Optimization of Customer Energy Efficiency Incentive and Marketing: Cooperative Research and Development Final Report, CRADA Number CRD-13-535

North East utility National Grid (NGrid) is developing a portfolio-scale application of OpenStudio designed to optimize incentive and marketing expenditures for their energy efficiency (EE) programs. NGrid wishes to leverage a combination of geographic information systems (GIS), public records, customer data, and content from the Building Component Library (BCL) to form a JavaScript Object Notation (JSON) input file that is consumed by an OpenStudio-based expert system for automated model generation. A baseline model for each customer building will be automatically tuned using electricity and gas consumption data, and a set of energy conservation measures (ECMs) associated with each NGrid incentive program will be applied to the model. The simulated energy performance and return on investment (ROI) will be compared with customer hurdle rates and available incentives to A) optimize the incentive required to overcome the customer hurdle rate and B) determine if marketing activity associated with the specific ECM is warranted for that particular customer. Repeated across their portfolio, this process will enable NGrid to substantially optimize their marketing and incentive expenditures, targeting those customers that will likely adopt and benefit from specific EE programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1970

Detection and identification of benthic communities and shoreline features in Biscayne Bay

Progress made in the development of a technique for identifying and delinating benthic and shoreline communities using multispectral imagery is described. Images were collected with a multispectral scanner system mounted in a C-47 aircraft. Concurrent with the overflight, ecological ground- and sea-truth information was collected at 19 sites in the bay and on the shore. Preliminary processing of the scanner imagery with a CDC 1604 digital computer provided the optimum channels for discernment among different underwater and coastal objects. Automatic mapping of the benthic plants by multiband imagery and the mapping of isotherms and hydrodynamic parameters by digital model can become an effective predictive ecological tool when coupled together. Using the two systems, it appears possible to predict conditions that could adversely affect the benthic communities. With the advent of the ERTS satellites and space platforms, imagery data could be obtained which, when used in conjunction with water-level and meteorological data, would provide for continuous ecological monitoring.

Kolipinski, M. C.↗

NASA NTRS1991

An autonomous rendezvous and docking system using cruise missile technologies

In November 1990 the Autonomous Rendezvous & Docking (AR&D) system was first demonstrated for members of NASA's Strategic Avionics Technology Working Group. This simulation utilized prototype hardware from the Cruise Missile and Advanced Centaur Avionics systems. The object was to show that all the accuracy, reliability and operational requirements established for a space craft to dock with Space Station Freedom could be met by the proposed system. The rapid prototyping capabilities of the Advanced Avionics Systems Development Laboratory were used to evaluate the proposed system in a real time, hardware in the loop simulation of the rendezvous and docking reference mission. The simulation permits manual, supervised automatic and fully autonomous operations to be evaluated. It is also being upgraded to be able to test an Autonomous Approach and Landing (AA&L) system. The AA&L and AR&D systems are very similar. Both use inertial guidance and control systems supplemented by GPS. Both use an Image Processing System (IPS), for target recognition and tracking. The IPS includes a general purpose multiprocessor computer and a selected suite of sensors that will provide the required relative position and orientation data. Graphic displays can also be generated by the computer, providing the astronaut / operator with real-time guidance and navigation data with enhanced video or sensor imagery.

Jones, Ruel Edwin↗

NASA NTRS2000

Multi-Disciplinary Design Optimization Using WAVE

The current preliminary design tools lack the product performance, quality and cost prediction fidelity required to design Six Sigma products. They are also frequently incompatible with the tools used in detailed design, leading to a great deal of rework and lost or discarded data in the transition from preliminary to detailed design. Thus, enhanced preliminary design tools are needed in order to produce adequate financial returns to the business. To achieve this goal, GEAE has focused on building the preliminary design system around the same geometric 3D solid model that will be used in detailed design. With this approach, the preliminary designer will no longer convert a flowpath sketch into an engine cross section but rather, automatically create 3D solid geometry for structural integrity, life, weight, cost, complexity, producibility, and maintainability assessments. Likewise, both the preliminary design and the detailed design can benefit from the use of the same preliminary part sizing routines. The design analysis tools will also be integrated with the 3D solid model to eliminate manual transfer of data between programs. GEAE has aggressively pursued the computerized control of engineering knowledge for many years. Through its study and validation of 3D CAD programs and processes, GEAE concluded that total system control was not feasible at that time. Prior CAD tools focused exclusively on detail part geometry and Knowledge Based Engineering systems concentrated on rules input and data output. A system was needed to bridge the gap between the two to capture the total system. With the introduction of WAVE Engineering from UGS, the possibilities of an engineering system control device began to formulate. GEAE decided to investigate the new WAVE functionality to accomplish this task. NASA joined GEAE in funding this validation project through Task Order No. 1. With the validation project complete, the second phase under Task Order No. 2 was established to develop an associative control structure (framework) in the UG WAVE environment enabling multi-disciplinary design of turbine propulsion systems. The capabilities of WAVE were evaluated to assess its use as a rapid optimization and productivity tool. This project also identified future WAVE product enhancements that will make the tool still more beneficial for product development.

Irwin, Keith↗

NASA NTRS2006

An improved method for precise automatic co-registration of moderate and high-resolution spacecraft imagery

Improvements to the automated co-registration and change detection software package, AFIDS (Automatic Fusion of Image Data System) has recently completed development for and validation by NGA/GIAT. The improvements involve the integration of the AFIDS ultra-fine gridding technique for horizontal displacement compensation with the recently evolved use of Rational Polynomial Functions/ Coefficients (RPFs/RPCs) for image raster pixel position to Latitude/Longitude indexing. Mapping and orthorectification (correction for elevation effects) of satellite imagery defies exact projective solutions because the data are not obtained from a single point (like a camera), but as a continuous process from the orbital path. Standard image processing techniques can apply approximate solutions, but advances in the state-of-the-art had to be made for precision change-detection and time-series applications where relief offsets become a controlling factor. The earlier AFIDS procedure required the availability of a camera model and knowledge of the satellite platform ephemeredes. The recent design advances connect the spacecraft sensor Rational Polynomial Function, a deductively developed model, with the AFIDS ultrafine grid, an inductively developed representation of the relationship raster pixel position to latitude /longitude. As a result, RPCs can be updated by AFIDS, a situation often necessary due to the accuracy limits of spacecraft navigation systems. An example of precision change detection will be presented from Quickbird.

co-registration↗

DOE OSTI2023

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Less is more: Sampling chemical space with active learning

We present the development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble’s prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. Finally, this universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

PERLE: A High Power Energy Recovery Facility

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Kaabi, W.↗

DOE OSTI2019

SRF Cavity Fault Classification Using Machine Learning At CEBAF

Solopova, A.↗

DOE OSTI2019

SRF Cavity Fault Classification Using Machine Learning At CEBAF

Solopova, A.↗