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At least 1,045 records · Page 58

Faster Array Training and Rapid Analysis for a Sensor Array Intended for an Event Monitor in Air

Environmental monitoring, in particular, air monitoring, is a critical need for human space flight. Both monitoring and life support systems have needs for closed loop process feedback and quality control for environmental factors. Monitoring protects the air environment and water supply for the astronaut crew and different sensors help ensure that the habitat falls within acceptable limits, and that the life support system is functioning properly and efficiently. The longer the flight duration and the farther the destination, the more critical it becomes to have carefully monitored and automated control systems for life support. There is an acknowledged need for an event monitor which samples the air continuously and provides near real-time information on changes in the air. Past experiments with the JPL ENose have demonstrated a lifetime of the sensor array, with the software, of around 18 months. We are working on a sensor array and new algorithms that will incorporate transient sensor responses in the analysis. Preliminary work has already showed more rapid quantification and identification of analytes and the potential for faster training time of the array. We will look at some of the factors that contribute to demonstrating faster training time for the array. Faster training will decrease the integrated sensor exposure to training analytes, which will also help extend sensor lifetime.

sensing array↗

Identification of Security related Bug Reports via Text Mining using Supervised and Unsupervised Classification

This paper is focused on automated classification of software bug reports to security and non-security related, using both supervised and unsupervised approaches. For both approaches, three types of feature vectors are used. For supervised learning, we experiment with multiple learning algorithms and training sets with different sizes. Furthermore, we propose a novel unsupervised approach based on anomaly detection. The evaluated is based on three NASA datasets. The results show that supervised classification is affected more by the learning algorithms than by feature vectors and using only 25% of the data for training provides as good results as if 90% of data are used for training. Both supervised and unsupervised learning can be used for identification of security bug reports; the former slightly outperforms the latter at the expense of labeling the testing set. In general, the performance differs across datasets, mainly due to the different amounts of security related information.

Goseva-Popstojanova, Katerina↗

Operability on the Europa Clipper Mission: Challenges and Opportunities

Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the pre-Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.

Signorelli, Joel↗

Operability on the Europa Clipper Mission: Challenges and Opportunities

Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.

Kumar, Meghana↗

Enterprise Digital Transformation: Transforming NASA's Workplace

NASA has a long history of serving the Nation and the world, by reaching for new heights and revealing the unknown for the benefit of all humankind. To ensure the agency will deliver on its mission in a hyper-connected, technology-enabled, globally-partnered, and rapidly-changing world, NASA launched a Digital Transformation initiative in late 2020 to modernize the way it works, the experience of its workforce and the agility of its workplace. At the core of its workplace transformation strategy, NASA is on a journey to leverage smart operational technologies, weaving together IoT, AI, intelligent automation, extended reality, and digital twins to build a network of reliable, adaptable and sustainable “Smart Centers” to execute its mission… on planet Earth and beyond. Come hear NASA’s digital strategy, the exciting progress being made, lessons learned from their journey, and how the AIMST community can enable NASA and its partners to serve humanity in a digital future.

Jill Marlowe↗

Use of Data Comm by Flight Crew to Conduct Interval Management Operations to Parallel Dependent Runways

The Interval Management (IM) concept is being developed as a method to maintain or increase high traffic density airport arrival throughput while allowing aircraft to conduct near idle thrust descents. The Interval Management with Spacing to Parallel Dependent Runways (IMSPiDR1) experiment at NASA Langley Research Center used 24 commercial pilots to examine IM procedures to conduct parallel dependent runway arrival operations while maintaining safe but efficient intervals behind the preceding aircraft. The use of IM procedures during these operations requires a lengthy and complex clearance from Air Traffic Control (ATC) to the participating aircraft, thereby making the use of Controller Pilot Data Link Communications (CPDLC) highly desirable as the communication method. The use of CPDLC reduces the need for voice transmissions between controllers and flight crew, and enables automated transfer of IM clearance elements into flight management systems or other aircraft avionics. The result is reduced crew workload and an increase in the efficiency of crew procedures. This paper focuses on the subset of data collected related to the use of CPDLC for IM operations into a busy airport. Overall, the experiment and results were very successful, with the mean time under 43 seconds for the flight crew to load the clearance into the IM spacing tool, review the calculated speed, and respond to ATC. An overall mean rating of Moderately Agree was given when the crews were asked if the use of CPDLC was operationally acceptable as simulated in this experiment. Approximately half of the flight crew reported the use of CPDLC below 10,000 for IM operations was unacceptable, with 83% reporting below 5000 was unacceptable. Also described are proposed modifications to the IM operations that may reduce CPDLC Respond time to less than 30 seconds and should significantly reduce the complexity of crew procedures, as well as follow-on research issues for operational use of CPDLC during IM operations.

Baxley, Brian T.↗

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis↗

Structural Sizing of a Tow-Steered Truss-Braced Wing Box Test Article

Tailoring of composite laminates is traditionally performed by changing the orientation of straight fibers in one or more plies. Modern automated fiber placement machines facilitate placement of bundles of curved fibers (tows) in a process called tow-steering, but additional variables must be used to define the shapes of tow-steered fiber paths. In this paper, the design of a tow-steered truss-braced wing test article called the Structural Wing Experiment Evaluating Truss-bracing 15-ft concept (SWEET-15) is discussed. The SWEET-15 test article is scaled to 18.6% of the span length and chord width of a full-scale vehicle. The test article is designed to withstand +2.5-g (positive limit) and -1.0-g (negative limit) maneuvering loads with a factor of safety of 1.5 under strength and buckling constraints. Design studies were performed using commercial finite element analysis and optimization software in conjunction with a tow-steering modeling tool called ATSCOOL (Automated Tool for Steered COmposite Optimizable Laminates) developed at NASA Langley Research center. A 6.4% weight reduction in the upper cover panels was achieved using a tow-steered layup configuration.

Structural Optimization↗

Structural Sizing of a Tow-Steered Truss-Braced Wing Box Test Article

Tailoring of composite laminates is traditionally performed by changing the orientation of straight fibers in one or more plies. Modern automated fiber placement machines facilitate placement of bundles of curved fibers (tows) in a process called tow-steering, but additional variables must be used to define the shapes of tow-steered fiber paths. In this paper, the design of a tow-steered truss-braced wing test article called the Structural Wing Experiment Evaluating Truss-bracing 15-ft concept (SWEET-15) is discussed. The SWEET-15 test article is scaled to 18.6% of the span length and chord width of a full-scale vehicle. The test article is designed to withstand +2.5-g (positive limit) and -1.0-g (negative limit) maneuvering loads with a factor of safety of 1.5 under strength and buckling constraints. Design studies were performed using commercial finite element analysis and optimization software in conjunction with a tow-steering modeling tool called ATSCOOL (Automated Tool for Steered COmposite Optimizable Laminates) developed at NASA Langley Research center. A 6.4% weight reduction in the upper cover panels was achieved using a tow-steered layup configuration.

Composites↗

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (i.e. urban and rural unmanned aircraft systems) ecosystem, NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV). HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with FMI to identify optimal approaches for displaying information for human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a controlled vehicle completed a takeoff, active flight, and landing sequence while automated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self-reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users would like greater configurability of the interface based on their personal information requirements, and they would like the opportunity to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could be introduced as a potential way to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

vertiplex↗

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗

Critical Simulation Pipeline for COG Suites [Poster]

The CRItical Simulation Pipeline (CRISP) is a Python package for automating validation of reactor criticality benchmarks. CRISP supplies COG—a multi-particle radiation transport code maintained by the Nuclear Criticality Safety Division—with a pipeline to calculate k eff performance for 400+ benchmark experiments with 3,400+ configurations from the International Criticality Safety Benchmark Evaluation Project (ICSBEP). The pipeline includes four stages: materials configuration, input card templating, cluster submission, and results analysis. CRISP includes a command-line interface to facilitate user interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A distributed temperature profiling system for vertically and laterally dense acquisition of soil and snow temperature

Abstract. Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗

Dial

A key step in almost all scientific endeavors is answering the question: Given this data I already collected, what new data do I expect will yield the most useful information toward my scientific objective? The area of (sequential) experimental design has long been investigating answers to this question, but in recent years techniques from the machine learning subfield of active learning are increasingly applied. Researchers need a simple software tool for active learning applied to experimental design that can easily integrate into their existing workflows. This computer code, Dial, provides a microservice in ORNL's INTERSECT ecosystem for active learning applied to experimental design. By being part of the INTERSECT ecosystem, Dial is simple to integrate into any INTERSECT-based workflow. Dial provides multiple backend options, where a backend is an implementation of a specific active learning method. Users can select the backend that performs best for their application. Developers can also add new backends as needed. At its core, Dial receives a set of pre-existing measurements and input parameter bounds and then recommends one or more new sets of parameters to measure. Dial also includes interfaces to other microservices in the INTERSECT ecosystem so that it can be incorporated into INTERSECT campaigns. Dial provides a simple, yet powerful interface to convert automated INTERSECT workflows into autonomous workflows that adapt based on the results that are obtained. A shared microservice for active learning prevents duplicated effort by each application team implementing its own adaptive design of experiments tool.

Drane, Lance [Oak Ridge National Laboratory (ORNL)↗

Space Station Furnace Facility Core. Requirements definition and conceptual design study. Volume 2: Technical report. Appendix 6: Technical summary reports

The Space Station Furnace Facility (SSFF) is a modular facility for materials research in the microgravity environment of the Space Station Freedom (SSF). The SSFF is designed for crystal growth and solidification research in the fields of electronic and photonic materials, metals and alloys, and glasses and ceramics and will allow for experimental determination of the role of gravitational forces in the solidification process. The facility will provide a capability for basic scientific research and will evaluate the commercial viability of low-gravity processing of selected technologically important materials. The facility is designed to support a complement of furnace modules as outlined in the Science Capabilities Requirements Document (SCRD). The SSFF is a three rack facility that provides the functions, interfaces, and equipment necessary for the processing of the furnaces and consists of two main parts: the SSFF Core Rack and the two Experiment Racks. The facility is designed to accommodate two experimenter-provided furnace modules housed within the two experiment racks, and is designed to operate these two furnace modules simultaneously. The SCRD specifies a wide range of furnace requirements and serves as the basis for the SSFF conceptual design. SSFF will support automated processing during the man-tended operations and is also designed for crew interface during the permanently manned configuration. The facility is modular in design and facilitates changes as required, so the SSFF is adept to modifications, maintenance, reconfiguration, and technology evolution.

Source record↗

Automated analysis of complex data

We have examined some of the issues involved in automating exploratory data analysis, in particular the tradeoff between control and opportunism. We have proposed an opportunistic planning solution for this tradeoff, and we have implemented a prototype, Igor, to test the approach. Our experience in developing Igor was surprisingly smooth. In contrast to earlier versions that relied on rule representation, it was straightforward to increment Igor's knowledge base without causing the search space to explode. The planning representation appears to be both general and powerful, with high level strategic knowledge provided by goals and plans, and the hooks for domain-specific knowledge are provided by monitors and focusing heuristics.

Saintamant, Robert↗

Life in the fast lane for protein crystallization and X-ray crystallography

The common goal for structural genomic centers and consortiums is to decipher as quickly as possible the three-dimensional structures for a multitude of recombinant proteins derived from known genomic sequences. Since X-ray crystallography is the foremost method to acquire atomic resolution for macromolecules, the limiting step is obtaining protein crystals that can be useful of structure determination. High-throughput methods have been developed in recent years to clone, express, purify, crystallize and determine the three-dimensional structure of a protein gene product rapidly using automated devices, commercialized kits and consolidated protocols. However, the average number of protein structures obtained for most structural genomic groups has been very low compared to the total number of proteins purified. As more entire genomic sequences are obtained for different organisms from the three kingdoms of life, only the proteins that can be crystallized and whose structures can be obtained easily are studied. Consequently, an astonishing number of genomic proteins remain unexamined. In the era of high-throughput processes, traditional methods in molecular biology, protein chemistry and crystallization are eclipsed by automation and pipeline practices. The necessity for high-rate production of protein crystals and structures has prevented the usage of more intellectual strategies and creative approaches in experimental executions. Fundamental principles and personal experiences in protein chemistry and crystallization are minimally exploited only to obtain "low-hanging fruit" protein structures. We review the practical aspects of today's high-throughput manipulations and discuss the challenges in fast pace protein crystallization and tools for crystallography. Structural genomic pipelines can be improved with information gained from low-throughput tactics that may help us reach the higher-bearing fruits. Examples of recent developments in this area are reported from the efforts of the Southeast Collaboratory for Structural Genomics (SECSG).

Review↗