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At least 1,639 records · Page 91

Conclusions of a Cryogenic Motor Configuration Study for Aircraft Propulsion

Cryogenic electric machines are a promising technology for multi megawatt electric aircraft drivetrains. Superconducting stators that make use of low AC loss superconducting wires are considered to be a key component for these cryogenic machines. This paper shares the lessons learned from a design study that evaluated several configurations of a 5 MW cryogenic motor for this application. The lessons are drawn from a qualitative study of 16 configurations and a quantitative study of 8 down selected configurations. The quantitative study involved using analytical calculations (electromagnetic, thermofluid, mechanical, and motor sizing) to determine each configuration’s Pareto front of total efficiency and total specific power. The conclusions from a preliminary assessment of different stator conductors (superconductors and cryogenic metals) are also presented. One motor configuration is down selected for future detailed design and demonstration at the multi megawatt level.

Electrified aircraft propulsion↗

The Evolution of the Impact Evidence Library Methods – What We Learned

During fiscal years 2020-2022 NASA’s Human Research Program, Exploration Medical Capability Element has worked to source this evidence for the model. During the process several lessons learned have been identified and drive recommendations for future efforts. The ICL 1.0: Spaceflight Medical Operational expertise should be consulted regarding the addition of future conditions. A clinician experienced with the nuances of spaceflight is recommended to have a minimum of 20 hours per condition to adequately source the evidence. Condition definitions should be carefully crafted to maintain mutual exclusivity. Common side effects from medications, common and consequential incidental findings in laboratory studies, and consequential side effects of diagnostic and procedural capabilities should be considered for inclusion as unique conditions in future iterations of the ICL 1.0. Incidence: Spaceflight incidence, although based on small population sizes, is the gold standard. Terrestrial incidence should be applied cautiously. Probability of minor vs. severe cases of the condition: Data informing severity probability of the medical condition are nearly always terrestrial. Special attention should be paid to this metric as it drives outcomes in the model and terrestrial data often overestimate severity of expected cases in spaceflight. Condition duration: Three clinical phases currently exist in the model. They represent the time necessary for diagnosis, treatment/convalescence/recurrence, and the remaining duration of the mission after maximal recovery, respectively. Further subdivision of acute and convalescent treatment/recurrence is recommended. Probability of need for evacuation: Surrogates for “Return to Definitive Care” were chosen prior to data collection. In most cases these surrogates were “need for hospitalization” or “need for surgery.” For many conditions these may not be realistic drivers of “Return to Definitive Care” and should be re-examined. Probability of Crew Mortality: Terrestrial mortality data is difficult to apply to astronauts who, invariably, have less comorbidities than terrestrial subjects. It must be applied judiciously. The proportion of mission tasks impaired by the condition: “Task Impairment” was assigned to each condition prior to the condition duration data becoming available. Future efforts should assign Task Impairment as the final step in sourcing the evidence.

A.J. Kreykes↗

NASA's astrophysics archives at the National Space Science Data Center

NASA maintains an archive facility for Astronomical Science data collected from NASA's missions at the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. This archive was created to insure the science data collected by NASA would be preserved and useable in the future by the science community. Through 25 years of operation there are many lessons learned, from data collection procedures, archive preservation methods, and distribution to the community. This document presents some of these more important lessons, for example: KISS (Keep It Simple, Stupid) in system development. Also addressed are some of the myths of archiving, such as 'scientists always know everything about everything', or 'it cannot possibly be that hard, after all simple data tech's do it'. There are indeed good reasons that a proper archive capability is needed by the astronomical community, the important question is how to use the existing expertise as well as the new innovative ideas to do the best job archiving this valuable science data.

Vansteenberg, M. E.↗

A Study to Estimate the Effectiveness of Visual Testing Training for Aviation Maintenance Management

The Air Commerce Act of 1926 set the beginning for standards in aviation maintenance. Even after deregulation in the late l970s, maintenance standards and requirements still have not changed far from their initial criteria. After a potential candidate completes Federal Aviation Administration training prerequisites, they may test for their Airframe and Powerplant (A&P) certificate. Performing maintenance in the aviation industry for a minimum of three years, the technician may then test for their Inspection Authorization (IA). After receiving their Airframe and Powerplant certificate, a technician is said to have a license to perform. At no time within the three years to eligibility for Inspection Authorization are they required to attend higher-level inspection training. What a technician learns in the aviation maintenance industry is handed down from a seasoned technician to the new hire or is developed from lessons learned on the job. Only in Europe has the Joint Aviation Authorities (JAA) required higher-level training for their aviation maintenance technicians in order to control maintenance related accidents (Lu, 2005). Throughout the 1990s both the General Accounting Office (GAO) and the National Transportation Safety Board (NTSB) made public that the FAA is historically understaffed (GAO, 1996). In a safety recommendation the NTSB stated "The Safety Board continues to lack confidence in the FAA's commitment to provide effective quality assurance and safety oversight of the ATC system (NTSB, 1990)." The Federal Aviation Administration (FAA) has been known to be proactive in creating safer skies. With such reports you would suspect the FAA to also be proactive in developing more stringent inspection training for aviation maintenance technicians. The purpose of this study is to estimate the effectiveness of higher-level inspection training, such as Visual Testing (VT) for aviation maintenance technicians, to improve the safety of aircraft and to make recommendations to management with regard to the value of such training.

Law, Lewis Lyle↗

Device-Centric Ransomware Detection using Machine Learning-Based Memory Forensics for Smart Inverters

Ransomware attacks are the fastest-growing form of cyberattacks worldwide. Recently, ransomware attacks have targeted industrial control systems (ICSs), including power grids. Lessons learned from recent incidents in ICSs show that ransomware groups can deliver ransomware into not only the organization’s control servers, but also the operational technology (OT) devices such as smart inverters and smart grid devices. This paper proposes a machine learning (ML)- based memory forensics method enabling the detection of ransomware binaries stored in the memory of a commercial smart inverter. Device firmware binary files are extracted from a Serial Peripheral Interface (SPI) flash memory, and samples of both benign and ransomware binaries are generated by a binary manipulation method and a real-world ransomware encryption, separately. A deep transfer learning (DTL) method is used to retrain a convolutional neural network (CNN)-based ransomware detection algorithm using the generated samples. The experimental result validates that the proposed ML-based memory forensics method can accurately detect ransomware files.

97 MATHEMATICS AND COMPUTING↗

Wolf

The Workflow Orchestration Language Framework (WOLF) is an agentic framework grounded in natural language with an architecture inspired by reinforcement learning (RL)—designed to orchestrate, scale, and accelerate complex workflows. The concept of WOLF was born out of the very successful ASC Tri-lab Multi-Agent Design Assistant (MADA) project, but extends beyond its domain-specific design agents to provide a more general and extensible architecture. WOLF capitalizes on the lessons learned from MADA and is fully aligned with Sutton’s The Bitter Lesson—that the most enduring progress in AI comes from general-purpose methods that scale with computation, rather than narrow techniques built on domain-specific human knowledge. In this spirit, WOLF enables agents to autonomously learn workflows, capture strategies as reusable playbooks, and build a growing corpus of interpretable, auditable “wisdom artifacts.” These artifacts, expressed in natural language, bridge human and machine understanding while preserving adaptability and scalability as computational power continues to expand.

Boureima, Ismaeal↗

Advanced Syngas Cleanup Using Radically Engineered Modular Systems

The overall objective of this project was to address key knowledge gaps and develop modular sorbent-based warm syngas cleanup designs that will enable 1- to 5-MW REMS-based plants to be cost-competitive with large state-of-the-art commercial plants utilizing all of our abundant domestic coal reserves. We proposed to develop two potential new desulfurization process designs specifically targeted for REMS plants for combined heat and power or polygeneration from coal gasification. The first design is a modification of our commercially offered dual transport reactor design based Warm-gas Desulfurization Process (WDP) where the transport-reactor regenerator is replaced with a fluidized-bed regenerator. This design leverages the learnings obtained from our pre-commercial demonstration testing of the WDP technology for over 3,000 hours and conclusions from the lessons learned workshop. The second design is a fixed-bed process that leverages the successful composition and chemistry of our WDP sorbent to develop an effective fixed-bed sorbent formulation and fixed-bed process design. These two potential desulfurization process designs have a strong technical likelihood of success and offer the best opportunity to effectively employ design standardization, mass production and advanced manufacturing techniques to enable cost competitiveness for syngas cleanup at smaller scale.

01 COAL, LIGNITE, AND PEAT↗

From Aeronautics to Space: Lessons in Human Automation

Civilian air flight continues on a growth curve, as more and more people utilize air travel to meet business and personal travel needs: This consumer-driven demand has resulted in the adoption of new methods to increase air system capacity and to make the air transportation system increasingly more efficient. As a consequence, civilian aviation, as an industry, has assumed a leading role in the use of automated systems, and, by implication, in the understanding of how human openers interact with these systems. Aeronautical automation systems serve a variety of roles. These include controlling aircraft and aiding, advising and monitoring numerous functions in the aircraft/airspace system. Experiences in the use of human/automation systems gathered from aviation are, in many cases, generalizable to other industries having similar requirements for human and non-human intelligent system interaction. However, the human/automation lessons learned from aviation have special relevance to the space application, where many of the same operational demands prevail. The application of aeronautical lessons of human-automated interaction to spaceflight is the subject of this paper. The discussion will address: the progress that has been made through aeronautically-based research and experience in understanding human/automation interaction, ways that this understanding can be applied to the needs of space, and the limits of our present understanding of human/automations systems. Suggestions will be offered related to human-automation research generally, and to the particular needs of the space endeavor.

Connors, Mary M.↗

Desert Research and Technology Studies (D-RATS) 2022 Test Report

The primary purpose of the 2022 Desert Research and Technology Studies (D-RATS 22) analog mission field test was to provide data and recommendations regarding how pressurized rover (PR) design, cabin configuration, driving modes, timeline constraints, and mission operations can be acceptably implemented to support future Artemis missions that include a PR. The goal of these evaluations was to provide first order feedback and data on design concepts currently being traded as options for future pressurized rover flight prototypes. The Japan Aerospace Exploration Agency (JAXA) may provide a PR for future Artemis missions. One of the primary objectives of D-RATS 2022 was to share with the JAXA PR team the processes and protocols by which NASA has learned to successfully evaluate rover concepts through integrated HITL evaluations over the past 20+ years, so that JAXA could apply any applicable lessons learned to their PR design, development, testing, and evaluation.

analog↗

Lessons of History: Organizational Factors in Three Aviation Mishaps

This presentation examines organizational factors that contributed to three aircraft mishaps and provides analysis of lessons learned. Three historical aviation mishaps were studied from a human factors perspective, and organizational factors identified and analyzed. These case studies provide valuable lessons for understanding the interaction of people with aircraft systems and with each other during flight operations.

Merlin, Peter William↗

Cassini distributed instrument operations – what we’ve learned since Saturn orbit insertion

The Cassini mission to Saturn is complex with 12 science teams conducting distributed operations across the United States and Europe. Each Team includes scientists from around the world who actively participate in operations, including observation design, instrument commanding, downlink processing, and archiving. This represents a change in how JPL complex deep-space missions have been operated. Since Saturn Orbit Insertion (SOI), the Cassini Project has spent 17 months conducting science operations and has gained realworld experience that has tested the assumptions and rationale for this approach. We have learned that many of the expected benefits have been realized, but there were numerous unexpected challenges as well. This paper will discuss the lessons learned from the Cassini Tour experience to date. It will revisit the assumptions and rationale behind the distributed instrument operations design and will describe the results, good and bad, of implementing this method of operations. We will describe how Instrument Teams are structured, their roles and responsibilities, what challenges they faced going into orbital operations (the “tour”) and what creative solutions were proposed when funding limitations and schedule milestones prevented optimum solutions. We will also discuss the problems that have been encountered both on the ground and with the instruments, how these problems and anomalies were overcome, and what was learned along the way about the characteristics of distributed instrument operations.

Woncik, Pam↗

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards continual machine learning for particle accelerators

This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.

Accelerator Physics↗

Learning from the Mars Rover Mission: Scientific Discovery, Learning and Memory

Purpose: Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in knowledge production. This paper uses the Mars Exploration Rover mission as a site to explore this process. Approach: Observational study and analysis of the work of the MER science and engineering team during rover operations, to investigate how learning occurs, how it is recorded, and how these representations might be made available for subsequent missions. Findings: Learning occurred in many areas: planning science strategy, using instrumen?s within the constraints of the martian environment, the Deep Space Network, and the mission requirements; using software tools effectively; and running two teams on Mars time for three months. This learning is preserved in many ways. Primarily it resides in individual s memories. It is also encoded in stories, procedures, programming sequences, published reports, and lessons learned databases. Research implications: Shows the earliest stages of knowledge creation in a scientific mission, and demonstrates that knowledge management must begin with an understanding of knowledge creation. Practical implications: Shows that studying learning and knowledge creation suggests proactive ways to capture and use knowledge across multiple missions and generations. Value: This paper provides a unique analysis of the learning process of a scientific space mission, relevant for knowledge management researchers and designers, as well as demonstrating in detail how new learning occurs in a learning organization.

Linde, Charlotte↗

Mars mission science operations facilities design

A variety of designs for Mars rover and lander science operations centers are discussed in this paper, beginning with a brief description of the Pathfinder science operations facility and its strengths and limitations. Particular attention is then paid to lessons learned in the design and use of operations facilities for a series of mission-like field tests of the FIDO prototype Mars rover. These lessons are then applied to a proposed science operations facilities design for the 2003 Mars Exploration Rover (MER) mission. Issues discussed include equipment selection, facilities layout, collaborative interfaces, scalability, and dual-purpose environments. The paper concludes with a discussion of advanced concepts for future mission operations centers, including collaborative immersive interfaces and distributed operations. This paper's intended audience includes operations facility and situation room designers and the users of these environments.

Ground Data System (GDC)↗

SECURED: Simulator-Enhanced Control and Understanding of Reactor systems for cyber-Event Defense

The study discusses a learning approach for analyzing cyber-events in reactor systems using integrated hardware and personal computer simulator models. Key points include the rise in cyber-attacks and their sophistication in industrial control systems (ICS), the necessity for awareness, understanding, resource allocation, and preparation to combat these threats, and the digital transformation of old and new nuclear plants, increasing their exposure to cyber threats. It highlights the cyber vulnerabilities of advanced reactor systems, which rely on digital instrumentation and control for operations and safety functions, making them susceptible to cyber-attacks. The approach involves demonstrating reactor system plant ICS cyber-attacks under various operational conditions utilizing tools like simulator models and hardware-based kits. A strategic solution approach tailored to critical infrastructure is emphasized, along with community engagement for public and government support, adopting effective learning approaches, and the preparation for anticipated future challenges. The presentation concludes with a call to action to address challenges, leverage opportunities, and advance through lesson learning in cybersecurity for nuclear energy systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Use of Earth Observing Satellite Data for the Development of "Learning Exercises" for College-Level Science Courses

This paper is based on experiences being gained through a project entitled "The Mississippi Community College Pilot Project". The project was labeled "pilot" because it is thought that lessons learned during the implementation of this project may aid similar endeavors in other states. The objective of the project is to provide curriculum enrichment and associated faculty enhancement through the use of earth observations data in biological and physical sciences courses. The premise underlying the objective is that information from earth observations from satellite and aircraft platforms provides an effective means of illustrating and explaining science topics/phenomena in a new and/or different perspective. It is also thought that the use of data acquired from space may also serve to captivate the students interest and/or inquisitiveness about the particular science issue.

Joyce, Armond T.↗

Apollo: Learning From the Past, For the Future

This paper shares an interesting and unique case study of knowledge capture by the National Aeronautics and Space Administration (NASA), an ongoing project to recapture and make available the lessons learned from the Apollo lunar landing project so that those working on future projects do not have to "reinvent the wheel". NASA's new Constellation program, the successor to the Space Shuttle program, proposes a return to the Moon using a new generation of vehicles. The Orion Crew Vehicle and the Altair Lunar Lander will use hardware, practices, and techniques descended and derived from Apollo, Shuttle and the International Space Station. However, the new generation of engineers and managers who will be working with Orion and Altair are largely from the decades following Apollo, and are likely not well aware of what was developed in the 1960s. In 2006 a project at NASA's Johnson Space Center was begun to find pertinent Apollo-era documentation and gather it, format it, and present it using modern tools for today's engineers and managers. This "Apollo Mission Familiarization for Constellation Personnel" project is accessible via the web from any NASA center for those interested in learning "how did we do this during Apollo?"

Grabois, Michael R.↗