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Modeling the Risk of U.S. Offshore Oil & Gas Exploration-Well Drilling, Commercial Nuclear Plants, and Human Spaceflight
Probabilistic Risk Assessment (PRA) has been applied in different industries for many years. Each industry and technology area presents varying challenges and priorities, and the risk models therefore need to be somewhat different. This paper compares aspects of risk modeling of offshore drilling, nuclear plant operation, and human spaceflight. Risk models in all three technology areas have certain high-level similarities: (1) they employ redundancy and diversity in their means to prevent or mitigate risk, including a mix of active and passive systems designed to respond to off-normal evolutions; (2) they are affected by human reliability; (3) their models require consideration of coupling between scenario structure and scenario phenomenology. But in examining the models in more detail, one sees important differences in methodology and emphasis. For purposes of comparison, this paper discusses aspects of a risk model of an offshore drilling operation in the U.S. Gulf of Mexico, focusing on where such a development differs in important ways from models of commercial U.S. nuclear plants and models developed for human spaceflight.
Modeling the Risk of U.S. Offshore Oil & Gas Exploration-Well Drilling, Commercial Nuclear Plants, and Human Spaceflight
Nuclear power, offshore oil & gas exploration, and human spaceflight all have high consequence potential if something goes wrong. Each has had at least one major incident that pointed to a need for improved risk assessment. Careful risk analysis is very important for technologies having complexity, uncertainty, and high consequence potential. All rely on multiple barriers/controls/redundancy to minimize risk. NRC (Nuclear Regulatory Commission) has moved towards risk informed regulation with Probabilistic Risk Assessment (PRA) as a major input.
The Effects of Severity of Losses of Well Clear on Minimum Operations Performance Standards End-To-End Verification and Validation Simulation Study for Integrating Unmanned Aircraft Systems into the National Airspace System Using Detect and Avoid Systems
As Unmanned Aircraft Systems (UAS) make their way to mainstream aviation operations within the National Airspace System (NAS), research efforts are underway to develop a safe and effective environment for their integration into the NAS. Detect and Avoid (DAA) systems are required to account for the lack of “eyes in the sky” due to having no human on-board the aircraft. The technique, results, and lessons learned from a detailed End-to-End Verification and Validation (E2-V2) simulation study of a DAA system representative of RTCA Special Committee(SC)-228’s proposed Phase I DAA Minimum Operational Performance Standards (MOPS), based on specific test vectors and encounter cases, will be presented in this paper.
Detect-and-Avoid Well Clear and Low SWaP Operations
This presentation recaptures NASA's work in the low SWaP surveillance area in the UAS in the NAS project.
Low C-SWaP Detect and Avoid: Defining Well Clear
This presentation evaluates four non-cooperative DWCs in terms of their safety and operational suitability metrics.
An Examination of Two Non-Cooperative Detect and Avoid Well Clear Definitions
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The JPL Deep-Well Mid-Infrared Array Camera
We describe the development of a mid-infrared camera intended for use at the Palomar 5-m telescope and at the NASA Infrared Telescope Facility.
640x512 pixel four-band, broad-band, and narrow-band quantum well infrared photodetector focal plane arrays
In this paper, we discuss the development of this very sensitive long wavelength infrared (LWIR) camera based on a Ga/AlGaAs QWIP focal plane array and its performance in quantum efficiency, NETD, uniformity, and operability.
Long-Wavelength 640 x 486 GaAs/AlGaAs Quantum Well Infrared Photodetector Snap-shot Camera
The performcance of this QWIP camera is reported including indoor and outdoor imaging.
How well do passive microwave algorithms estimate vertical profiles of precipitation?
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Multi-Physics Based Thermo-Well Fatigue Design Tool
In this study, mathematical-based models for fatigue life were developed for Resistance Temperature Detectors (RTDs)/thermo-wells in high-pressure, cryogenic flow environments. A literature review that identified bench-scale stainless steel fatigue data at cryogenic temperatures that can be used to validate fatigue life mathematical models was pursued. Developing the ability to predict the fatigue life of RTDs/thermo-wells in high-pressure, cryogenic flow environments, especially liquid oxygen (LOX), will lead to a lower incidence of RTD damage and failure during propulsion test activities. During this performance period, the project was transitioned to a computational RTD/thermos-well fatigue modeling effort rather than the previously proposed experimental fatigue testing in FY19 since the computational modeling approach will provide a better fundamental understanding of fatigue behavior at cryogenic temperatures prior to pursuing potentially costly experimental tests. Due to COVID-19 issues impairing access to some needed university partnerships, tools and lab facilities, FY20efforts are continuing into FY21 and will be reported upon in greater detail in the FY21 report
Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges
The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.