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At least 559 records · Page 31

Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge

This viewgraph presentation reviews Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge. The Wing Leading Edge Impact Detection System (WLE IDS) and the Impact Analysis Process are also described to monitor WLE debris threats. The contents include: 1) Risk Management via SHM; 2) Hardware Overview; 3) Instrumentation; 4) Sensor Configuration; 5) Debris Hazard Monitoring; 6) Ascent Response Summary; 7) Response Signal; 8) Distribution of Flight Indications; 9) Probabilistic Risk Analysis (PRA); 10) Model Correlation; 11) Impact Tests; 12) Wing Leading Edge Modeling; 13) Ascent Debris PRA Results; and 14) MM/OD PRA Results.

Yap, Keng C.↗

High Ice Water Content Associated with a Darwin, Austrailia, Mesoscale Convective System

A mesoscale convective complex that occurred during the 2014 Darwin, Australia, flight campaign is examined via a three-dimensional, numerical simulation. The focus of the study is to better understand the presence and development of High Ice Water Content (HIWC) that was observed in the actual storm. Although peak values of ice water content may occur early in the storm lifetime, large areas of high concentrations expand with time and persist even when the storm tops begin to warm. The storm canopy which contains HIWC, has low radar reflectivity factor and is fed by an ensemble of regenerating thermal plumes. Detection via weather radar of the aviation hazard associated HIWC is challenging due the low values of radar reflectivity factor.

Aviation hazards↗

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

Dreher, Joseph↗

SPHINX: An SEP Model Validation Infrastructure developed through Community Challenges and the SEP Scoreboards

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars (Whitman et al 2022). For these reasons, SEP modelers have developed a rich and diverse set of models with a wide variety of aims. Some models probe the basic physics at the heart of particle acceleration and transport. Others produce fast statistical forecasts or employ disruptive new techniques like Machine Learning with the goal to assist end users in making operational decisions. To enable a consistent and quantitative understanding of SEP model performance, a generalized, automated validation infrastructure, called SPHINX, is being developed at NASA SRAG in close collaboration with NASA CCMC, NASA M2M, NOAA SWPC, and BIRA-IASB. This infrastructure has been built up through a multi-year community challenge. Starting in 2018 at the SHINE workshop, an effort was launched through SHINE, ISWAT, and ESWW to encourage quantitative, comprehensive, and consistent validation of SEP models. This effort has defined a set of challenge SEP events with the aim of generating quantitative comparisons between forecasts and observations and a set of challenge “non-events” to assess false alarms. In 2023, these challenge lists have been extended to statistically significant numbers with a prescribed set of rules for producing forecasts and supported through the dedicated SEPVAL working meetings. The participation of the research community has allowed the infrastructure to validate all the types of outputs being produced by SEP models. In parallel, the SPHINX code is being applied to real time forecasts submitted to the SEP Scoreboards, ensuring that the validation infrastructure can interpret forecasts produced in an operational scenario and provide metrics meaningful for operations. Upon completion, SPHINX and its interactive user interface, SPHINX-Web, will be made available for public use.

space weather↗

Statistical Issues for Uncontrolled Reentry Hazards

A number of statistical tools have been developed over the years for assessing the risk of reentering objects to human populations. These tools make use of the characteristics (e.g., mass, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. This inevitably involves assumptions that simplify the problem and make it tractable, but it is often difficult to test the accuracy and applicability of these assumptions. This paper looks at a number of these theoretical assumptions, examining the mathematical basis for the hazard calculations, and outlining the conditions under which the simplifying assumptions hold. In addition, this paper will also outline some new tools for assessing ground hazard risk in useful ways. Also, this study is able to make use of a database of known uncontrolled reentry locations measured by the United States Department of Defense. By using data from objects that were in orbit more than 30 days before reentry, sufficient time is allowed for the orbital parameters to be randomized in the way the models are designed to compute. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors - including the effects of gravitational harmonics, the effects of the Earth's equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and change the ground footprints. The measured latitude and longitude distributions of these objects provide data that can be directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.

Matney, Mark↗

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗

Preliminary design package for Sunair SEC-601 solar collector

The preliminary design of the Owens-Illinois model Sunair SEC-601 tubular air solar collector is presented. Information in this package includes the subsystem design and development approaches, hazard analysis, and detailed drawings available as the preliminary design review.

Source record↗

Forestry timber typing. Tanana demonstration project, Alaska ASVT

The feasibility of using LANDSAT digital data in conjunction with topographic data to delineate commercial forests by stand size and crown closure in the Tanana River basin of Alaska was tested. A modified clustering approach using two LANDSAT dates to generate an initial forest type classification was then refined with topographic data. To further demonstrate the ability of remotely sensed data in a fire protection planning framework, the timber type data were subsequently integrated with terrain information to generate a fire hazard map of the study area. This map provides valuable assistance in initial attack planning, determining equipment accessibility, and fire growth modeling. The resulting data sets were incorporated into the Alaska Department of Natural Resources geographic information system for subsequent utilization.

Morrissey, L. A.↗

Hazardous Environment Robotics

Jet Propulsion Laboratory (JPL) developed video overlay calibration and demonstration techniques for ground-based telerobotics. Through a technology sharing agreement with JPL, Deneb Robotics added this as an option to its robotics software, TELEGRIP. The software is used for remotely operating robots in nuclear and hazardous environments in industries including automotive and medical. The option allows the operator to utilize video to calibrate 3-D computer models with the actual environment, and thus plan and optimize robot trajectories before the program is automatically generated.

Source record↗

Orbital Debris Quarterly News: No. 1 - Volume 13

Topics discussed include: new debris from a decommissioned satellite with a nuclear power source; debris from the destruction of the Fengyun-1C meteorological satellite; quantitative analysis of the European Space Agency's Automated Transfer Vehicle 'Jules Verne' reentry event; microsatellite impact tests; solar cycle 24 predictions and other long-term projections and geosynchronus (GEO) environment for the Orbital Debris Engineering Model (ORDEM2008). Abstracts from the NASA Orbital Debris Program Office, examining satellite reentry risk assessments and statistical issues for uncontrolled reentry hazards, are also included.

Liou, J.-C.↗

The Staring OBservations of the Atmosphere (SOBA) Mission Concept

The Staring OBservations of the Atmosphere (SOBA) Mission is a concept that was developed and matured under the guidance of the NASA Ames Project EXellence (APEX) program. If funded, it will provide an unprecedented opportunity to improve ash transport forecasts and climate model simulations associated with volcanic eruptions. NASA and National science objectives require a better understanding of volcanic aerosol and trace gas emissions, transport, chemical transformation, and deposition, since they impact Earth's climate and atmospheric composition, human health, and aviation safety. Natural hazards such as the 2010 eruption of the Eyjafjallajökull volcano in Iceland demonstrated how existing remote-sensing assets were inadequate for individual volcanic event monitoring. During this eruption, available instruments were unable to provide data necessary to initialize volcanic plume transport models so that they could accurately predict the quantity and location of volcanic ash. As a result, thousands of flights around the world were grounded unnecessarily, at great expense. Volcanoes can also play a large role in regulation of the Earth's climate, so SOBA observations will also be used to evaluate and improve volcanic aerosol and trace gas simulation in chemical transport models (CTMs) and global climate models (GCMs). We propose the development of an airborne remote sensing concept and field campaign that will respond to an eruption and provide near real time observations of a volcanic plume, specifically ash injection height, transport, aerosol microphysical physical properties, and the location and concentration of sulfur dioxide (SO2) (sulfate (SO42-) aerosol precursor). This airborne system will utilize a depolarization sensitive, downward looking Light Detection And Ranging (lidar) instrument and an ultraviolet (UV) imaging spectrometer, and will provide data to be ingested by volcanic ash advisory models. Furthermore, the lessons learned in the development of this system could eventually guide regular deployment of similar systems by NASA or other government agencies.

Aerosols↗

Understanding the Impact of Meteorological Spatiotemporal Variability on Distant Focusing Overpressure Risk

Since the 1950s, the amplification of blast energy from explosions has been understood to be a significant hazard to public safety at launch ranges. Historically, the risk assessment of the Distant Focusing Overpressure (DFO) hazard started with a single temperature and wind profile (e.g., a radiosonde launch) as input to acoustic 1-D ray tracing models. By analyzing rays propagation and performing ray tracing, population centers under high DFO risk can be identified. Although this method is useful, less is known about how the blast waves can be focused when the spatiotemporal variability of the input profiles are considered. In summary, this work aims to consider how realistic atmospheric boundary layer variability (e.g., turbulence, land-surface contrasts) may affect blast waves propagation and focusing and, as a result, DFO risk assessment results.

Blumberg, W. Greg↗

Micro-meteoroid and orbital debris radar from Goldstone Radar observations

Micro-meteoroid and orbital debris (MMOD) refers to millions of micrometeoroids and orbital debris orbiting the Earth, which can come from remaining parts of spacecraft from previous missions. These debris objects keep colliding and creating new pieces of debris, with very different sizes ranging from millimeters to meters. About 99.3% of the debris objects have a size < 1 cm. These debris objects, traveling at speeds of 10 km/s, can cause significant damage to spacecraft, satellites, and astronauts. Despite the historic use of shielding to protect vital spacecraft components, MMOD still present a potential hazard for future missions and operations involving humans in space. Goldstone’s orbital debris radar (ODR) enhances the NASA orbital debris model with radar observation data, providing vital information on orbital debris detections: size, Doppler, range, and orbit inclination angle. These observations currently provide the only access to information about this size class (2 mm to 10 mm) of orbital debris. Goldstone’s ODR experiments operate as a bistatic radar, with overlapping beams providing a wide distribution of ranges. From 2004 to early 2018, Goldstone’s ODR has been collecting orbital debris data from altitudes covering 280 km to 3000 km. Since the decommissioning of the receive antenna in early 2018, Goldstone’s range coverage is reduced to a 300 km window within a 600 km to 1000 km potential range coverage, depending on the pointing geometry and antenna station used. We are investigating alternative transmit receive configurations at Goldstone to recover our previous wide range coverage.

Rodriguez-Alvarez, Nereida↗

Shoemaker-Levy 9 and Plume-Forming Collisions on Earth

Computational models for the July, 1994 collision of comet Shoemaker Levy 9 with Jupiter have provided a framework for interpreting the observational data. Imaging, photometry, and spectroscopy data from ground-based, Hubble Space Telescope, and Galileo spacecraft instruments are consistent with phenomena that were dominated by the generation of incandescent fireballs that were ballistically ejected to high altitudes, where they formed plumes that subsequently collapsed over large areas of Jupiter's atmosphere. Applications of similar computational models to collisions into Earth's atmosphere show that a very similar sequence of events should take place for NEO impacts with energies as low as 3 megatons, recurring on 100 year timescales or less. This result suggests that the 1908 Tunguska event was a plume-forming atmospheric explosion, and that some of the phenomena associated with it might be related to the ejection and collapse of a high plume. Hazards associated with plume growth and collapse should be included in the evaluation of the impact threat to Earth, and opportunities should be sought for observational validation of atmospheric impact models by exploiting data already being collected from the natural flux of multikiloton to megaton sized objects that constantly enter Earth's atmosphere on annual to decadal timescales.

Boslough, Mark B. E.↗

Interface for Physics Simulation Engines

DSS-Prototyper is an open-source, realtime 3D virtual environment software that supports design simulation for the new Vision for Space Exploration (VSE). This is a simulation of NASA's proposed Robotic Lunar Exploration Program, second mission (RLEP2). It simulates the Lunar Surface Access Module (LSAM), which is designed to carry up to four astronauts to the lunar surface for durations of a week or longer. This simulation shows the virtual vehicle making approaches and landings on a variety of lunar terrains. The physics of the descent engine thrust vector, production of dust, and the dynamics of the suspension are all modeled in this set of simulations. The RLEP2 simulations are drivable (by keyboard or joystick) virtual rovers with controls for speed and motor torque, and can be articulated into higher or lower centers of gravity (depending on driving hazards) to enable drill placement. Gravity also can be set to lunar, terrestrial, or zero-g. This software has been used to support NASA's Marshall Space Flight Center in simulations of proposed vehicles for robotically exploring the lunar surface for water ice, and could be used to model all other aspects of the VSE from the Ares launch vehicles and Crew Exploration Vehicle (CEV) to the International Space Station (ISS). This simulator may be installed and operated on any Windows PC with an installed 3D graphics card.

Damer, Bruce↗

An Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant damage to infrastructure and economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures. Situational awareness in near real-time is essential to ensure community resilience and enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions at regional to global scale. Given the global coverage of two operational flood models - GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the first component of this project first focuses on integrating the outputs from these two models to classify flood severity and send alerts based on potential for impacts similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes). The second component of the project focuses on using flood outputs derived from earth observation data to validate, update and add additional exposure and impact products to flood alerts. The flood impacts and severity information will be disseminated as alerts and maps through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. In this presentation, the following objectives will be covered: (i) the effective integration of the flood models, (ii) accuracy of the flood model outputs (flood extent and depth) for specific flood events both in the United States and globally in comparison with each other and earth observation data.

Glasscoe, Margaret↗

Characterizing the Preturbulence Environment for Sensor Development, New Hazard Algorithms and NASA Experimental Flight Planning

During the grant period, several tasks were performed in support of the NASA Turbulence Prediction and Warning Systems (TPAWS) program. The primary focus of the research was on characterizing the preturbulence environment by developing predictive tools and simulating atmospheric conditions that preceded severe turbulence. The goal of the research being to provide both dynamical understanding of conditions that preceded turbulence as well as providing predictive tools in support of operational NASA B-757 turbulence research flights. The advancements in characterizing the preturbulence environment will be applied by NASA to sensor development for predicting turbulence onboard commercial aircraft. Numerical simulations with atmospheric models as well as multi-scale observational analyses provided insights into the environment organizing turbulence in a total of forty-eight specific case studies of severe accident producing turbulence on commercial aircraft. These accidents exclusively affected commercial aircraft. A paradigm was developed which diagnosed specific atmospheric circulation systems from the synoptic scale down to the meso-y scale that preceded turbulence in both clear air and in proximity to convection. The emphasis was primarily on convective turbulence as that is what the TPAWS program is most focused on in terms of developing improved sensors for turbulence warning and avoidance. However, the dynamical paradigm also has applicability to clear air and mountain turbulence. This dynamical sequence of events was then employed to formulate and test new hazard prediction indices that were first tested in research simulation studies and then ultimately were further tested in support of the NASA B-757 turbulence research flights. The new hazard characterization algorithms were utilized in a Real Time Turbulence Model (RTTM) that was operationally employed to support the NASA B-757 turbulence research flights. Improvements in the RTTM were implemented in an effort to increase the accuracy of the operational characterization of the preturbulence environment. Additionally, the initial research necessary to create a statistical evaluation scheme for the characterization indices utilized in the RTTM was undertaken. Results of all components of this research were then published in NASA contractor reports and scientific journal papers.

Kaplan, Michael L.↗

Modeling the Prodromal Effects and Performance Reduction of Astronauts from Exposure to Large Solar Particle Events

In space exploration outside the Earth's geomagnetic field, radiation exposure from solar particle events (SPE) presents a health concern for astronauts, that could impair their performance and result in possibility of failure of the mission. Acute risks are especially of concern during spacewalks on the lunar surface because of the rapid onset of SPE's and science goals that involve long distances to crew habitats. Thus assessing the potential of early radiation effect under such adverse conditions is of prime importance. Here we present a biologic based mathematical model which describes the dose and time-dependent early human responses to ionizing radiation. We examine the possible early effects on crew behind various shielding materials from exposure to some historical large SPEs on the lunar and Mars surfaces. The doses and dose rates were calculated using the BRYNTRN code (Kim, M.Y, Hu, X, and Cucinotta, F.A, Effect of Shielding Materials from SPEs on the Lunar and Mars Surface, AIAA Space 2005, paper number AIAA-2005-6653, Long Beach, CA, August 30-September 1, 2005) and the hazard of the early radiation effects and performance reduction were calculated using the RIPD code (Anno, G.H, McClellan, G.E., Dore, M.A, Protracted Radiation-Induced Performance Decrement, Volume 1 Model Development,1996, Defense Nuclear Agency: Alexandria VA). Based on model assumptions we show that exposure to these historical SPEs do cause early effects to crew members and impair their performance if effective shielding and medical countermeasure tactics are not provided. The calculations show multiple occurrence of large SPEs in a short period of time significantly increase the severity of early illness, however early death from failure of the hematopoietic system is very unlikely because of the dose-rate and dose heterogeneity of SPEs. Results from these types of calculations will be a guide in design of protection systems and medical response strategy for astronauts in case of exposure to high dose irradiation during future space missions.

Hu, S.↗