Results for “conditional probability” — Page 32

NASA NTRS2005

Optimal Sampling of a Reaction Coordinate in Molecular Dynamics

Estimating how free energy changes with the state of a system is a central goal in applications of statistical mechanics to problems of chemical or biological interest. From these free energy changes it is possible, for example, to establish which states of the system are stable, what are their probabilities and how the equilibria between these states are influenced by external conditions. Free energies are also of great utility in determining kinetics of transitions between different states. A variety of methods have been developed to compute free energies of condensed phase systems. Here, I will focus on one class of methods - those that allow for calculating free energy changes along one or several generalized coordinates in the system, often called reaction coordinates or order parameters . Considering that in almost all cases of practical interest a significant computational effort is required to determine free energy changes along such coordinates it is hardly surprising that efficiencies of different methods are of great concern. In most cases, the main difficulty is associated with its shape along the reaction coordinate. If the free energy changes markedly along this coordinate Boltzmann sampling of its different values becomes highly non-uniform. This, in turn, may have considerable, detrimental effect on the performance of many methods for calculating free energies.

Pohorille, Andrew↗

NASA NTRS2011

The Integrated Medical Model: A Risk Assessment and Decision Support Tool for Human Space Flight Missions

This slide presentation reviews the Integrated Medical Model (IMM) and its use as a risk assessment and decision support tool for human space flight missions. The IMM is an integrated, quantified, evidence-based decision support tool useful to NASA crew health and mission planners. It is intended to assist in optimizing crew health, safety and mission success within the constraints of the space flight environment for in-flight operations. It uses ISS data to assist in planning for the Exploration Program and it is not intended to assist in post flight research. The IMM was used to update Probability Risk Assessment (PRA) for the purpose of updating forecasts for the conditions requiring evacuation (EVAC) or Loss of Crew Life (LOC) for the ISS. The IMM validation approach includes comparison with actual events and involves both qualitative and quantitaive approaches. The results of these comparisons are reviewed. Another use of the IMM is to optimize the medical kits taking into consideration the specific mission and the crew profile. An example of the use of the IMM to optimize the medical kits is reviewed.

Kerstman, Eric L.↗

NASA NTRS2012

Development of Standards for Nondestructive Evaluation of COPVs Used in Aerospace Applications

Composite OverWrapped Pressure Vessels (COPVs) are currently accepted by NASA based on design and qualification requirements and generally not verified by NDE for the following reasons: (1) Manufactures and end users generally do not have experience and validated quantitative methods of detecting flaws and defects of concern (1-a) If detected, the flaws are not adequately quantified and it is unclear how they may contribute to degradation in mechanical response (1-b) Carbon-epoxy COPVs also extremely sensitive to impact damage and impacts may be below the visible detection threshold (2) If damage is detected, this generally results in rejection since the effect on mechanical response is generally not known (3) NDE response has not generally been fully characterized, probability of detection (POD) established, and processes validated for evaluation of vessel condition as manufactured and delivered.

Waller, Jess M.↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

NASA NTRS2021

Loss of Control Detection for Commercial Transports Using Conditional Variational Autoencoders

This work describes a detector for the loss of control of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. We demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is experiencing a flight state change is approaching by measuring a shift in the sampling Gaussian distributions of the latent space. We provide an analysis of its applicability to flight data from a NASA generic commercial transport-type aircraft.

Newton H Campbell↗

NASA NTRSDate not supplied

IMPACT - Information for the Development of Human Health and Performance Systems

IMPACT is a suite of computational and systems engineering tools. Currently being developed for NASA Exploration Medical Capabilities (ExMC) and could be extended to other Human Research Program (HRP) elements. The purpose is to provide a data-driven means to inform human health and performance risk mitigation during exploration missions considering resource constraints in the medical system. IMPACT enables systematic trade studies to evaluate options. IMPACT uses Probabilistic Risk Assessment (PRA) as a systematic methodology to evaluate risks. IMPACT is currently under development to inform decision-makers the risks involved with trade-options that have likelihoods and consequences backed by scientific research in a medical evidence library. IMPACT supports multi-segment analysis to determine probabilities and risks in different mission segments. There are currently 122 medical conditions and 900 resources in the IMPACT Medical Database. IMPACT modeling is using mission segments and multiple vehicles for the NASA Artemis campaign and could support future complex missions in deep space.

IMPACT PRA↗

NASA NTRS2023

Assessing High Resolution Thermal Monitoring of Complex Intertidal Environments From Space: the Case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

ECOSTRESS↗

NASA NTRS1991

Physical and chemical properties of the Martian soil: Review of resources

The chemical and physical properties of Martian surface materials are reviewed from the perspective of using these resources to support human settlement. The resource potential of Martian sediments and soils can only be inferred from limited analyses performed by the Viking Landers (VL), from information derived from remote sensing, and from analysis of the SNC meteorites thought to be from Mars. Bulk elemental compositions by the VL inorganic chemical (x ray fluorescence) analysis experiments have been interpreted as evidence for clay minerals (possibly smectites) or mineraloids (palagonite) admixed with sulfate and chloride salts. The materials contained minerals bearing Fe, Ti, Al, Mg and Si. Martian surface materials may be used in many ways. Martian soil, with appropriate preconditioning, can probably be used as a plant growth medium, supplying mechanical support, nutrient elements, and water at optimal conditions to the plants. Loose Martian soils could be used to cover structures and provide radiation shielding for surface habitats. Martian soil could be wetted and formed into abode bricks used for construction. Duricrete bricks, with strength comparable to concrete, can probably be formed using compressed muds made from martian soil.

Stoker, C. R.↗

NASA NTRS1978

Direct measurements of the atmospheric conduction current

A method of measuring the atmospheric conduction current above the ground has been employed to obtain data for 12 weeks during the first half of 1974. The instrument consists of a split aluminum sphere suspended by insulated wires to a wooden frame. The measuring electronics and the transmitter are enclosed within the spherical structure. The interaction of the instrument with its atmospheric electrical environment is analyzed, and it is shown that in steady state conditions, predictable differences in the instrumentally measured currents and the atmospheric conduction current will be less than 5% and in the nonsteady state situations the difference is less than 20%. Diurnal variations, a probable winter-summer variation, sunrise, and fog effects were observed for the data obtained during fair-weather conditions. Disturbed weather data are interpreted for the effects of low clouds on the atmospheric current. The charge concentrations within overcast clouds sufficient to produce the observed reversed atmospheric currents are estimated to be small in relation to values in thunderclouds.

Burke, H. K.↗

NASA NTRS2019

Investigation of Transient Gas Phase Column Density Due to Droplet Evaporation

A NASA robotic refueling mission experiment on board the International Space Station (ISS) was designed to repeatedly transfer simulated cryogenic propellant between two dewars. After each metered transfer, the cryogen was to be vented overboard via sublimation or evaporation. ISS payload providers must conduct analyses to demonstrate that any planned gaseous venting generate no more than a certain level of material that may interfere with optical measurements from other payloads that may be located nearby. This requirement is expressed in terms of a maximum column number density (CND). For the experiment under consideration, fluid droplets likely would accompany any such vapor. Earlier related studies led to development of analytical expressions for column density along general paths for a number of practical cases under steady conditions, including droplet evaporation. These expressions are not appropriate for rapid evaporation of small droplets however, since the droplets cannot sustain mass flow rates consistent with steady conditions. In this work, analytical expressions are developed for estimating column density near a rapidly evaporating droplet along general paths. The influence for instantaneous evaporation is created first as a limiting case, where it is found the peak value occurs at the time it takes a wave of vapor to reach the closest point along the optical path traveling at its most probable thermal speed. Next, the case for finite-period evaporation is evaluated for constant conditions. Compared to the instantaneous case, peak column density occurs shortly after droplet extinction, but at a lower intensity. A new mathematical function is discovered that solves the integrals associated with this case. Finally, ways to account for droplet motion and changes in evaporation rate with size and temperature are discussed in this framework.

Woronowicz, M. S.↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRS1992

Atmospheric effects on the NDVI - Strategies for its removal

The compositing technique used to derive global vegetation index (NDVI) from the NOAA AVHRR radiances reduces the residual effect of water vapor and aerosol on the NDVI. The reduction in the atmospheric effect is shown using a comprehensive measured data set for desert conditions, and a simulation for grass with continental aerosol. A statistical analaysis of the probability of occurrence of aerosol optical thickness and precipitable water vapor measured in different climatic regimes is used for this simulation. It is concluded that for a long compositing period (e.g., 27 days), the residual aerosol optical thickness and precipitable water vapor are usually too small to be corrected. For a 9-day compositing, the residual average aerosol effect may be about twice the correction uncertainty. For Landsat TM or Earth Observing System Moderate Resolution Imaging Spectrometer (EOS-MODIS) data, the newly defined atmospherically resistant vegetation index (ARVI) is more promising than possible direct atmospheric correction schemes, except for heavy desert dust conditions.

Kaufman, Y. J.↗

NASA NTRS2014

Effect of Preconditioning and Soldering on Failures of Chip Tantalum Capacitors

Soldering of molded case tantalum capacitors can result in damage to Ta205 dielectric and first turn-on failures due to thermo-mechanical stresses caused by CTE mismatch between materials used in the capacitors. It is also known that presence of moisture might cause damage to plastic cases due to the pop-corning effect. However, there are only scarce literature data on the effect of moisture content on the probability of post-soldering electrical failures. In this work, that is based on a case history, different groups of similar types of CWR tantalum capacitors from two lots were prepared for soldering by bake, moisture saturation, and longterm storage at room conditions. Results of the testing showed that both factors: initial quality of the lot, and preconditioning affect the probability of failures. Baking before soldering was shown to be effective to prevent failures even in lots susceptible to pop-corning damage. Mechanism of failures is discussed and recommendations for pre-soldering bake are suggested based on analysis of moisture characteristics of materials used in the capacitors' design.

multilayer ceramic capacitor (MLCC)↗

NASA NTRS2003

Extension of Liouville Formalism to Postinstability Dynamics

A mathematical formalism has been developed for predicting the postinstability motions of a dynamic system governed by a system of nonlinear equations and subject to initial conditions. Previously, there was no general method for prediction and mathematical modeling of postinstability behaviors (e.g., chaos and turbulence) in such a system. The formalism of nonlinear dynamics does not afford means to discriminate between stable and unstable motions: an additional stability analysis is necessary for such discrimination. However, an additional stability analysis does not suggest any modifications of a mathematical model that would enable the model to describe postinstability motions efficiently. The most important type of instability that necessitates a postinstability description is associated with positive Lyapunov exponents. Such an instability leads to exponential growth of small errors in initial conditions or, equivalently, exponential divergence of neighboring trajectories. The development of the present formalism was undertaken in an effort to remove positive Lyapunov exponents. The means chosen to accomplish this is coupling of the governing dynamical equations with the corresponding Liouville equation that describes the evolution of the flow of error probability. The underlying idea is to suppress the divergences of different trajectories that correspond to different initial conditions, without affecting a target trajectory, which is one that starts with prescribed initial conditions.

Zak, Michail↗

NASA NTRS1983

Effect of oxygen concentration in ZDP containing oils on surface composition and wear

A pin-on-disk wear study was performed with the lubricants dibutyl sebacate (DBS) and mineral oil (MO) with and without 1 weight percent zinc-dialkyl-dithiophospatee (ZDP) as an additive. The pin was annealed pure iron and the disk was M-2 tool steel. The selected load and speed guaranteed boundary lubrication. The ambient atmospheric oxygen concentration in an oxygen-nitrogen mixture was varied from 0 percent to 20 percent in order to examine its relationship to ZDP effectiveness. Auger electron spectroscopy combined with argon ion bombardment (depth profiling) was used to determine surface elemental composition on the pin when tested in DBS with and without ZDP. The ambient atmosphere was found to cause large variations in wear rate and surface composition. With MO, ZDP reduced wear under all conditions, but had little advantage over oxides formed at 20 percent oxygen atmosphere. With DBS, ZDP reduced wear at 0 percent oxygen, but gave varied results at other oxygen concentrations. Depth profiling revealed sulfuide formation at 0 percent oxygen and probably sulfates at 20 percent oxygen. The results are significant because varied oxygen concentrations can occur under actual lubricating conditions in practical machinery.

Brainard, W. A.↗