Selection from multivariate normal populations.
Selection probability from multivariable normal populations in terms of noncentrality parameters
POPULATION↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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An investigation of the internal and external aerodynamics of cattle trucks
Wind tunnel tests were conducted on a one-tenth scale model of a conventional tractor trailer livestock hauler to determine the air flow through the trailer and the drag of the vehicle. These tests were conducted with the trailer empty and with a full load of simulated cattle. Additionally, the drag was determined for six configurations, of which details for three are documented herein. These are: (1) conventional livestock trailer empty, (2) conventional trailer with smooth sides (i.e., without ventilation openings), and (3) a stream line tractor with modified livestock trailer (cab streamlining and gap fairing). The internal flow of the streamlined modification with simulated cattle was determined with two different ducting systems: a ram air inlet over the cab and NACA submerged inlets between the cab and trailer. The air flow within the conventional trailer was random and variable. The streamline vehicle with ram air inlet provided a nearly uniform air flow which could be controlled. The streamline vehicle with NACA submerged inlets provided better flow conditions than the conventional livestock trailer but not as uniform or controllable as the ram inlet configuration.
Muirhead, V. U.↗
Reliability analysis in intelligent machines
Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.
Mcinroy, John E.↗
Fatigue crack growth under variable amplitude loading
Fatigue crack growth tests were conducted on an Fe 510 E C-Mn steel and a submerged arc welded joint from the same material under constant, variable, and random loading amplitudes. Paris-Erdogan's crack growth rate law was tested for the evaluation of m and C using the stress intensity factor K, the J-integral, the effective stress intensity factor K(sub eff), and the root mean square stress intensity factor K(sub rms) fracture mechanics concepts. The effect of retardation and residual stresses resulting from welding was also considered. It was found that all concepts gave good life predictions in all cases.
Sidawi, Jihad A.↗
An Approach to Simulation of Extreme Conditions for a Planetary Lander
A "Monte Carlo-like" design analysis tool is developed and applied to an aeromaneuvering Mars entry vehicle. This tool provides realistic but challenging design cases using many fewer cases than a full Monte Carlo analysis. The problem of random input variables that are provided a priori (as opposed to being drawn from a given distribution) is addressed and a solution is found that shows prospects for future improvement.
Queen, Eric M.↗
Life Predicted in a Probabilistic Design Space for Brittle Materials With Transient Loads
Analytical techniques have progressively become more sophisticated, and now we can consider the probabilistic nature of the entire space of random input variables on the lifetime reliability of brittle structures. This was demonstrated with NASA s CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code combined with the commercially available ANSYS/Probabilistic Design System (ANSYS/PDS), a probabilistic analysis tool that is an integral part of the ANSYS finite-element analysis program. ANSYS/PDS allows probabilistic loads, component geometry, and material properties to be considered in the finite-element analysis. CARES/Life predicts the time dependent probability of failure of brittle material structures under generalized thermomechanical loading--such as that found in a turbine engine hot-section. Glenn researchers coupled ANSYS/PDS with CARES/Life to assess the effects of the stochastic variables of component geometry, loading, and material properties on the predicted life of the component for fully transient thermomechanical loading and cyclic loading.
Nemeth, Noel N.↗
Total Variance: A Progress Report on a New Frequency Stability Characterization
Explore the source record for details and available documents.
Total variance random variable function of tau All↗
Probabilistic Independence Networks for Hidden Markov Probability Models
In this paper we explore hidden Markov models(HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general enference algorithms for arbitrary PINs.
random variables pattern recognition signal proces↗
A Maximum Likelihood Approach to Determine Sensor Radiometric Response Coefficients for NPP VIIRS Reflective Solar Bands
Optical sensors aboard Earth orbiting satellites such as the next generation Visible/Infrared Imager/Radiometer Suite (VIIRS) assume that the sensors radiometric response in the Reflective Solar Bands (RSB) is described by a quadratic polynomial, in relating the aperture spectral radiance to the sensor Digital Number (DN) readout. For VIIRS Flight Unit 1, the coefficients are to be determined before launch by an attenuation method, although the linear coefficient will be further determined on-orbit through observing the Solar Diffuser. In determining the quadratic polynomial coefficients by the attenuation method, a Maximum Likelihood approach is applied in carrying out the least-squares procedure. Crucial to the Maximum Likelihood least-squares procedure is the computation of the weight. The weight not only has a contribution from the noise of the sensor s digital count, with an important contribution from digitization error, but also is affected heavily by the mathematical expression used to predict the value of the dependent variable, because both the independent and the dependent variables contain random noise. In addition, model errors have a major impact on the uncertainties of the coefficients. The Maximum Likelihood approach demonstrates the inadequacy of the attenuation method model with a quadratic polynomial for the retrieved spectral radiance. We show that using the inadequate model dramatically increases the uncertainties of the coefficients. We compute the coefficient values and their uncertainties, considering both measurement and model errors.
Lei, Ning↗
Uncertainty Quantification of the FUN3D-Predicted NASA CRM Flutter Boundary
A nonintrusive point collocation method is used to propagate parametric uncertainties of the flexible Common Research Model, a generic transport configuration, through the unsteady aeroelastic CFD solver FUN3D. A range of random input variables are considered, including atmospheric flow variables, structural variables, and inertial (lumped mass) variables. UQ results are explored for a range of output metrics (with a focus on dynamic flutter stability), for both subsonic and transonic Mach numbers, for two different CFD mesh refinements. A particular focus is placed on computing failure probabilities: the probability that the wing will flutter within the flight envelope.
Stanford, Bret K.↗
Remote Linkages to Anomalous Winter Atmospheric Ridging over the Northeastern Pacific
Severe drought in California between 2013 and 2016 has been linked to the multiyear persistence of anomalously high atmospheric pressure over the northeastern Pacific Ocean, which deflected the Pacific storm track northward and suppressed regional precipitation during California's winter 'rainy season.' Multiple hypotheses have emerged regarding why this high pressure ridge near the west coast of North America was so resilient-including unusual sea surface temperature patterns in the Pacific Ocean, reductions in Arctic sea ice, random atmospheric variability, or some combination thereof. Here we explore relationships between previously documented atmospheric conditions over the North Pacific and several potential remote oceanic and cryospheric influences using both observational data and a large ensemble of climate model simulations. Our results suggest that persistent wintertime atmospheric ridging similar to that implicated in California's 2013-2016 drought can at least partially be linked to unusual Pacific sea surface temperatures, and that Pacific Ocean conditions may offer some degree of cool-season foresight in this region despite the presence of substantial internal variability.
California drought;Atmospheric ridging;Ocean-atmos↗
Applying Simulated Annealing to Problems in Model-Based Diagnosis
Generating all diagnoses is computationally intractable. Therefore, many of the state-of-the-art approaches are incomplete. Quantum computers may however offer a solution. The first commercially available quantum computer is being used to minimize polynomials that are difficult for classical simulated annealing but easy for quantum annealing. All problems in Model-based Diagnosis (MBD) can be transformed into a polynomial minimization problem, allowing one to apply a quantum algorithm called quantum annealing to solve MBD problems. To better understand the need for this quantum approach, we designed two simulated annealingdiagnostic algorithms tailored to run on a polynomial representation of MBD. These algorithms differ on their policy for random neighborhood variable selection. In addition, enhanced metrics were devised to provide more diagnostic coverage. Finally, these two simulated annealing algorithms were analyzed and empirically evaluated and compared against state-of-the-art probabilistic methods for MBD such as SAFARI using ISCAS-85.
Simulated annealing↗
Interannual variability of global dust storms on Mars
Under the assumption that the cross-equatorial Hadley circulation plays a key role in the onset of Martian global dust storms, numerical simulations indicate that a northern hemisphere dust haze weakens its intensity and, thereby, its contribution to the surface stress in the southern hemisphere. Since this in turn reduces the possibility of global dust storm development, the interannual variability observed is the result of either a competition between circulations in opposite hemispheres, in which case the variability has a random component, or it is the result of dust cycling between hemispheres, so that the variability is related to the characteristics of the global dust storms themselves.
Haberle, Robert M.↗
Interannual variability of global dust storms on Mars
Global dust storms on Mars occur in some years but not in others. In years with global dust storms, dust is raised in the Southern Hemisphere and spread over much of the planet by an intensified Hadley circulation. In years without global dust storms, dust is raised in the Northern Hemisphere by relatively active midlatitude storm systems, but does not spread globally. In both cases the dusty season is winter in the north. It is shown from numerical simulations that a Northern Hemisphere dust haze weakens the intensity of the cross equatorial Hadley circulation and the contribution it makes to the surface stress in the Southern Hemisphere. This, in turn, reduces the possibility of global dust storm development. The interannual variability is the result either of a competition between circulation in opposite hemispheres, in which case the variability has a random component; or it is the result of the cycling of dust between hemispheres, in which case the variability is related to the characteristics of global dust storms themselves.
Haberle, R. M.↗
Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization
Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.
3D seismic↗
H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays
This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Integrated Logistics Support Analysis of the International Space Station Alpha, Background and Summary of Mathematical Modeling and Failure Density Distributions Pertaining to Maintenance Time Dependent Parameters
The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.
Sepehry-Fard, F.↗
Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning
Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R 2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R 2 = 0.23) and SoilGrids 2.0 ( R 2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.
58 GEOSCIENCES↗