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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Duda, David P.↗
NASA NTRS2010

Exhaustive Versus Randomized Searchers for Nonlinear Optimization in 21st Century Computing: Solar Application

We present a simple multi-dimensional exhaustive search method to obtain, in a reasonable time, the optimal solution of a nonlinear programming problem. It is more relevant in the present day non-mainframe computing scenario where an estimated 95% computing resources remains unutilized and computing speed touches petaflops. While the processor speed is doubling every 18 months, the band width is doubling every 12 months, and the hard disk space is doubling every 9 months. A randomized search algorithm or, equivalently, an evolutionary search method is often used instead of an exhaustive search algorithm. The reason is that a randomized approach is usually polynomial-time, i.e., fast while an exhaustive search method is exponential-time i.e., slow. We discuss the increasing importance of exhaustive search in optimization with the steady increase of computing power for solving many real-world problems of reasonable size. We also discuss the computational error and complexity of the search algorithm focusing on the fact that no measuring device can usually measure a quantity with an accuracy greater than 0.005%. We stress the fact that the quality of solution of the exhaustive search - a deterministic method - is better than that of randomized search. In 21 st century computing environment, exhaustive search cannot be left aside as an untouchable and it is not always exponential. We also describe a possible application of these algorithms in improving the efficiency of solar cells - a real hot topic - in the current energy crisis. These algorithms could be excellent tools in the hands of experimentalists and could save not only large amount of time needed for experiments but also could validate the theory against experimental results fast.

Sen, Syamal K.↗
NASA NTRS2009

A Multivariate Randomization Text of Association Applied to Cognitive Test Results

Randomization tests provide a conceptually simple, distribution-free way to implement significance testing. We have applied this method to the problem of evaluating the significance of the association among a number (k) of variables. The randomization method was the random re-ordering of k-1 of the variables. The criterion variable was the value of the largest eigenvalue of the correlation matrix.

Ahumada, Albert↗
NASA NTRS2011

Numerically Exact Computer Simulations of Light Scattering by Densely Packed, Random Particulate Media

Direct computer simulations of electromagnetic scattering by discrete random media have become an active area of research. In this progress review, we summarize and analyze our main results obtained by means of numerically exact computer solutions of the macroscopic Maxwell equations. We consider finite scattering volumes with size parameters in the range, composed of varying numbers of randomly distributed particles with different refractive indices. The main objective of our analysis is to examine whether all backscattering effects predicted by the low-density theory of coherent backscattering (CB) also take place in the case of densely packed media. Based on our extensive numerical data we arrive at the following conclusions: (i) all backscattering effects predicted by the asymptotic theory of CB can also take place in the case of densely packed media; (ii) in the case of very large particle packing density, scattering characteristics of discrete random media can exhibit behavior not predicted by the low-density theories of CB and radiative transfer; (iii) increasing the absorptivity of the constituent particles can either enhance or suppress typical manifestations of CB depending on the particle packing density and the real part of the refractive index. Our numerical data strongly suggest that spectacular backscattering effects identified in laboratory experiments and observed for a class of high-albedo Solar System objects are caused by CB.

Dlugach, Janna M.↗
NASA NTRS2016

Analysis of Random Segment Errors on Coronagraph Performance

At 2015 SPIE O&P we presented "Preliminary Analysis of Random Segment Errors on Coronagraph Performance" Key Findings: Contrast Leakage for 4thorder Sinc2(X) coronagraph is 10X more sensitive to random segment piston than random tip/tilt, Fewer segments (i.e. 1 ring) or very many segments (> 16 rings) has less contrast leakage as a function of piston or tip/tilt than an aperture with 2 to 4 rings of segments. Revised Findings: Piston is only 2.5X more sensitive than Tip/Tilt

Technology↗
NASA NTRSDate not supplied

Random Vibration Simulation and Testing of a Compact, Magnetic Bearing Supported Blower for Space Applications

NASA is developing a next-generation CO2 removal system, the Four Bed Carbon Dioxide Scrubber (4BCO2), which will use the International Space Station (ISS) as a testbed. A key component of both the existing and the new system is the blower that provides the airflow through the CO2 sorbent beds. To improve performance and reliability, magnetic bearings will be used in lieu of more conventional bearings (e.g. ball bearings or hydrodynamicbearings) to improve resistance to contaminants and enable extensibility with regards to blower speed, pressure rise and mass flow rate. The new blower features a high-efficiency permanent magnet motor, a five-axis, active magnetic bearing system, and a compact controller containing both a variable speed drive and magnetic bearing controller. The blower uses a centrifugal impeller to pull air from the inlet port and drive it through an annular space around the motor and magnetic bearing components to the exhaust port. Technical challenges of the blower and controller development include survival of the blower system under launch random vibration loads, operation in microgravity, packaging under strict size and weight requirements, and successful operation during 4BCO2 operational changeovers. Each serial build of the blower must pass a severe random vibration test to prove it will survive launch conditions. This paper discusses the vibration test requirements and provides details of a simulation performed to estimate the peak backup bearing displacements and loads. Results from the successful random vibration testing of the prototype build are discussed and compared to the simulation.

bearings↗
NASA NTRS2021

Center of Gravity Quasi-Static Load Estimate Using Random Vibration Responses

The quasi-static load (QSL) is often levied on flight hardware structural designers as a requirement. This requirement is verified either by test or analysis with engineers using different approaches to meet this requirement. A commonly used approach is to consider the rms acceleration Center of Gravity (CG) responses of test articles undergoing random vibration tests. The differences between structural engineers and/or test conductors within the space industry exist on how the data obtained from flight hardware random vibration tests are used to estimate QSLs. In this paper, a method to accurately estimate the CG grms from the acceleration power spectral densities with an appropriate sigma value to estimate peak CG accelerations is discussed. Recommendations are made to ensure that the community follows consistent QSL estimation from random vibration test data. The method of estimating the CG acceleration responses may also be used to response limit if the flight hardware responses exceed flight limit loads with margins.

Kolaini, Ali↗
NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗
NASA NTRS2026

Methods for Validation of Random Uncertainty Estimates and Their Applications to Ozone Profiles From Limb-Viewing Satellite Instruments

For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or the presence of unknown error sources. This paper presents an overview of the methods used for the validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed. This overview evaluates these methods in application to ozone profile measurements from limb and occultation satellite instruments and provides practical illustrations of random uncertainty validation.

Viktoria F Sofieva↗
NASA NTRS2025

Assembly of catalytic complexes from randomized oligonucleotides

The early evolution of life relied on catalytic RNAs (ribozymes) for central functions. To test whether early catalysts could have assembled from multiple short nucleic acid fragments in random sequence environments, we performed an in vitro selection from a short RNA library in the presence of 256 different DNA 20-nucleotide oligomers. High-throughput sequencing and biochemical analysis showed that most of the selected 1331 RNA sequences required at least one DNA for activity. Representatives for four of six RNA clusters that depended on DNA cofactors were active even when the 256 DNAs were replaced by completely random DNA 20-nucleotide oligomers. The formation of these catalytic complexes and the recruitment of oligonucleotide cofactors from completely random libraries demonstrate an important principle for the emergence of the earliest oligonucleotide catalysts.

Xu Han↗
NASA NTRS2008

Random Test Run Length and Effectiveness

A poorly understood but important factor in many applications of random testing is the selection of a maximum length for test runs. Given a limited time for testing, it is seldom clear whether executing a small number of long runs or a large number of short runs maximizes utility. It is generally expected that longer runs are more likely to expose failures -- which is certainly true with respect to runs shorter than the shortest failing trace. However, longer runs produce longer failing traces, requiring more effort from humans in debugging or more resources for automated minimization. In testing with feedback, increasing ranges for parameters may also cause the probability of failure to decrease in longer runs. We show that the choice of test length dramatically impacts the effectiveness of random testing, and that the patterns observed in simple models and predicted by analysis are useful in understanding effects observed.

software testing↗
NASA NTRS1969

Automatic Gaussian random-noise limiter

Circuit limits the positive and negative peaks of a random-noise signal. It accurately establishes a known limiting level relative to any given RMS value of the random-noise signal input.

Woodbury, R. C.↗
NASA NTRS1970

Random operator equations in mathematical physics. I

Stochastic differential equations objectives, limitations and restrictive assumptions in physical problems, discussing electromagnetic wave propagation in random continuum or random dAlembertian operator

Adomian, G.↗