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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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It was shown that clerical personnel can be tested for proofreading performance under simulated industrial conditions. A statistical study showed that errors in proofreading follow an extreme value probability theory. The study showed that innovative man/machine interfaces can be developed to improve and control accuracy during data entry.
Radar interferometry based on (near) exact repeat passes has lately been used by many groups of scientists, worldwide, to achieve state of the art measurements of topography, glacier and ice stream motion, earthquake displacements, oil field subsidence, lava flows, crop-induced surface decorrelation, and other effects. Variations of tropospheric and ionospheric propagation delays limit the accuracy of all such measurements. We are investigating the extent of this limitation, using data from the Shuttle radar flight, SIR-C, which is sensitive to the troposphere, and the Earth Resources Satellites, ERS-1/2, which are sensitive to both the troposphere and the ionosphere. We are presently gathering statistics of the delay variations over selected, diverse areas to determine the best accuracy possible for repeat track interferometry. The phases of an interferogram depend on both the topography of the scene and variations in propagation delay. The delay variations can be caused by movement of elements in the scene, by changes in tropospheric water vapor and by changes of the charge concentrations in the ionosphere. We plan to separate these causes by using the data from a third satellite visit (three-pass interferometry). The figure gives the geometry of the three-pass observations. The page of the figure is taken to be perpendicular to the spacecraft orbits. The three observational locations are marked on the figure, giving baselines B-12 and B-13, separated by the angle alpha. These parameters are almost constant over the whole scene. However, each pixel has an individual look angle, theta, which is related to the topography, rho is the slant range. A possible spurious time delay is shown. Additional information is contained in the original.
CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.
A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.
A relatively simple, inexpensive, and generic technique that could be used in both laboratories and some operation site environments is introduced at the Robotics Applications and Development Laboratory (RADL) at Kennedy Space Center (KSC). In addition, this report gives a detailed explanation of the set up procedure, data collection, and analysis using this new technique that was developed at the State University of New York at Farmingdale. The technique was used to evaluate the repeatability, accuracy, and overshoot of the Unimate Industrial Robot, PUMA 500. The data were statistically analyzed to provide an insight into the performance of the systems and components of the robot. Also, the same technique was used to check the forward kinematics against the inverse kinematics of RADL's PUMA robot. Recommendations were made for RADL to use this technique for laboratory calibration of the currently existing robots such as the ASEA, high speed controller, Automated Radiator Inspection Device (ARID) etc. Also, recommendations were made to develop and establish other calibration techniques that will be more suitable for site calibration environment and robot certification.
A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.
The current cosmological probes have provided a fantastic confirmation of the standard Λ Cold Dark Matter cosmological model, which has been constrained with unprecedented accuracy. However, with the increase of the experimental sensitivity, a few statistically significant tensions between different independent cosmological datasets emerged. While these tensions can be in part the result of systematic errors, the persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the need for new physics. In this Letter of Interest we will focus on the 4.4σ tension between the Planck estimate of the Hubble constant H0 and the SH0ES collaboration measurements. After showing the H0 evaluations made from different teams using different methods and geometric calibrations, we will list a few interesting models of new physics that could solve this tension and discuss how the next decade’s experiments will be crucial.
Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.
Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.
A detailed electron microprobe study has been conducted on natural and experimentally grown zoned plagioclase feldspars. Discontinuous, sector, and oscillatory chemical zoning are observed superimposed on continuous normal or reverse zoning trends. The relative accuracy of 3 element (Na, Ca, K) microprobe traverses was found statistically to be 2 mole percent. Comparison of microprobe data on natural zoned plagioclase with zoned plagioclase grown in controlled experiments has shown that it may be possible to distinguish zonal development resulting from physio-chemical changes to the bulk magma from zoning related to local kinetic control on the growth of individual crystals.
The photometric and positional accuracy of a microdensitometer is examined. A calibrating plate with different density areas available is used to determine photometric accuracy. Position accuracy is evaluated by multiple scans of sharp edges and analyzed statistically.
Eleven of the 15 stars on Serkowski's (1974) list of 'Standard Stars with Large Interstellar Polarization' were investigated to determine whether the orientation of the plane of their linear polarization showed any dependence on wavelength. Nine of the eleven stars exhibited a statistically significant wavelength dependence of position angle when measured with an accuracy of about 0.1 deg standard deviation. For the majority of these stars, the effect is caused primarily by intrinsic polarization. The calibration of polarimeter position angles in a celestial coordinate frame must evidently be done at the 0.1 deg level of accuracy by using only carefully selected standard stars or by using other astronomical or laboratory methods.
Numerical comparisons in decaying isotropic turbulence suggest that there exist discrepancies in time correlations evaluated by DNS and LES using eddy-viscosity-type SGS models. This is consistent with the previous observations in forced isotropic turbulence. Therefore, forcing is not the main cause of the discrepancies. Comparisons among different SGS models in the LES also indicate that the model choice affects the time correlations in the LES. The multi-scale LES method using the dynamic Smagorinsky model on the small scale equation is the most accurate of the all models, the classic Smagorinsky model is the least accurate and the dynamic Smagorinsky model and spectral eddy viscosity model give intermediate results with small differences. The generalized sweeping hypothesis implies that time correlations in decaying isotropic turbulence are mainly determined by the instantaneous energy spectra and sweeping velocities. The analysis based on the sweeping hypothesis explains the discrepancies in our numerical simulations: the LES overpredicts the decorrelation time scales because the sweeping velocities are smaller than the DNS values, and underpredicts the magnitudes of time correlations because the energy spectrum levels are lower than the DNS ones. Since the sweeping velocity is determined by the energy spectra, one concludes that an accurate prediction of the instantaneous energy spectra guarantees the accuracy of time correlations. An analytical expression of sound power spectra based on Lighthill's theory and the quasi-normal closure assumption suggests that the sound power spectra are sensitive to errors in time correlations. Small errors in time correlations can cause significant errors in the sound power spectra, which exhibit a sizable drop at moderate to high frequencies accompanied by a shift of the peaks to lower frequencies. Based on the above analysis, two possible ways to improve the acoustic power spectrum predictions can be considered. The first is to construct better SGS models to improve the LES accuracy for time correlations. The second is to remedy the temporal statistics of the Lighthill stress tensor in order to "recover" the contribution from the unresolved scales in LES to time correlations.
INTRODUCTION: Astronauts on future long duration space exploration will be required to execute complex tasks in which even minor errors could have devastating consequences. Intact cognition is critical to maintain exceptional performance standards and it is possible that variability even at the highest ends of the performance spectrum will impact operational tasks. Exposure to spaceflight hazards could compromise cognitive performance, and decrements have been documented under conditions of altered gravity and radiation exposure. The cognitive impacts of prolonged isolation and confinement remain relatively unknown. Traditional neuropsychological assessments cannot support earth independent monitoring, do not have normative comparisons for high functioning individuals, and most are not sensitive enough to detect small performance decrements. The Cognition Battery was developed to address these limitations but currently lacks comprehensive normative data. In this project, we aimed to 1) develop a preliminary normative database for astronauts and astronaut surrogates using the Cognition Battery, and 2) characterize differences in cognitive performance after short and long duration analog missions. PARTICIPANTS & METHODS: We assessed baseline cognitive performance by administering the Cognition Battery to 97 astronaut and astronaut surrogates recruited to approximate astronaut demographics (mean age: 39.38, SD=7.62; 35.1% female; 91.7% advanced degrees). For aim 1, we calculated speed and accuracy outcomes for each subtest and summarized the data with descriptive statistics. We examined the relationship between age and performance with Pearsons’s correlations and the relationship between gender and performance with independent samples t-tests. A subset of individuals on short duration (n=48; mean age: 38.18, SD=7.14; 37.5% female; 91.7% advanced degrees) missions of 45 days and long duration (n=34; mean age: 42.12, SD=8.39; 38.2% female; 94.1% advanced degrees) missions ranging from 4-8 months were administered the Cognition Battery during and after their respective missions. For aim 2, we accounted for practice effects using published corrections, and z-transformed post-mission scores using the full sample’s baseline scores. One-way analysis of covariance tests determined the main effect of analog duration on post-mission performance, after accounting for age and gender. The False Discovery Rate method was applied to adjust for multiple comparisons. RESULTS: In the full normative sample, older age was associated with slower reaction times on a processing speed task (r=.39, p=.02), and men were more accurate on processing speed (t=-2.19, p=.03) and faster on sustained attention (t=2.02, p=.049) and risk-taking tasks (t=2.49, p=.02). We observed a main effect of duration on performance on tasks of visual memory (f(1)=5.62, p=.04), processing speed (f(1)=5.48, p=.03), and sensorimotor functioning (f(1)=39.52, p < .001), such that slower performance was observed after long duration missions relative to short. Only sensorimotor functioning was significant after adjustments for multiple comparisons (adjusted p<.001). DISCUSSION: The full sample represents the largest dataset of cognitive performance assessed by the Cognition Battery available and can be used for further research in spaceflight and high-performance populations. Relationships between performance and key demographic variables suggest future research that include age and gender stratification is needed. Our results also show minimal changes in cognitive performance between longer and shorter periods of isolation and confinement.
Test procedures and results for accuracy and timing tests of the basic IBM 360/50 FORTRAN 4 /H/ subroutine library are reported. The testing was undertaken to verify performance capability and as a prelude to providing some replacement routines of improved performance.