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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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Advances in Integrally Formed Metallic Structures

NASA Langley Research Center (LaRC) is applying flow forming technology to aerospace aluminum alloys for the manufacture of integrally stiffened cylinders that meet aircraft fuselage or launch vehicle specifications. High-strength 2xxx series alloys, in various temper/product form conditions, are being compared with medium-strength, very formable AA 6061 as the benchmark. Screening of candidate materials traditionally relies on empirical forming trials, but more recently on metrics derived from constant nominal strain rate tensile testing. In this work, the established hierarchy of formability metrics is adopted as the baseline; tensile area reduction, work hardening exponent, and modulus of resilience. The suite of metrics can be expanded based on phenomenological and mechanistic considerations of plastic flow instabilities to incorporate anisotropy ratio, rate of work hardening, and dynamic strain aging effects. Separate research thrusts at LaRC are aimed at formulating a protocol for scalable production of fully formed, defect-free components: 1) quantifying the extended set of formability metrics from constant and jump strain rate tensile tests with digital image correlation (DIC); 2) using statistically designed experiments on a vertical spin/flow forming facility (WF VUD-600®) to isolate the effect of convenient process variables, such as axial feed rate and thickness reduction; and 3) predicting the effect of feed ratio (roller translation relative to mandrel rotation speeds) on material response via process modeling using the DEFORM® simulation software. Current results will be discussed in terms of correlating intrinsic material properties with the bulk workability characteristics to successfully fabricate cylinders with complex cross-sections.

Aluminum↗
NASA NTRS1991

Hexagonal and nematic phases of chains. I - Correlation functions. II - Phase transitions

The statistical mechanics of a system of semiflexible chains, which can represent polymer liquid crystals, long-chain biomolecules, stiff wormlike micelles, or columns of discotic liquid crystals, are examined. A continuum theory is used to calculate static correlation functions in the hexagonal and nematic phases. Two correlation functions are considered: (1) the structure factor which describes fluctuations in the density; and (2) the director fluctuation spectrum, which describes fluctuations in the local optical axis. In addition, a model is developed for the phase transitions of a system of infinitely long, semiflexible chains which interact through a steric, excluded-volume repulsion. The model yields generic phase diagrams in terms of pressure or density vs. persistence length or temperature.

Selinger, Jonathan V.↗
NASA NTRS1988

Linear Least Squares for Correlated Data

Throughout the literature authors have consistently discussed the suspicion that regression results were less than satisfactory when the independent variables were correlated. Camm, Gulledge, and Womer, and Womer and Marcotte provide excellent applied examples of these concerns. Many authors have obtained partial solutions for this problem as discussed by Womer and Marcotte and Wonnacott and Wonnacott, which result in generalized least squares algorithms to solve restrictive cases. This paper presents a simple but relatively general multivariate method for obtaining linear least squares coefficients which are free of the statistical distortion created by correlated independent variables.

Dean, Edwin B.↗
NASA NTRS2010

Roles of Engineering Correlations in Hypersonic Entry Boundary Layer Transition Prediction

Efforts to design and operate hypersonic entry vehicles are constrained by many considerations that involve all aspects of an entry vehicle system. One of the more significant physical phenomenon that affect entry trajectory and thermal protection system design is the occurrence of boundary layer transition from a laminar to turbulent state. During the Space Shuttle Return To Flight activity following the loss of Columbia and her crew of seven, NASA's entry aerothermodynamics community implemented an engineering correlation based framework for the prediction of boundary layer transition on the Orbiter. The methodology for this implementation relies upon similar correlation techniques that have been is use for several decades. What makes the Orbiter boundary layer transition correlation implementation unique is that a statistically significant data set was acquired in multiple ground test facilities, flight data exists to assist in establishing a better correlation and the framework was founded upon state of the art chemical nonequilibrium Navier Stokes flow field simulations. Recent entry flight testing performed with the Orbiter Discovery now provides a means to validate this engineering correlation approach to higher confidence. These results only serve to reinforce the essential role that engineering correlations currently exercise in the design and operation of entry vehicles. The framework of information related to the Orbiter empirical boundary layer transition prediction capability will be utilized to establish a fresh perspective on this role, and to discuss the characteristics which are desirable in a next generation advancement. The details of the paper will review the experimental facilities and techniques that were utilized to perform the implementation of the Orbiter RTF BLT Vsn 2 prediction capability. Statistically significant results for multiple engineering correlations from a ground testing campaign will be reviewed in order to describe why only certain correlations were selected for complete implementation to support the Shuttle Program. Historical Orbiter flight data on early boundary layer transition due to protruding gap fillers will be described in relation to the selected empirical correlations. In addition, Orbiter entry flight testing results from the BLT Flight Experiment will be discussed in relation to these correlations. Applicability of such correlations to the entry design problem will be reviewed, and finally a perspective on the desirable characteristics for a next generation capability based on high fidelity physical models will be provided.

Campbell, Charles H.↗
NASA NTRS1990

Potential vorticity index

Using standard data analysis techniques, researchers explore the links between disturbance growth and quasi-geostrophic potential vorticity (PV) gradients; appearance and disappearance of cutoff lows and blocking highs and their relation to a zonal index (properly defined in terms of PV); and teleconnections between different flow patterns and their relation to the zonal index. It was found that the PV index and the eddy index correlate better than a zonal index (defined by zonal wind) and the eddy index. In the frequency domain there are three frequencies (.03, .07 and .17 cpd (cycle per day) corresponding to periods of 33, 14 and 6 days) at which PV index and the eddy index exhibit local maxima. The high correlation found at periods of 33 days is mainly due to eddy activity at high latitudes while the local correlation maxima found at the shorter periods are mainly due mid-latitude eddy activity. The correlation between the PV index and the geopotential height anomaly at 500 mb, at each grid point in the Northern Hemisphere, shows the existence of most of the teleconnection patterns summarized by Wallace and Gutzler (1981): the North Atlantic Oscillation, the North Pacific Oscillation, and the Pacific/North American patterns. Results show that the Isentropic Potential Vorticity (IPV) analysis can be a very useful and powerful tool when used to understand the dynamics of several large scale atmospheric systems. Although the data are limited to only one winter, and it is difficult to assess the statistical significance of the correlation coefficients presented here, the results are encouraging from physical viewpoint.

Barcilon, Albert↗
NASA NTRS2004

Forecasting Coronal Mass Ejections from Magnetograms

We report further results from our ongoing assessment of magnetogram-based measures of active-region nonpotentiality (magnetic shear and twist) and size as predictors of coronal mass ejections (CMEs). From a set of 36 vector magnetograms of bipolar active regions (Falconer, Moore, & Gary 2004, ApJ, submitted), we have found that (1) each of our 5 measures of active-region nonpotentiality has a 75-80% success rate in predicting whether an active region will produce a CME within 2 days after the magnetogram, (2) the correlation of each of these measures with CME production in this window is statistically significant (confidence level greater than 95%), (3) our measure of active-region size has a 65% success rate in predicting CMEs in this window, but (4) the correlation between active-region size and CME productivity is not statistically significant (confidence level approximately 80%). As part of the work under our pending 2003 LWS TR&T proposal, we will double our sample to approximately 70 active regions in order to demonstrate a statistically significant correlation between active-region size and CME productivity, and to determine whether this correlation is a byproduct of any Sun-produced correlation between magnetic twist and size of active regions. Since the 2002 LWS Science Workshop, we have found a measure of active-region nonpotentiality (the length of strong-gradient neutral line) that can be measured from a line-of-sight magnetogram, and we have generalized this measure and the corresponding measure from a vector magnetogram (the length of strong-shear neutral line) so that they can be applied to multiple-bipole complex active regions as well as to bipolar active regions. Preliminary results will be presented from application of these two generalized measures to our sample of bipolar active regions and to a new sample of multiple-bipole active regions.

Falconer, D. A.↗
NASA NTRS2000

The Correlation Between Atmospheric Dust Deposition to the Surface Ocean and SeaWiFS Ocean Color: A Global Satellite-Based Analysis

Since the atmospheric deposition of iron has been linked to primary productivity in various oceanic regions, we have conducted an objective study of the correlation of dust deposition and satellite remotely sensed surface ocean chlorophyll concentrations. We present a global analysis of the correlation between atmospheric dust deposition derived from a satellite-based 3-D atmospheric transport model and SeaWiFs estimates of ocean color. We use the monthly mean dust deposition fields of Ginoux et al. which are based on a global model of dust generation and transport. This model is driven by atmospheric circulation from the Data Assimilation Office (DAO) for the period 1995-1998. This global dust model is constrained by several satellite estimates of standard circulation characteristics. We then perform an analysis of the correlation between the dust deposition and the 1998 SeaWIFS ocean color data for each 2.0 deg x 2.5 deg lat/long grid point, for each month of the year. The results are surprisingly robust. The region between 40 S and 60 S has correlation coefficients from 0.6 to 0.95, statistically significant at the 0.05 level. There are swaths of high correlation at the edges of some major ocean current systems. We interpret these correlations as reflecting areas that have shear related turbulence bringing nitrogen and phosphorus from depth into the surface ocean, and the atmospheric supply of iron provides the limiting nutrient and the correlation between iron deposition and surface ocean chlorophyll is high. There is a region in the western North Pacific with high correlation, reflecting the input of Asian dust to that region. The southern hemisphere has an average correlation coefficient of 0.72 compared that in the northern hemisphere of 0.42 consistent with present conceptual models of where atmospheric iron deposition may play a role in surface ocean biogeochemical cycles. The spatial structure of the correlation fields will be discussed within the context of guiding the design of field programs.

Erickson, D. J., III↗
NASA NTRS1989

Cosmological velocity correlations - Observations and model predictions

By applying the present simple statistics for two-point cosmological peculiar velocity-correlation measurements to the actual data sets of the Local Supercluster spiral galaxy of Aaronson et al. (1982) and the elliptical galaxy sample of Burstein et al. (1987), as well as to the velocity field predicted by the distribution of IRAS galaxies, a coherence length of 1100-1600 km/sec is obtained. Coherence length is defined as that separation at which the correlations drop to half their zero-lag value. These results are compared with predictions from two models of large-scale structure formation: that of cold dark matter and that of baryon isocurvature proposed by Peebles (1980). N-body simulations of these models are performed to check the linear theory predictions and measure sampling fluctuations.

Gorski, Krzysztof M.↗
DOE OSTI2026

Anticipating decoherence in quantum systems

Large-scale quantum technologies require coherence across distant nodes, necessitating indistinguishable quantum states. However, environmental disorder, including dephasing, spectral diffusion, and spin-bath interactions, undermines coherence. Using statistical methods, we uncover correlations in decoherence channels induced by slowly varying environments. Spectral diffusion serves as a representative demonstration case that can be extended to other remote, disordered systems such as spins in nitrogen-vacancy centers and quantum-dot spin qubits, as well as flux noise in superconducting qubits. In this work, we employ replica-theory-inspired trajectory analysis to reveal predictable temporal structures in decoherence dynamics, and validate these through an anticipatory systems framework with internal prediction of unseen spectral dynamics in multiple quantum systems, showing that this framework could, if implemented, reduce spectral shift by average factors of approximately 2 to 19, depending on emitter stability, thereby enabling enhanced coherence and multi-node synchronization for scalable quantum communication, computation, imaging, and sensing.

Maan, Pranshu [Purdue University]↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
DOE OSTI2024

A Hybrid Anomaly Detection Approach for Obfuscated Malware

With the rapid evolution of malicious software, cyber threats have become increasingly sophisticated, employing advanced obfuscation techniques to evade traditional detection methods. This study presents a hybrid anomaly detection approach applied to obfuscated malware. Even though there is a large body of research in this field, existing malware detection techniques have some drawbacks, such as requiring large amounts of data, trustworthiness (imprecise results) of algorithms, and advanced obfuscation. To overcome these challenges, there is a need to employ solid and efficient techniques for malware detection. This paper proposes a hybrid approach, combining an autoencoder with traditional machine-learning methods to create an efficient malware detection framework. We used the malware memory dataset (MalMemAnalysis-2022) to evaluate this framework. The results indicate that our proposed approach can detect obfuscated malware when a deep autoencoder used for feature learning is combined with logistic regression, and it is extremely fast with an Accuracy, Detection Rate (DR), Matthew Correlation Coefficient(MCC), and Statistical Parity Difference

malware detection, Hybrid Anomly Detection, Obfusc↗
DOE OSTI2024

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study

Background The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. Methods This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t -tests, Pearson correlation analyses, and mediation effect analysis. Results The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058–0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185–0.327). Conclusion The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Chai, Yulin↗
DOE OSTI2024

Test Vector Development for Verification and Validation of Heavy-Duty Autonomous Vehicle Operations

The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation. Continuing this prior work, this paper focuses on the generation of test cases based on the critical scenarios identified that can be used to prioritize either the most common nominal driving scenarios or the least common severe driving scenarios. These test cases can then be used validate, through simulation or real-world testing, the operating design domain (ODD) for a generalized driving mission and built upon to identify spatial and temporal impacts on the driving mission of an autonomous vehicle.

Siekmann, Adam↗
NASA NTRS1973

A note on signal enhancement for dual magnetometer systems.

Demonstration of the value of cross-correlation signal enhancement achieved by means of a dual magnetometer system. A unique feature of a dual magnetometer system is, as pointed out by Ness et al. (1971), the opportunity it allows for signal enhancement by using cross-correlation techniques developed in statistical communication theory. Their use is shown to make it possible to overcome the limitations that the intrinsic sensor ambient noise and the measurement-contaminating spacecraft field fluctuations impose upon the signal detection performance of a single magnetometer.

Ness, N. F.↗
NASA NTRS1976

Wind power prediction models

Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

Levy, R.↗
NASA NTRS1979

Development of LACIE CCEA-1 weather/wheat yield models

The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.

Strommen, N. D.↗