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DOE OSTI2023

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R 2 = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. Further, the key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗

DOE OSTI2023

Multifunctional Fiber-Reinforced Polymer Composites for Damage Detection and Memory

Self-structural health monitoring (SHM) functionalities for fiber-reinforced polymer composites have become highly sought after to ensure the structural safety of newly advancing components in the automotive, civil, mechanical, and aerospace industries. This paper introduces a self-damage detection and memory (SDDM) hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive sensor network, and the structural glass fiber operates as electrical insulation. In this study, SDDM specimens were fabricated, and tensile and impact tests were performed. The tensile tests of SDDM specimens find two distinct loading peaks: first where the carbon fiber fails, and second where the glass fiber fails. A linear correlation was observed between the carbon fiber resistance and composite strain up to a threshold, beyond which a sharp nonlinear increase in resistance occurred. The resistance then approached infinity, coinciding with the first loading peak and failure of the carbon fiber elements. This demonstrates the potential for a damage early warning threshold. Additionally, the effect of stitching the sensor tow in a zig-zag pattern over a large area was investigated using tailored fiber placement (TFP) of 1-loop, 3-loop, and 5-loop specimens. Tensile testing found that increasing the number of loops improved the sensor’s accuracy for strain sensing. Furthermore, impact tests were conducted, and as the impact energy progressively increased, the sensor resistance permanently increased. This illustrates a capability for self-memory of microdamage throughout the life cycle of the structure, potentially useful for predicting the remaining life of the composite.

Demo, Luke B. (ORCID:0000000208806522)↗

NASA NTRS1980

The influence of wing, fuselage and tail design on rotational flow aerodynamics data obtained beyond maximum lift with general aviation configurations

The NASA Langley Research Center has initiated a broad general aviation stall/spin research program. A rotary balance system was developed to support this effort. Located in the Langley spin tunnel, this system makes it possible to identify an airplane's aerodynamic characteristics in a rotational flow environment, and thereby permits prediction of spins. This paper presents a brief description of the experimental set-up, testing technique, five model programs conducted to date, and an overview of the rotary balance results and their correlation with spin tunnel free-spinning model results. It is shown, for example, that there is a large, nonlinear dependency of the aerodynamic moments on rotational rate and that these moments are pronouncedly configuration-dependent. Fuselage shape, horizontal tail and, in some instances, wing location are shown to appreciably influence the yawing moment characteristics above an angle of attack of 45 deg.

Bihrle, W., Jr.↗

NASA NTRS1984

Assessment of preliminary prediction techniques for wing leading-edge vortex flows at supersonic speeds

The applicability of existing theoretical models for predicting wing leading edge separated flow characteristics for high-lift supersonic flight was examined with regard to previous experimental force, pressure and flow visualization data. Correlations of data on uncambered delta wings revealed that the upper surface normal force and minimum pressure coefficients decreased nonlinearly with increasing angles of attack. The attainable vacuum pressure decreased with increasing Mach number, while the lower surface normal force increased nonlinearly with increasing speed. The LISTAR and VORCAM linear theory aerodynamic codes generated predictions for comparison with the data. LISTAR displayed better agreement with measured vortex strength and position and lifting characteristics than did VORCAM. The Euler code SWINT was ill-suited to calculating wing performance in separated flows at high lift and low supersonic speeds.

Wood, R. M.↗

NASA NTRS1988

Large-scale structure from cosmic-string loops in a baryon-dominated universe

The results are presented of a numerical simulation of the formation of large-scale structure in a universe with Omega(0) = 0.2 and h = 0.5 dominated by baryons in which cosmic strings provide the initial density perturbations. The numerical model yields a power spectrum. Nonlinear evolution confirms that the model can account for 700 km/s bulk flows and a strong cluster-cluster correlation, but does rather poorly on smaller scales. There is no visual 'filamentary' structure, and the two-point correlation has too steep a logarithmic slope. The value of G mu = 4 x 10 to the -6th is significantly lower than previous estimates for the value of G mu in baryon-dominated cosmic string models.

Melott, Adrian L.↗

NASA NTRS1992

Extension and validation of an unsteady wake model for rotors

A new three-dimensional, finite-state induced-flow model is extended to treat nonlinearities associated with the mass flow induced through the rotor plane. This new theory is then applied to the correlation of a recent set of unsteady, hover laser Doppler velocimetry inflow measurements conducted in the Aeroelastic Rotor Test Chamber at Georgia Institute of Technology. Although the model is intended primarily as a representation of unsteady aerodynamics for aeroelasticity applications, the results show that it has an excellent capability in predicting the inflow distribution in hover except near the root and tip. In addition, the computed unsteady spanwise lift distribution of a rotor is compared with that from an unsteady vortex lattice method for pitch oscillations at various frequencies. The new model is shown to be capable of prediction of unsteady loads typical of aeroelastic response.

Su, AY↗

NASA NTRS1994

Fatigue crack growth in 2024-T3 aluminum under tensile and transverse shear stresses

The influence of transverse shear stresses on the fatigue crack growth rate in thin 2024-T3 aluminum alloy sheets is investigated experimentally. The tests are performed on double-edge cracked sheets in cyclic tensile and torsional loading. This loading generates crack tip stress intensity factors in the same ratio as the values computed for a crack lying along a lap joint in a pressurized aircraft fuselage. The relevant fracture mechanics of cracks in thin plates along with the details of the geometrically nonlinear finite element analyses used for the test specimen calibration are developed and discussed. Preliminary fatigue crack growth data correlated using the fully coupled stress intensity factor calibration are presented and compared with fatigue crack growth data from pure delta K(sub I)fatigue tests.

Viz, Mark J.↗

NASA NTRS1995

Evidence for Chemical Processing of Precometary Icy Grains In Circumstellar Environments of Pre-Main-Sequence Stars

We report the detection of a broad absorption feature near 2166 cm-1 in the spectrum of the Taurus cloud source Elias 18. This pre-main-sequence source is the second in Taurus, the third in our survey, and the fifth known in the sky to show the broad 2166 cm-1 absorption feature. Of equal importance, this feature is not seen toward several other embedded sources in our survey, nor is it seen toward the source Elias 16, located behind the Taurus cloud. Laboratory experiments with interstellar ice analogs show that such a feature is associated with a complex C=-N containing compound [called X(C=-N)] that results from high-energy processing (ultraviolet irradiation or ion bombardment) of simple ice components into more complex, organic components, We find a nonlinear anticorrelation between the abundance of X(C=-N) and frozen CO in non- polar lattices. We find no correlation between the abundance of X(C=-N) and frozen CO in polar lattices. Because the abundances of frozen CO and H20 are strongly correlated with each other and with visual extinction toward sources embedded in and located behind the Taurus molecular cloud, these ice components usually are associated with intracloud material. Our results indicate that X(C=-N) molecules result from chemical processing of dust grains dominated by nonpolar icy mantles in the local environments of pre-main- sequence stars. Such processing of icy grains in the early solar system may be an important source of organic compounds observed in minor solar system bodies. The delivery of these organic compounds to the surface of the primitive Earth through comet impacts may have provided the raw materials for prebiotic chemistry.

Teglier, Stephen C.↗

NASA NTRS2002

Buckling Testing and Analysis of Space Shuttle Solid Rocket Motor Cylinders

A series of full-scale buckling tests were performed on the space shuttle Reusable Solid Rocket Motor (RSRM) cylinders. The tests were performed to determine the buckling capability of the cylinders and to provide data for analytical comparison. A nonlinear ANSYS Finite Element Analysis (FEA) model was used to represent and evaluate the testing. Analytical results demonstrated excellent correlation to test results, predicting the failure load within 5%. The analytical value was on the conservative side, predicting a lower failure load than was applied to the test. The resulting study and analysis indicated the important parameters for FEA to accurately predict buckling failure. The resulting method was subsequently used to establish the pre-launch buckling capability of the space shuttle system.

Weidner, Thomas J.↗

DOE OSTI2026

Bayesian Exploration and Surrogate Emulation of Nonlinear Beam-Response Geometry in the LBNF Beamline

Next-generation long-baseline neutrino experiments aim to achieve multi-MW proton beam power while reducing accelerator-induced systematic uncertainties. At Fermilab, the LBNF beamline is designed for 1.2 MW operation with PIP-II and is upgradeable to 2.4 MW. DUNE will probe the three-flavor neutrino paradigm and search for CP violation, requiring precise neutrino-flux normalization and improved control of accelerator-related uncertainties. Within the LBNF beamline, the System for On-Axis Neutrino Detection (SAND) will constrain flux uncertainties using precision near-detector measurements, while the Muon Monitor System (MuMS) will provide beamline diagnostics sensitive to the proton beam, target, and horn configuration. However, the pion phase space relevant for DUNE depends simultaneously on many correlated parameters, including beam centroid, beam width, horn current and alignment, target position, optics shifts, and radiation-induced changes. Consequently, MuMS observables exhibit nonlinear and coupled responses that are difficult to characterize using traditional one-parameter scans. To address this challenge, we are developing a Bayesian Exploration framework coupled to physics-informed surrogate emulators trained on Geant4 beamline simulations. Gaussian-process emulators provide both fast predictions and uncertainty estimates, enabling adaptive selection of new simulation points in beam-parameter space. As an initial demonstration, we construct surrogate emulators for MuMS response observables using a verified simulation campaign spanning proton-beam steering conditions. The emulators reproduce the simulated dependence of MuMS centroid and gradient observables while providing predictive uncertainties, and serve as the foundation for future multidimensional exploration including beam width, horn current, and additional beamline parameters. This work establishes a framework for uncertainty-aware beam monitoring, adaptive simulation campaigns, and rapid beam-response inference for future DUNE operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

DOE OSTI2020

Perturbation theory for modeling galaxy bias: validation with simulations of the Dark Energy Survey

We describe perturbation theory (PT) models of galaxy bias for applications to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter correlation functions in configuration space and validate against measurements from mock catalogs designed for the Dark Energy Survey (DES). We find that an effective PT model with five galaxy bias parameters provides a good description of the 3D correlation functions above scales of 4 Mpc/$h$ and $z < 1$. Our tests show that at the projected precision of the DES-Year 3 analysis, two of the non-linear bias parameters can be fixed to their co-evolution values, and a third (the $k^2$ term for higher derivative bias) set to zero. The agreement is typically at the 2 percent level over scales of interest, which is the statistical uncertainty of our simulation measurements. To achieve this level of agreement, our {\it fiducial} model requires using the full non-linear matter power spectrum (rather than the 1-loop PT one). We also measure the relationship between the non-linear and linear bias parameters and compare them to their expected co-evolution values. We use these tests to motivate the galaxy bias model and scale cuts for the cosmological analysis of the Dark Energy Survey; our conclusions are generally applicable to all photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2018

Relationships Between the Planetary Boundary Layer Height and Surface Pollutants Derived from Lidar Observations over China: Regional Pattern and Influencing Factors

The frequent occurrence of severe air pollution episodes in China has been a great concern and thus the focus of intensive studies. Planetary boundary layer height (PBLH) is a key factor in the vertical mixing and dilution of near-surface pollutants. However, the relationship between PBLH and surface pollutants, especially particulate matter (PM) concentration across China, is not yet well understood. We investigate this issue at 1600 surface stations using PBLH derived from space-borne and ground-based lidar, and discuss the influence of topography and meteorological variables on the PBLH–PM relationship. Albeit the PBLH–PM correlations are roughly negative for most cases, their magnitude, significance, and even sign vary considerably with location, season, and meteorological conditions. Weak or even uncorrelated PBLH–PM relationships are found over clean regions (e.g., Pearl River Delta), whereas nonlinearly negative responses of PM to PBLH evolution are found over polluted regions (e.g., North China Plain). Relatively strong PBLH–PM interactions are found when the PBLH is shallow and PM concentration is high, which typically corresponds to wintertime cases. Correlations are much weaker over the highlands than the plains regions, which may be associated with lighter pollution loading at higher elevations and contributions from mountain breezes. The influence of horizontal transport on surface PM is considered as well, manifested as a negative correlation between surface PM and wind speed over the whole nation. Strong wind with clean upwind air plays a dominant role in removing pollutants, and leads to obscure PBLH–PM relationships. A ventilation rate is used to jointly consider horizontal and vertical dispersion, which has the largest impact on surface pollutant accumulation over the North China Plain. As such, this study contributes to improved understanding of aerosol–planetary boundary layer (PBL) interactions and thus our ability to forecast surface air pollution.

Su, Tianning↗

DOE OSTI2021

Nonlinear Architectures Can Alter the Dynamics of Polymer–Nanoparticle Composites

Polymer nanocomposites exhibit remarkable physical properties that are attractive for many applications. These systems have been so far investigated by using linear polymer chains; the role of polymer matrix architecture on local dynamics, bulk rheology and nanoparticle motion remains unexplored. Here, using quasielastic neutron scattering, bulk rheology and x-ray photon correlation spectroscopy, we investigated nanocomposites with spherical silica nanoparticles well dispersed in poly (ethylene oxide) matrices having different architectures (specifically linear, stars and hyperbranched). The results reveal that the mechanical reinforcement of the nanocomposites with the nonlinear polymers can be altered by orders of magnitude with respect to the conventional nanocomposite with the linear polymer. Additionally, the polymer compactness and inter-penetrability are found to play crucial roles in determining their bulk rheology. At microscopic level, average segmental dynamics is remarkably slowed down by the attractive NPs in the matrices of high degree of branching whereas no significant effect is observed in linear polymer matrix at the same NP loading. In addition, the nanoscale dynamics of particles in the compact nonlinear matrices exhibit strong decoupling from the bulk viscoelasticity, allowing their fast relaxation even at ≈ 30% by volume. These results provide an experimental evidence that macromolecular architecture is a powerful new tool for tuning the bulk rheological properties as well as the nanoscale dynamics of PNCs without the need for changing polymer molecular weight, nanoparticle size, shape, loading or dispersion state.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Correlation between the temperature evolution of the interfacial region and the growing dynamic cooperativity length scale

Here, we study experimentally the temperature evolution of the thickness of the interfacial layer, L int (T), between bulk matrices and the surface of nanoparticles in nanocomposites through broadband dielectric spectroscopy. Analyses revealed a power-law dependence between the logarithm of structural relaxation time in the interfacial layer, τ int (T), and the L int (T): ln τ i n t ( T ) / τ 0 ∝ L i n t β ( T ) / T , with τ 0 ~ 10 –12 s, and β index ~0.67 at high temperatures and ~1.7 at temperatures close to the glass transition temperature. In addition, our analysis revealed that the L int (T) is comparable to the length scale of dynamic heterogeneity estimated from previous nonlinear dielectric measurements and the four-point NMR [ξ NMR (T)], with L int (T) ~ ξ NMR (T). These observations may suggest a direct correlation between the L int (T) and the size of the cooperatively rearranging regions and have strong implications for understanding the dynamic heterogeneity and cooperativity in supercool liquids and their role in interfacial dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Preserving Tracer Correlations in Moment-Based Atmospheric Transport Models

A linear non-diffusive algorithm for advective transport is developed that greatly improves the detail at which aerosols and clouds can be represented in atmospheric models. Linear advection schemes preserve tracer correlations but the most basic linear scheme is rarely used by atmospheric modelers on account of its excessive numerical diffusion. Higher-order schemes are in widespread use, but these present new problems as nonlinear adjustments are required to avoid occurrences of negative concentrations, spurious oscillations, and other non-physical effects. Generally successful at reducing numerical diffusion during the advection of individual tracers, for example, particle number or mass, the higher-order schemes fail to preserve even the simplest of correlations between interrelated tracers. As a result, important attributes of aerosol and cloud populations including radial moments of particle size distributions, molecular precursors related through chemical equilibria, aerosol mixing state, and distribution of cloud phase are poorly represented. We introduce a new transport scheme, minVAR, that is both non-diffusive and preservative of tracer correlations, thereby combining the best features of the basic and higher-order schemes while enabling new features such as the tracking of sub-grid information at arbitrarily fine scales with high computational efficiency.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

An experimental study on round-trip efficiency of a preheating control with a medium office building

The interactions between building energy efficiency and demand response (DR) with buildings may complicate their impacts on the electricity grid and buildings, yet those interactions are not well understood. This work fills this gap by presenting an experimental study that quantifies the effects of DR on building energy efficiency. Unlike existing studies, this work 1) investigates the uncertainty levels when assessing the effects of DR; 2) studies the impact of rooftop heat pumps with direct expansion refrigerant-to-air coils on the round-trip efficiency. Specifically, we considered a medium office building (~1860 m 2 ), which is served by rooftop units, located in eastern Washington State. We performed preheating control tests during typical heating days in the year 2022 and compared the building’s energy consumption during the testing days to that during normal operation days under similar weather conditions. To understand how well those normal operation days represent the baseline for the assessment, the relationship between the energy performance of this building and weather conditions was also studied. Experimental results reveal that the round-trip efficiency (i.e., the ratio of discharging energy output from the building to charging energy input into the building) was between 0.16 and 0.92. The results show that significant energy loss may occur during the preheating process. Finally, it also suggests a significant correlation between the energy efficiency of the studied building under the preheating control and the outdoor temperature; and a highly nonlinear relationship between the zone temperature changing rate and the weather condition.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Unveiling the Significance of Correlations in K-Space and Configuration Space for Drift Wave Turbulence in Tokamaks

Turbulence and transport phenomena play a crucial role in the confinement and stability of tokamak plasmas. Turbulent fluctuations in certain physical quantities, such as density or temperature fluctuations, can have a wide range of spatial scales, and understanding their correlation length is important for predicting and controlling the behavior of the plasma. The correlation length in the radial direction is identified as the critical length in real space. The dynamics in real space are of significant interest because transport in configuration space is primarily focused on them. When investigating transport caused by the E×B drift, the correlation length in real space represents the size of E×B whirls. It was numerically discovered that in drift wave turbulence, this length is inversely proportional to the normalized mode number of the fastest growing mode relative to the drift frequency. Considerable time was required before a proper analytical derivation of this condition was accomplished. Therefore, a connection has been established between phenomena occurring in real space and those occurring in k-space. Although accompanied by a turbulent spectrum in k-space with a substantial width, transport in real space is uniquely determined by the correlation length, allowing for accurate transport calculations through the dynamics of a single mode. Naturally, the dynamics are subject to nonlinear effects, with resonance broadening in frequency being the most significant nonlinear effect. Thus, mode number space is once again involved. Resonance broadening leads to the detuning of waves from particles, permitting a fluid treatment. It should be emphasized that the consideration here involves the total electric field, including the induction part, which becomes particularly important at higher beta plasmas.

Weiland, Jan (ORCID:0000000253496025)↗