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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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RSP Data Status, Updates and Plans from the Team

•All radiance (L1C), aerosol and cloud (L2) products are in the archive. •Revised calibration performed at GSFC in July 2021 applied to reprocessing. •Clouds have smaller drops and higher optical depths (larger droplet number concentrations) in winter than in summer. Cloud top droplet size distributions tend to be narrow with roughly 80% having an effective variance of less than 0.07. This is relevant to how well bulk auto-conversion parameterizations are likely to work at cloud top (order of magnitude effects) and also to assumptions used in the remote sensing of droplet number (~ 10% effect). Real refractive index and effective radius retrievals are consistent with hygroscopic growth of aerosols Real refractive index and effective variance retrievals imply the presence of an insoluble fraction of aerosols Future work on clouds will look at differences in cloud top drizzle formation and auto-conversion rates between the different campaigns and at how well mixed phase clouds can be detected (if at all) from sensors such as VIIRS and PACE OCI.

Research Scanning Polarimeter↗

DOE OSTI2020

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗

NASA NTRS1982

Multivariable root loci on the real axis

Some methods for determining the number of branches of multivariable root loci which are located on the real axis at a given point are obtained by using frequency domain methods. An equation for the number of branches is given for the general case, and simpler results for the special cases when the transfer function G(s) has size 2 x 2, and when G(s) is symmetric, are also presented.

Yagle, A. E.↗

DOE OSTI2023

Impact Analysis of Future Electric Vehicles Using Model of Real Distribution Feeders

Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles and complete this case study.

Xie, Jing↗

NASA NTRS1961

Laminar Boundary Layer Behind a Strong Shock Moving into Air

The laminar wall boundary layer behind a strong shock advancing into stationary air has been determined. Numerical results have been obtained for shock Mach numbers up to 14 using real gas values for density and viscosity and assuming Prandtl and Lewis numbers of 0.72 and 1, respectively. The numerical results for shear and heat transfer agree, within 4 percent, with a previously presented approximate analytical expression for these quantities. A slight modification of this expression results in agreement with the numerical data to within 2.5 percent. Analytical expressions for boundary-layer thickness and displacement thickness, correct to within 4 percent for the present data, have also been obtained.

Mirels, Harold↗

DOE OSTI2022

Neutron Stars with Baryon Number Violation, Probing Dark Sectors

The neutron lifetime anomaly has been used to motivate the introduction of new physics with hidden-sector particles coupled to baryon number, and on which neutron stars provide powerful constraints. Although the neutron lifetime anomaly may eventually prove to be of mundane origin, we use it as motivation for a broader review of the ways that baryon number violation, be it real or apparent, and dark sectors can intertwine and how neutron star observables, both present and future, can constrain them.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1988

Range data description based on multiple characteristics

An algorithm for describing range images based on Mean curvature (H) and Gaussian curvature (K) is presented. Range images are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The curvature parameters are derived from the fundamental theorems of differential geometry and provides visible invariant pixel labels that can be used to characterize the scene. The sign of H and K can be used to classify each pixel into one of eight possible surface types. Due to the sensitivity of these parameters to noise the resulting HK-sing map does not directly identify surfaces in the range images and must be further processed. A region growing algorithm based on modeling the scene points with a Markov Random Field (MRF) of variable neighborhood size and edge models is suggested. This approach allows the integration of information from multiple characteristics in an efficient way. The performance of the proposed algorithm on a number of synthetic and real range images is discussed.

Al-Hujazi, Ezzet↗

DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗

DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Thermodynamics and magnetism in the two-dimensional to three-dimensional crossover of the Hubbard model

The realization of antiferromagnetic (AF) correlations in ultracold fermionic atoms on an optical lattice is a significant achievement. Experiments have been carried out in one, two, and three dimensions, and have also studied anisotropic configurations with stronger tunneling in some lattice directions. Such anisotropy is relevant to the physics of cuprate superconductors and other strongly correlated materials. Moreover, this anisotropy might be harnessed to enhance AF order. Here we numerically investigate, using the determinant quantum Monte Carlo method, a simple realization of anisotropy in the three-dimensional (3D) Hubbard model in which the tunneling between planes, $t_⊥$, is unequal to the intraplane tunneling $\textit{t}$. This model interpolates between the three-dimensional isotropic ($t_⊥ = t$) and two-dimensional (2D;$t_⊥$ = 0) systems. In this work, we show that at fixed interaction strength to tunneling ratio ($\textit{U/t}$), anisotropy can enhance the magnetic structure factor relative to both 2D and 3D results. However, this enhancement occurs at interaction strengths below those for which the Néel temperature $T_{\text{Néel}}$ is largest, in such a way that the structure factor cannot be made to exceed its value in isotropic 3D systems at the optimal $\textit{U/t}$. We characterize the 2D-3D crossover in terms of the magnetic structure factor, real space spin correlations, number of doubly occupied sites, and thermodynamic observables. An interesting implication of our results stems from the entropy's dependence on anisotropy. As the system evolves from 3D to 2D, the entropy at a fixed temperature increases. Correspondingly, at fixed entropy, the temperature will decrease going from 3D to 2D. This suggests a cooling protocol in which the dimensionality is adiabatically changed from 3D to 2D.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS1978

Ball bearing heat analysis program (BABHAP)

The Ball Bearing Heat Analysis Program (BABHAP) is an attempt to assemble a series of equations, some of which are non-linear algebraic systems, in a logical order, which when solved, provide a complex analysis of load distribution among the balls, ball velocities, heat generation resulting from friction, applied load, and ball spinning, minimum lubricant film thickness, and many additional characteristics of ball bearing systems. Although initial design requirements for BABHAP were dictated by the core limitations of the PDP 11/45 computer, (approximately 8K of real words with limited number of instructions) the program dimensions can easily be expanded for large core computers such as the UNIVAC 1108. The PDP version of BABHAP is also operational on the UNIVAC system with the exception that the PDP uses 029 punch and the UNIVAC uses 026. A conversion program was written to allow transfer between machines.

Source record↗

NASA NTRS1982

Experimental evaluation of the concept of supevisory manipulation

A computer-controlled teleoperator system which is based on task-referenced sensor-aided control has been developed to study supervisory manipulation. This system, called SUPERMAN, is capable of performing complicated tasks in real-time by utilizing the operator for high-level functions related to the unpredictable portions of a task, while the subordinate machine performs the more well-defined subtasks under human supervison. To determine whether supervisory control schemes such as these offer any advantage over manual control under real-time conditions, a number of experiments involving both simple and complicated tasks were performed. Six representative tasks were chosen for the study: (1) obtaining a tool from a rack, (2) returning the tool to the rack, (3) removing a nut, (4) placing samples in a storage bin, (5) opening and closing a valve, and (6) digging with a shovel. The experiments were performed under simulated conditions using four forms of manual control (i.e., switch rate, joystick rate, master-slave position control, and master-slave with force feedback), as well as supervisory control. Through these experiments the effectiveness and quality of control were evaluated on the basis of the time required to complete each portion of the task and the type and number of errors which occurred.

Brooks, T. L.↗

NASA NTRS1992

Transmission zeros of non-collocated flexible structures - Finite-dimensional effects

This paper studies the properties of the transmission zeros of flexible structures with non-collocated sensors and actuators. These properties are shown to be considerably more complicated than for the case of collocated structures. In particular, a finite-dimensional non-collocated structure can have zeros which are general complex numbers, rather than purely real or imaginary quantities. Furthermore, the convergence of these zeros to the true values as the order of the model is increased is typically quite complicated.

Williams, Trevor↗

DOE OSTI2023

The Tiny Triplet Finder as a Versatile Track Segment Seeding Engine for Trigger Systems

In high energy physics experiment trigger systems, track segment seeding is a resource consuming function and the primary reason is the computing complexity of the segment finding process. As the Moore's Law is reaching its physical limit, reducing computing complexity should be carefully considered, rather than keep piling up silicon resources. The Tiny Triplet Finder is a scheme that reduces the computing complexity of the segment seeding. As a proof of concept, a 3D track segment seeding engine core based on the Tiny Triplet Finder has been implemented and tested in a low-cost FPGA device. The seeding engine is designed to preselect and group hits (stubs) from detector layers to feed subsequent track fitting stage. The seeding engine consists of a Hough transform space for r-z view and a Tiny Triplet Finder for r-phi view to implement 3D constraints. The seeding engine is organized as a pipeline so that each hit is processed in a single clock cycle. Taking advantage of the register-like storage block scheme which enables effectively resetting of a block RAM within a single clock cycle, clearing or refreshing the seeding engine takes only one clock cycles between two events. The Tiny Triplet Finder is also a generic coincidence finding scheme that can be used for many tasks. As a versatility demonstration, track segment finding performances for two distinctive detector geometries are tested in our seeding engine. In a collider barrel-layer geometry, the fake segment rates are studied for 3D (i.e., both r-phi and r-z views) and 2D (i.e., r-phi or r-z view only) configurations for high hit multiplicity events (>4000 hits/layer in the barrel region). Another detector geometry contains strip plane layers with timing information. The numbers of coincidences, both real or fake, with or without timing ("3D" or "2D") information at various hit multiplicities are studied.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

We report surrogate mixtures are routinely used for understanding gasoline fuel combustion in engine simulations. The general trend in surrogate formulation has been to increase the number of fuel components in a mixture to better emulate real fuel properties. Recently, a new surrogate design strategy based on functional group analysis of real gasolines was proposed using a minimal number of species [minimalist functional group (MFG)-approach]. MFG surrogates (having just one or two components) could experimentally capture the ignition delay time (IDT), threshold sooting index, and smoke point of different gasoline fuels with hundreds of components. However, other combustion characteristics were not explored, and kinetic modeling of MFG surrogates was not reported. These aspects are addressed in this paper, where the combustion behavior of MFG surrogates for various gasolines was assessed by simulating IDT, jet-stirred reactor oxidation, and premixed laminar flame speeds using chemical kinetic modeling. MFG simulations were compared with experimental data of the real gasolines as well as with the more complex multicomponent (five to nine species) surrogates. This study reveals that binary MFG surrogate mixtures are capable of accurately simulating the combustion behavior of more complex gasoline fuels with hundreds of components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1994

Recovering the real density field of galaxies from redshift space

This paper proposes a number of methods for recovering the true, real-space large-scale density distributions from redshift data sets. On the largest scales, the effects of peculiar velocities is to compress the galaxy clustering along the line of sight and consequently the density gradient in the radial direction is enhanced in comparison with the density gradient perpendicular to the line of sight. We calculate the relation between the density gradients in real and redshift space in the linear regime and show that it is a simple function of the density parameter, Omega. By considering the anisotropy of density gradients in redshift space it is possible, in principle, to obtain information about the value of Omega. Using N-body simulations we examine different methods for correcting the effects of large-scale velocities to obtain the true density distributions. We derive the peculiar velocity field from the redshift data and iterate to place each galaxy at a distance consistent with its measured redshift and its derived velocity. Linear theory, as well as second-order solutions for the peculiar velocity field are tested. The density distributions in low-density regions predicted by both linear theory and second-order theory are similar, and in good agreement with the true density field in the simulation. The use of the second-order corrections improves the prediction of the density field in high-density regions.

Gramann, Mirt↗