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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dynamic analysis of fully constrained Cable-Driven Parallel Robots for automated prefabricated component installation

This paper presents a dynamic analysis and validation framework to assess a fully constrained six-anchor Cable-Driven Parallel Robot (CDPR) for automated installation of prefabricated facade components. Compared with conventional eight-anchor systems, the six-anchor configuration simplifies setup and reduces cost, but it also reduces control authority, shrinks the wrench-feasible workspace, and tightens orientation limits. Consequently, it is unclear a priori whether dynamically feasible trajectories exist to move the end effector from pickup to the facade. A constrained trajectory optimization is formulated to enforce the system dynamics, cable-tension bounds, and pose/velocity limits, and the framework is evaluated in simulation at three levels: (i) an idealized reference model, (ii) a lab-scale prototype model incorporating measured anchor misalignments and identified damping, and (iii) a full-scale three-story building model with load decomposition for structural feasibility checks. Across these scenarios, the analysis shows that optimal, constraint-satisfying trajectories exist that move the end effector from pickup to installation while maintaining a near-plumb, level orientation at the final pose. Collectively, this multi-scale dynamic analysis and validation framework supports the deployment readiness of the six-anchor CDPR and provides a prototype-based sensitivity case study of how measured anchor placement deviations affect feasibility.

CDPR↗
DOE OSTI2025

Nearby Sites Show Similar Upwind Sources and Differing Semivolatile Concentrations in Coastal Aerosol Particles

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) characterized aerosol composition using measurements at two sites within 3 km (Scripps Pier and Mt. Soledad) from 15 February 2023 to 14 February 2024. Comparing the two sites shows the strong influence of upwind sources that results in similar monthly compositions at both sites. The seasonal changes in chemical mass concentrations were largely driven by the upwind source regions, with coastal northwesterly back-trajectories occurring 63−65% of the year and bringing submicrometer mass concentrations that were lower than the EPCAPE average for all trajectories at each site. In contrast, refractory black carbon (rBC) and nonrefractory (NR)-organics and nitrate mass concentrations exceeded EPCAPE average concentrations for back-trajectories from urban areas such as Los Angeles-Long Beach. For hourly measurements, NR-organics and non-sea-salt (NSS)-sulfate mass concentrations at Mt. Soledad were correlated strongly (r = 0.73−0.82) to those measured at Scripps Pier, but NR-nitrate was correlated only moderately (r = 0.63). The explanation for the lower correlation of NR-nitrate is both emissions between the sites and semivolatility, with semivolatility accounting for site-to-site changes in daily averages of +0.01 μg m −3 per percentage site-to-site difference in relative humidity and −0.07 μg m −3 per degree Celsius site-to-site difference in temperature. On average, comparing Scripps Pier to Mt. Soledad, NR-nitrate was higher by 29% because of relative humidity and lower by −26% because of temperature. NR-nitrate and rBC mass concentrations at Scripps Pier for nighttime were 13−15% higher than those for daytime because land breezes brought higher inland concentrations. Concentrations of rBC were 52% higher at Mt. Soledad than those measured at Scripps Pier, accompanied by increases in tracers for brake wear because of traffic on the steep roads within 10 m of that site. The implications are that these nearby sites had comparable monthly concentrations of measured components due to their similar backtrajectories, but hourly and daily concentration differences supported quantification of the meteorological effects from relative humidity and temperature on semivolatile NR-nitrate as well as minor differences from land−sea breezes and local emissions.

Aerosols↗
DOE OSTI2025

Quasiclassical sampling and Wigner sampling of initial vibrational coordinates and momenta for polyatomic molecules in Monte Carlo molecular dynamics simulations

In a quasiclassical trajectory simulation, the vibrational modes are initialised with quantised vibrational energies, but vibrational phases are sampled by Monte Carlo. This requires an algorithm to assign coordinates and momenta to the various atoms. In this work, we present two methods for implementing this for nonrotating polyatomic molecules, namely, fixed-energy vibrational-state-selected initial conditions and thermal initial conditions. We also present a method for initiating classical trajectories with a ground-state Wigner distribution. These vibrational treatments are sufficient to initialise trajectories for unimolecular processes, and we also show how they can be applied to simulate bimolecular collision processes. The treatments of unimolecular and bimolecular collision processes are available in two Python codes called wigner_state_selected.py and bimolecular_collision.py, respectively, which will generate initial condition files that are recognisable by the SHARC and SHARC-MN computer programs for dynamics calculations. Both codes are available as standalone programs, as well as being included in SHARC-MN, and they will be included in future versions of SHARC. Here, the methods implemented in these codes are mostly also available in the ANT computer program, and those that are not available in ANT will be incorporated in future versions of ANT.

Wigner distribution↗
DOE OSTI2024

Light-ray wave functions and integrability

Using integrability, we construct (to leading order in perturbation theory) the explicit form of twist-three light-ray operators in planar $\mathcal{N}$ = 4 SYM. This construction allows us to directly compute analytically continued CFT data at complex spin. We derive analytically the “magic” decoupling zeroes previously observed numerically. Using the Baxter equation, we also show that certain Regge trajectories merge together into a single unifying Riemann surface. Perhaps more surprisingly, we find that this unification of Regge trajectories is not unique. If we organize twist-three operators differently into what we call “cousin trajectories” we find infinitely more possible continuations. We speculate about which of these remarkable features of twist-three operators might generalize to other operators, other regimes and other theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Local practically safe extremum seeking with assignable rate of attractivity to the safe set

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured, static objective function while maintaining a measured, static metric of safety (a control barrier function or CBF) to be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small through appropriately chosen design constants. We also guarantee an assignable “attractivity” rate: from unsafe initial conditions, the trajectories approach the safe set, in the sense of the measured CBF, at a rate no slower than a user-assigned rate. Similarly, from safe initial conditions, the trajectories approach the unsafe set, in the sense of the CBF, no faster than the assigned attractivity rate. The feature of assignable attractivity is not present in the semiglobal version of safe extremum seeking, where the semiglobality of convergence is achieved by slowing the adaptation. We also demonstrate local convergence of the parameter to a neighborhood of the minimum of a quadratic objective function constrained to the safe set with a linear CBF. The ASfES algorithm and analysis are multivariable, but we also extend the algorithm to a Newton-Based ASfES scheme which we show is only useful in the scalar case. The proven properties of the designs are illustrated through simulation examples.

42 ENGINEERING↗
DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2024

High-dimensional control co-design of a wave energy converter with a novel pitch resonator power takeoff system

Researchers are exploring adding wave energy converters to existing oceanographic buoys to provide a predictable source of renewable power. A ”pitch resonator” power take-off system has been developed that generates power using a geared flywheel system designed to match resonance with the pitching motion of the buoy. However, the novelty of the concept leaves researchers uncertain about various design aspects of the system. This work presents a novel design study of a pitch resonator to inform design decisions for an upcoming deployment of the system. The assessment uses control co-design via WecOptTool to optimize control trajectories for maximal electrical power production while varying five design parameters of the pitch resonator. Given the large search space of the problem, the control trajectories are optimized within a Monte Carlo analysis to identify optimal designs, followed by parameter sweeps around the optimum to identify trends between the design parameters. The gear ratio between the pitch resonator spring and flywheel are found to be the most sensitive design variables to power performance. Finally, the assessment also finds similar power generation for various sizes of resonator components, suggesting that correctly designing for optimal control trajectories at resonance is more critical to the design than component sizing.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2024

From Coherence to Function: Exploring the Connection in Chemical Systems

The role of quantum mechanical coherences or coherent superposition states in excited state processes has received considerable attention in the last two decades largely due to advancements in ultrafast laser spectroscopy. These coherence effects hold promise for enhancing the efficiency and robustness of functionally relevant processes, even when confronted with energy disorder and environmental fluctuations. Understanding coherence deeply drives us to unravel mechanisms and dynamics controlled by order and synchronization at a quantum mechanical level, envisioning optical control of coherence to enhance functions or create new ones in molecular and material systems. In this frontier, the interplay between electronic and vibrational dynamics, specifically the influence of vibrations in directing electronic dynamics, has emerged as the leading principle. Here, two energetically disparate quantum degrees of freedom work in-sync to dictate the trajectory of an excited state reaction. Moreover, with the vibrational degree being directly related to the structural composition of molecular or material systems, new molecular designs could be inspired by tailoring certain structural elements. In the realm of chemical kinetics, our understanding of the dynamics of chemical transformations is underpinned by fundamental theories, such as transition state theory, activated rate theory, and Marcus theory. These theories elucidate reaction rates by considering the energy barriers that must be overcome for reactants to transform into products. Those barriers are surmounted by the stochastic nature of energy gap fluctuations within reacting systems, emphasizing that the reaction coordinate, the pathway from reactants to products, is not rigidly defined by a specific vibrational motion but encompasses a diverse array of molecular motions. While less is known about the involvement of specific intramolecular vibrational modes, their significance in certain cases cannot be overlooked. In this Account, we summarize key experimental findings that offer deeper insights into the complex electronic–vibrational trajectories encompassing excited states afforded from state-of-the-art ultrafast laser spectroscopy in three exemplary processes: photoinduced electron transfer, singlet–triplet intersystem crossing, and intramolecular vibrational energy flow in molecular systems. We delve into the rapid decoherence, or loss of phase and amplitude correlations, of vibrational coherences along promoter vibrations during subpicosecond intersystem crossing dynamics in a series of binuclear platinum complexes. This rapid decoherence illustrates the vibration-driven reactive pathways from the Franck–Condon state to the curve crossing region. We also explore the generation of new vibrational coherences induced by impulsive reaction dynamics rather than by the laser pulse in these systems, which sheds light on specific energy dissipation pathways and thereby on the progression of the reaction trajectory in the vicinity of the curve crossing on the product side. Another property of vibrational coherences, amplitude, reveals how energy can flow from one vibration to another in the electronic excited state of a terpyridine–molybdenum complex hosting a nonreactive dinitrogen substrate. In conclusion, a slight change in vibrational energy triggers a quasi-resonant interaction, leading to constructive wavepacket interference and ultimately intramolecular vibrational redistribution from a Franck–Condon active terpyridine vibration to a dinitrogen stretching vibration, energizing the dinitrogen bond.

Electrical energy↗
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Going Off Grid: A Comparative Study of the Lagrangian and Eulerian Perspectives of New Particle Formation Events

New particle formation and growth (NPF&G) is the process by which ultrafine particles are formed from gas-phase precursors. NPF&G is the dominant source of global aerosol number with important influences on climate. Most observations of NPF&G events are conducted at stationary sites; however, NPF&G observed from stationary sites is influenced by gradual or rapid changes in the air masses passing over the site, complicating NPF&G analysis. In this work, we use observations and a 3D aerosol model to compare aerosol size distributions at a stationary site (Southern Great Plains [SGP] observatory, Oklahoma, USA) and along Lagrangian trajectories crossing the site. The model simulates the NPF&G events reasonably well at SGP. Using the model to compare the Lagrangian and stationary perspectives, we can explain previously unanalyzable days with some evidence of NPF&G as either non-event or analyzable NPF&G days. We find most of the unanalyzable NPF&G days are due to isolated and inhomogeneous NPF&G occurring upwind of the stationary site, often in the outflow of urban regions. Finally, we compare formation rates of 3 nm particles, growth rates, and the survival probability of 3 nm particles growing to 25 nm between the stationary and Lagrangian perspectives. Because of the much larger number of analyzable days along the Lagrangian trajectories, this perspective potentially provides more robust statistics and better characterization of NPF&G event extremes. Our method for extracting chemical/physical properties along Lagrangian trajectories from 3D models can be applied to a wide range of science questions.

O’Donnell, Samuel E. [Colorado State Univ., Fort C↗
DOE OSTI2024

Environmental damping and vibrational coupling of confined fluids within isolated carbon nanotubes

Abstract Because of their large surface areas, nanotubes and nanowires demonstrate exquisite mechanical coupling to their surroundings, promising advanced sensors and nanomechanical devices. However, this environmental sensitivity has resulted in several ambiguous observations of vibrational coupling across various experiments. Herein, we demonstrate a temperature-dependent Radial Breathing Mode (RBM) frequency in free-standing, electron-diffraction-assigned Double-Walled Carbon Nanotubes (DWNTs) that shows an unexpected and thermally reversible frequency downshift of 10 to 15%, for systems isolated in vacuum. An analysis based on a harmonic oscillator model assigns the distinctive frequency cusp, produced over 93 scans of 3 distinct DWNTs, along with the hyperbolic trajectory, to a reversible increase in damping from graphitic ribbons on the exterior surface. Strain-dependent coupling from self-tensioned, suspended DWNTs maintains the ratio of spring-to-damping frequencies, producing a stable saturation of RBM in the low-tension limit. In contrast, when the interior of DWNTs is subjected to a water-filling process, the RBM thermal trajectory is altered to that of a Langmuir isobar and elliptical trajectories, allowing measurement of the enthalpy of confined fluid phase change. These mechanisms and quantitative theory provide new insights into the environmental coupling of nanomechanical systems and the implications for devices and nanofluidic conduits.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Land-use and atmospheric shifts jointly amplify U.S. drought-driven crop losses

Agricultural drought (AD) poses a major threat to food security, yet its future risk remains uncertain under co-evolving atmospheric conditions and land-use trajectories. Using an integrated, multi-sector modeling framework, we project AD risks for major crops across the contiguous United States (CONUS) through 2055 under a range of plausible futures that capture thermodynamic changes and land-use and land-cover change (LULCC) trajectories. Model projections reveal that drought-driven crop production losses increase sharply by nearly 60% for corn, 250% for wheat, and 135% for soybean relative to historical levels. The primary drivers of these increases, which include atmospheric shifts and LULCC, vary by region and crop type. LULCC acting as an important risk amplifier in regions experiencing cropland expansion into drought-prone regions, such as the Great Plains and northwestern U.S. Wheat exhibits the largest projected loss increases, a result that remains robust across scenarios. These findings highlight that interactions between atmospheric conditions and land-use trajectories shape future agricultural drought risk and should be jointly considered to support effective adaptation and food-system planning.

Yao, Lili↗
DOE OSTI2026

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards modelling AR Sco: calibration – reproducing high-energy pulsar emission and testing convergence to Aristotelian electrodynamics

In recent years, kinetic simulations have been crucial to further our understanding of pulsar electrodynamics. Yet, due to the large-scale separation between the gyro-period and the stellar rotation period, resolving the particle gyration has been computationally unfeasible for realistic pulsar parameters. The main aim of this work is comparing our gyro-phase-resolved model with a gyro-centric pulsar model, where our model solves the general equations of motion with included radiation reaction using a higher order numerical solver with adaptive time-steps. Specifically, we aim to (i) reproduce a pulsar’s high-energy emission maps, namely one with 10 per cent of the surface B-field strength of Vela, and the spectra produced by an independent gyro-centric pulsar emission model; and (ii) test convergence of these results to the radiation-reaction limit of Aristotelian electrodynamics. (iii) Additionally, we identify the effect that a large $E_{\parallel }$-field has on the trajectories and radiation calculations. We find that we can reproduce the curvature radiation emission maps and spectra well, using 10 per cent field strengths of the Vela pulsar and injecting our particles at a higher altitude in the magnetosphere. Using sufficiently large $E_{\parallel }$-fields, our numeric results converge to the analytic radiation-reaction limit trajectories. Additionally, we illustrate the importance of accounting for the $\mathbf {E}\times \mathbf {B}$-drift in the particle trajectories and radiation calculations, validating the Harding and collaborators’ model approach. Lastly, we found that our model deals very well with the high-radiation-reaction and high-field regimes present in pulsars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Shearless effective barriers to chaotic transport induced by even twin islands in nontwist systems

For several decades now it has been known that systems with shearless invariant tori, nontwist Hamiltonian systems, possess barriers to chaotic transport. These barriers are resilient to breakage under perturbation and therefore regions where they occur are natural places to look for barriers to transport. Here we describe a kind of effective barrier that persists after the shearless torus is broken. Because phenomena are generic, for convenience we study the standard nontwist map (SNM), an area-preserving map that violates the twist condition locally in the phase space. The barrier occurs in nontwist systems when twin even period islands are present, which happens for a broad range of parameter values in the SNM. With a phase space composed of regular and irregular orbits, the movement of chaotic trajectories is hampered by the existence of shearless curves, total barriers, and a network of partial barriers formed by the stable and unstable manifolds of the hyperbolic points. Being a degenerate system, the SNM has twin islands and, consequently, twin hyperbolic points. We show that the structures formed by the manifolds intrinsically depend on period parity of the twin islands. For this even scenario the structure that we call a torus free barrier occurs because the manifolds of different hyperbolic points form an intricate chain atop a dipole configuration and the transport of chaotic trajectories through the chain becomes a rare event. This structure impacts the emergence of transport, the escape basin for chaotic trajectories, the transport mechanism, and the chaotic saddle. The case of odd periodic orbits is different: we find for this case the emergence of transport immediately after the breakup of the last invariant curve, and this leads to a scenario of higher transport, with intricate escape basin boundary and a chaotic saddle with nonuniformly distributed points.

classical mechanics↗
DOE OSTI2026

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)↗