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Results for “Complex dynamical systems”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Large-scale atomistic calculations of clusters in intense x-ray pulses

Here, we present the methodology of our recently developed Monte-Carlo/ Molecular-Dynamics method for studying the fundamental ultrafast dynamics induced by high-fluence, high-intensity x-ray free electron laser (XFEL) pulses in clusters. The quantum nature of the initiating ionization process is accounted for by a Monte Carlo method to calculate probabilities of electronic transitions, including photo absorption, inner-shell relaxation, photon scattering, electron collision and recombination dynamics, and thus track the transient electronic configurations explicitly. The freed electrons and ions are followed by classical particle trajectories using a molecular dynamics algorithm. These calculations reveal the surprising role of electron-ion recombination processes that lead to the development of nonuniform spatial charge density profiles in x-ray excited clusters over femtosecond timescales. In the high-intensity limit, it is important to include the recombination dynamics in the calculated scattering response even for a 2- fs pulse. We also demonstrate that our numerical codes and algorithms can make e!cient use of the computational power of massively parallel supercomputers to investigate the intense-field dynamics in systems with increasing complexity and size at the ultrafast timescale and in non-linear x-ray interaction regimes. In particular, picosecond trajectories of XFEL clusters with attosecond time resolution containing millions of particles can be e!ciently computed on upwards of 262,144 processes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Nearly Quantum-Limited Josephson-Junction Frequency-Comb Synthesizer

While coherently driven Kerr microcavities have rapidly matured as a platform for frequency-comb formation, such microresonators generally possess weak Kerr coefficients; consequently, triggering comb generation requires millions of photons to be circulating inside the cavity. This suppresses the role of quantum fluctuations in the dynamics of the comb. In this paper, we realize a minimal version of coherently driven Kerr-mediated microwave-frequency combs in the circuit quantum electrodynamics (cQED) architecture, where the fluctuations of the quantum vacuum are the primary limitation on comb coherence. We achieve a comb phase coherence of up to 35 μs, approaching the theoretical device quantum limit of 55 μs and vastly longer than the inherent lifetimes of the modes, of 13 ns. The ability within cQED to engineer stronger nonlinearities than optical microresonators, together with operation at cryogenic temperatures, and the excellent agreement of comb dynamics with quantum theory indicates a promising platform for the study of complex dynamics of quantum nonlinear systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Quality control procedures for dynamic treatment delivery techniques involving couch motion

In this study, the authors introduce and demonstrate quality control procedures for evaluating the geometric and dosimetric fidelity of dynamic treatment delivery techniques involving treatment couch motion synchronous with gantry and multileaf collimator (MLC). Tests were designed to evaluate positional accuracy, velocity constancy and accuracy for dynamic couch motion under a realistic weight load. A test evaluating the geometric accuracy of the system in delivering treatments over complex dynamic trajectories was also devised. Custom XML scripts that control the Varian TrueBeam™ STx (Serial #3) axes in Developer Mode were written to implement the delivery sequences for the tests. Delivered dose patterns were captured with radiographic film or the electronic portal imaging device. The couch translational accuracy in dynamic treatment mode was 0.01 cm. Rotational accuracy was within 0.3°, with 0.04 cm displacement of the rotational axis. Dose intensity profiles capturing the velocity constancy and accuracy for translations and rotation exhibited standard deviation and maximum deviations below 3%. For complex delivery involving MLC and couch motions, the overall translational accuracy for reproducing programmed patterns was within 0.06 cm. The authors conclude that in Developer Mode, TrueBeam™ is capable of delivering dynamic treatment delivery techniques involving couch motion with good geometric and dosimetric fidelity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Tethys: A Spatiotemporal Downscaling Model for Global Water Demand

Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

How to ADAPT

The ADAPT software allows for the examination of aleatory and epistemic uncertainties in complex system transients using the Dynamic Event Tree (DET) methodology. This manual outlines the principles of operation of ADAPT and provides directions for its use. Future plans for the code are briefly outlined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

w18_empower-aids - HIV Envelope at Multiple Scales [Slides]

Being the sole viral protein entity expressed on the surface of the Human Immunodeficiency Virus (HIV) envelope, the Envelope Glycoprotein (Env) remains the only plausible immunological target. Nevertheless, extreme dynamic heterogeneity, structural complexity, and system size make the study of these glycoproteins immensely challenging. We have utilized the Institutional Computing (IC) resources to overcome some of these challenges in computational modeling of the Env glycoproteins. This integrated technique can sufficiently sample a physiologically relevant conformational space accessible to carbohydrates, in a very short time. This method has been validated by quantitatively comparing to experimental cryoEM maps of the HIV Env protein. We have employed graph theory to capture the glycan shield topological network, pinpoint potential interaction pathways, and identify concerted behavior of the glycans. Analyses of various network attributes, such as relative centrality of different glycan positions, identification of communities, and critical subnetwork features, have aided in detailed examination of the glycan shield. Starting from select structures modeled by the pipeline, we are performing large-scale atomistic MD simulations to study the temporal behavior of this system, which will help elucidate biologically relevant antibody behavior. This method can be seamlessly extended to benefit research in Zika, Ebola and other high-density glycosylated systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Aerosol Deposition: Room Temperature Solid-State Deposition of Ceramics

The ability to integrate ceramics with other materials has been limited by the high temperature s (>800C) associated with ceramic processing. A novel process, known as aerosol deposition (AD), capable of preparing ceramic films at room temperature (RT) has been the subject of recent interest in the thermal spray and microelectronics communities. In this process, ceramic particles are accelerated using pressurized gas, impacted on a substrate and form a dense film under vacuum. This revolutionary process eliminates high temperature processing, enabling new coatings and microelectronic device integration as a back end of line process, in which ceramics can be deposited on metals, plastics, and glasses . Future impact s of this technology on Sandia's mission could include improved ceramic integration, miniaturized magnetic circulators in radar applications, new RF communication products, modification of commercial - off - the - shelf electronics, fabrication of conformal capacitors, thin batteries, glass - to - metal seals, and transparent electronics. Currently, optimization for RT solid - state deposition of ceramics is achieved empirically and fundamental mechanisms for ceramic particle - particle bonding are not well understood. Obtaining this knowledge will allow process - microstructure - property relation ship realization and will enable a differentiating ceramic integration capability. This LDRD leveraged Sandias existing equipment and capabilities in simulation, experimentation, and materials characterization to discover the fundamental mechanisms for ceramic particle deformation, particle - substrate bonding, and particle - particle bonding in RT consolidated films. RT deformation of individual Al 2 O 3 particles was examined computationally and experimentally as a model system for understanding the complex dynamics associated with in vacuo RT deposition conditions associated with AD. Subsequently, particle - substrate bonding and particle - particle bonding in AD Al 2 O 3 consolidated films were examined computationally and experimentally. Fundamental mechanisms behind the AD process were proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Efficient Use of an Adapting Database of Ab Initio Calculations To Generate Accurate Newtonian Dynamics

We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann–Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. Here, we discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Considering coasts: Adapting terrestrial models to characterize coastal wetland ecosystems

The Energy Exascale Earth System Model (E3SM) simulates fully coupled processes and interactions among water, energy, carbon and nutrient cycles. E3SM connects vegetation and soil dynamics through nutrient uptake, plant production, litterfall and decomposition as a function of abiotic parameters (e.g. temperature and moisture). However, E3SM is designed to characterize terrestrial ecosystems and connects land and open ocean systems using a single streamflow transport term, ignoring the complex dynamics of energy, water, carbon, and nutrients in coastal systems. The goals of our project were to: (1) Parameterize a point version of E3SM to capture coastal wetland habitats and (2) Determine marsh community responses to increased temperature and elevated CO 2 . We adapted a version of the E3SM land model, previously configured to represent forested bog hydrology to a coastal ecosystem using datasets from field experiments conducted at the Smithsonian Environmental Research Center's Global Change Research Wetland (GCReW). Tidal forcing in a marsh environment was simulated using a two-column system in which the columns are connected by lateral hydrologic flows. One column simulates interactions between vegetation and soil while a second column simulates variation in water level (both tidal and sea level rise). The updated model captures many aspects of the field experiments, showing that plant community responses to environmental change are non-linear, non-additive and different between plant types. Elevated CO 2 treatments increased C 3 plant biomass more than C 4 (33% vs 17%). Temperature exacerbated CO 2 responses in C 3 plants (0 °C: 26%, 5.1 °C: 56%). We were more successful at characterizing C 3 than C 4 responses and simulating above rather than belowground biomass production. Furthermore, the next steps will include updates to key physiological parameters such as root:shoot carbon allocation and the addition of mechanistic feedbacks between vegetation and biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Learning dynamical systems from data: An introduction to physics-guided deep learning

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Mixed-mode oscillations in a three-timescale coupled Morris–Lecar system

Mixed-mode oscillations (MMOs) are complex oscillatory behaviors of multiple-timescale dynamical systems in which there is an alternation of large-amplitude and small-amplitude oscillations. It is well known that MMOs in two-timescale systems can arise either from a canard mechanism associated with folded node singularities or a delayed Andronov–Hopf bifurcation (DHB) of the fast subsystem. While MMOs in two-timescale systems have been extensively studied, less is known regarding MMOs emerging in three-timescale systems. In this work, we examine the mechanisms of MMOs in coupled Morris–Lecar neurons with three distinct timescales. We investigate two kinds of MMOs occurring in the presence of a singularity known as canard-delayed-Hopf (CDH) and in cases where CDH is absent. In both cases, we examine how features and mechanisms of MMOs vary with respect to variations in timescales. Our analysis reveals that MMOs supported by CDH demonstrate significantly stronger robustness than those in its absence. Moreover, we show that the mere presence of CDH does not guarantee the occurrence of MMOs. This work yields important insights into conditions under which the two separate mechanisms in two-timescale context, canard and DHB, can interact in a three-timescale setting and produce more robust MMOs, particularly against timescale variations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Trajectory-probed instability and statistics of desynchronization events in coupled chaotic systems

Complex systems, such as financial markets, earthquakes, and neurological networks, exhibit extreme events whose mechanisms of formation are not still completely understood. These mechanisms may be identified and better studied in simpler systems with dynamical features similar to the ones encountered in the complex system of interest. For instance, sudden and brief departures from the synchronized state observed in coupled chaotic systems were shown to display non-normal statistical distributions similar to events observed in the complex systems cited above. The current hypothesis accepted is that these desynchronization events are influenced by the presence of unstable object(s) in the phase space of the system. Here, we present further evidence that the occurrence of large events is triggered by the visitation of the system's phase-space trajectory to the vicinity of these unstable objects. In the system studied here, this visitation is controlled by a single parameter, and we exploit this feature to observe the effect of the visitation rate in the overall instability of the synchronized state. We find that the probability of escapes from the synchronized state and the size of those desynchronization events are enhanced in attractors whose shapes permit the chaotic trajectories to approach the region of strong instability. This result shows that the occurrence of large events requires not only a large local instability to amplify noise, or to amplify the effect of parameter mismatch between the coupled subsystems, but also that the trajectories of the system wander close to this local instability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Exploring the Limits of Biological Complexity Amenable to Studies by Incoherent Neutron Spectroscopy

The wavelengths of neutrons available at neutron scattering facilities are comparable with intra- and inter-molecular distances, while their energies are comparable with molecular vibrational energies, making such neutrons highly suitable for studies of molecular-level dynamics. The unmistakable trend in neutron spectroscopy has been towards measurements of systems of greater complexity. Several decades of studies of dynamics using neutron scattering have witnessed a progression from measurements of solids to liquids to protein complexes and biomembranes, which may exhibit properties characteristic of both solids and liquids. Over the last two decades, the frontier of complexity amenable to neutron spectroscopy studies has reached the level of cells. Considering this a baseline for neutron spectroscopy of systems of the utmost biological complexity, we briefly review what has been learned to date from neutron scattering studies at the cellular level and then discuss in more detail the recent strides into neutron spectroscopy of tissues and whole multicellular organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Functional, Hierarchical Colloidal Liquid Crystal Gels and Liquid Crystal Elastomers Nanocomposites. Final Report

Biological systems rely on a hierarchy of dynamic, complex and reconfigurable compartments to carry out functions essential for life. This hierarchical organization serves as an amplifier that allows highly localized, molecular events to propagate into the mesoscale, resulting in dynamic functional properties of biological systems that have not yet been fully realized in synthetic material designs. We pursued a program of research in which we recreated such principles using synthetic liquid crystals (LC) as a versatile platform. We elucidated new hierarchical design strategies that heavily leverage surface anchoring, coexisting phases and interfacial tension gradient to realize equilibrium and non-equilibrium, dynamic mesoscale phenomena in the context of micrometer-sized LC droplets containing multiple compartments. Specifically, we unmasked how equilibrium and dynamical phenomena emerged from hierarchical organizations in reconfigurable, thermotropic liquid crystalline assemblies. The long-term impact of the project is to advance new hierarchical designs of complex soft matter systems in which biomimetic principles, including the propagation of events or information over multiple temporal and spatial scales, are enacted in a facile manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction (StFT) v1.0

We propose a novel machine learning model spatio-temporal Fourier transformer (StFT) to emulate long-term dynamics of multi-scale and multi-physics systems. Our method StFT overcomes the limitations of rapid error accumulation, particularly in long-term forecasting of systems characterized by complex and coupled dynamics. StFT achieves outstanding accuracy and computational efficiency by effectively capturing multi-scale interactions, and quantify the uncertainties inherent in the predictions. Our model leverages a structured hierarchy of StFT blocks, and explicitly captures dynamics across both macro- and micro- spatial scales. Evaluations conducted on three benchmark datasets (plasma, fluid, and atmospheric dynamics) demonstrate the advantages of our approach over state-of-the-art ML methods.

Bai, Zhe [Lawrence Berkeley National Laboratory (L↗