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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗
DOE OSTI2026

Multibody for Everybody (M4E): A Symbolic Dynamics Modeling Tool with Applications in Simulation, Control, and Optimization

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗
DOE OSTI2013

Structure, dynamics and biophysics of the cytoplasmic protein–protein complexes of the bacterial phosphoenolpyruvate: Sugar phosphotransferase system

The bacterial phosphotransferase system (PTS) couples phosphoryl transfer, via a series of bimolecular protein–protein interactions, to sugar transport across the membrane. The multitude of complexes in the PTS provides a paradigm for studying protein interactions, and for understanding how the same binding surface can specifically recognize a diverse array of targets. Fifteen years of work aimed at solving the solution structures of all soluble protein–protein complexes of the PTS has served as a test bed for developing NMR and integrated hybrid approaches to study larger complexes in solution and to probe transient, spectroscopically invisible states, including encounter complexes. We review these approaches, highlighting the problems that can be tackled with these methods, and summarize the current findings on protein interactions.

bacterial phosphotransferase system↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Nanoscale Probing of Dynamics in Local Molecular Environments

Vibrational spectroscopy can provide information about structure, coupling, and dynamics underlying the properties of complex molecular systems. While measurements of spectral line broadening can probe local chemical environments, the spatial averaging in conventional spectroscopies limits insight into underlying heterogeneity, in particular in disordered molecular solids. Here, using femtosecond infrared scattering scanning near-field optical microscopy (IR s-SNOM), we resolve in vibrational free-induction decay (FID) measurements a high degree of spatial heterogeneity in polytetrafluoroethylene (PTFE) as a dense molecular model system. In nanoscopic probe volumes as small as 103 vibrational oscillators, we approach the homogeneous response limit, with extended vibrational dephasing times of several picoseconds, that is, up to 10 times the inhomogeneous lifetime, and spatial average converging to the bulk ensemble response. We simulate the dynamics of relaxation with a finite set of local vibrational transitions subject to random modulations in frequency. The combined results suggest that the observed heterogeneity arises due to static and dynamic variations in the local molecular environment. This approach thus provides real-space and real-time visualization of the subensemble dynamics that define the properties of many functional materials.

Atkin, Joanna M.↗
DOE OSTI2021

Fluid dynamic simulation and analysis of water-cooling systems for the Electron-Ion Collider

The Electron-Ion Collider is the newest large-scale project at Brookhaven National Laboratory. The collider’s purpose is to provide further advancements in the knowledge of the universe’s origin by accelerating particles near the speed of light. Our project for this 3.8 km ring was to create a thermal hydraulic steady-state simulation design of the water-cooling system to be cost-effective and energy efficient, as envisioned by Charlie Foltz, the EIC Infrastructure Division Director. The system would include a supply and return header, which cools several thousand components of the ring. The water would then be returned and cooled down using a system of cooling towers and plate and frame heat exchangers. Due to the size of the system and the complexity of the network analysis, a fluid dynamic simulation software, AFT Fathom, was used. Since previous methods of maintaining systems relied on building upon smaller real-life models and implementing empirical data, this flow model was unique and first of a kind in the domain of accelerator design, construction and operation. Therefore, our hydraulic team piloted a new method to perform network analysis on a large scale cooling system. We successfully created several test scenarios for system behavior in a shorter time compared to the method of performing hand calculations. Cooling specifications for heat rejection, pressure drop, flow rate, and pipe sizing were changed based on the individual systems of the vacuum, radio frequency (RF), magnet and power supply, and cryogenics sections. Finally, we used DOE guidelines to perform life-cycle cost analysis with net present value and carbon saving analysis on the systems where pipe size could be optimized.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A LASER POWDER BED FUSION ADDITIVE MANUFACTURING CHARACTERIZATION APPROACH AND DATASET HIGHLIGHTING IMPLICATIONS FOR CROSS-PLATFORM PERFORMANCE CONSISTENCY

Additive Manufacturing (AM) has remained a rapidly growing technology over the past decade, which affords many benefits. However, there is one main hinderance to widespread adoption: Products produced on one Laser Powder Bed Fusion (LPBF) AM system can vary in mechanical performance from those produced on another system. This is due to the fact that while LPBF AM system is producing a product’s shape, it is also producing the product’s material. Due to the complex and dynamic nature of LPBF, items fabricated on one system can be different in terms of dimensions, microstructure and quality compared to items fabricated on a different make or model system. This lack of cross-platform performance consistency makes widespread adoption challenging. This work was created to help understand the variation between different Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM) systems across different manufacturers, models, and users, and to provide insight into how to ensure product consistency regardless of system selection.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Distributed Flexibility Characterization and Resource Allocation Strategies for Multi-zone Commercial Buildings in the Smart Grid

The HVAC (Heating, Ventilation, and Air- Conditioning) system of commercial buildings is a complex system with a large number of dynamically interacting components. In particular, the thermal dynamics of each zone are coupled with those of the neighboring zones. In this paper, we study a multi-agent based approach to model and control commercial building HVAC system for providing grid services. In the multi-agent system (MAS), individual zones are modeled as agents that can communicate, interact, and negotiate with one another to achieve a common objective. We first propose a distributed characterization method on the aggregated airflow (and thus fan power) flexibility that the HVAC system can provide to the ancillary service market. Then, we propose a Nash-bargaining based airflow allocation strategy to track a dispatch signal (that is within the offered flexibility limit) while respecting the preference and flexibility of individual zones. Moreover, we devise a distributed algorithm to obtain the Nash bargaining solution via dual decomposition and average consensus. Numerical simulations illustrate that the proposed distributed protocols are much more scalable than the centralized approaches especially when the system becomes larger and more complex.

Hao, He↗
DOE OSTI2022

An adaptive scalable fully implicit algorithm based on stabilized finite element for reduced visco-resistive MHD

The magnetohydrodynamics (MHD) equations are continuum models used in the study of a wide range of plasma physics systems, including the evolution of complex plasma dynamics in tokamak disruptions. However, efficient numerical solution methods for MHD are extremely challenging due to disparate time and length scales, strong hyperbolic phenomena, and nonlinearity. Additionally, therefore the development of scalable, implicit MHD algorithms and high-resolution adaptive mesh refinement strategies is of considerable importance. In this work, we develop a high-order stabilized finite-element algorithm for the reduced visco-resistive MHD equations based on the MFEM finite element library (mfem.org). The scheme is fully implicit, solved with the Jacobian-free Newton-Krylov (JFNK) method with a physics-based preconditioning strategy. Our preconditioning strategy is a generalization of the physics-based preconditioning methods in Chacón et al. (2002) to adaptive, stabilized finite elements. Algebraic multigrid methods are used to invert sub-block operators to achieve scalability. A parallel adaptive mesh refinement scheme with dynamic load-balancing is implemented to efficiently resolve the multi-scale spatial features of the system. Our implementation uses the MFEM framework, which provides arbitrary-order polynomials and flexible adaptive conforming and non-conforming meshes capabilities. Results demonstrate the accuracy, efficiency, and scalability of the implicit scheme in the presence of large scale disparity. The potential of the AMR approach is demonstrated on an island coalescence problem in the high Lundquist-number regime (≥ 10 7 ) with the successful resolution of plasmoid instabilities and thin current sheets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

MO-FG-209-02: Development of a Virtual Breast Phantom From a Multi-Modality Perspective

This symposium will review recent advances in the simulation methods for evaluation of novel breast imaging systems – the subject of AAPM Task Group TG234. Our focus will be on the various approaches to development and validation of software anthropomorphic phantoms and their use in the statistical assessment of novel imaging systems using such phantoms along with computational models for the x-ray image formation process. Due to the dynamic development and complex design of modern medical imaging systems, the simulation of anatomical structures, image acquisition modalities, and the image perception and analysis offers substantial benefits of reduced cost, duration, and radiation exposure, as well as the known ground-truth and wide variability in simulated anatomies. For these reasons, Virtual Clinical Trials (VCTs) have been increasingly accepted as a viable tool for preclinical assessment of x-ray and other breast imaging methods. Activities of TG234 have encompassed the optimization of protocols for simulation studies, including phantom specifications, the simulated data representation, models of the imaging process, and statistical assessment of simulated images. The symposium will discuss the state-of-the-science of VCTs for novel breast imaging systems, emphasizing recent developments and future directions. Presentations will discuss virtual phantoms for intermodality breast imaging performance comparisons, extension of the breast anatomy simulation to the cellular level, optimized integration of the simulated imaging chain, and the novel directions in the observer models design. Learning Objectives: Review novel results in developing and applying virtual phantoms for inter-modality breast imaging performance comparisons; Discuss the efforts to extend the computer simulation of breast anatomy and pathology to the cellular level; Summarize the state of the science in optimized integration of modules in the simulated imaging chain; Compare novel directions in the design of observer models for task based validation of imaging systems. PB: Research funding support from the NIH, NSF, and Komen for the Cure; NIH funded collaboration with Barco, Inc. and Hologic, Inc.; Consultant to Delaware State Univ. and NCCPM, UK. AA: Employed at Barco Healthcare.; P. Bakic, NIH: (NIGMS P20 #GM103446, NCI R01 #CA154444); M. Das, NIH Research grants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-209-05: Towards a Feature-Based Anthropomorphic Model Observer

This symposium will review recent advances in the simulation methods for evaluation of novel breast imaging systems – the subject of AAPM Task Group TG234. Our focus will be on the various approaches to development and validation of software anthropomorphic phantoms and their use in the statistical assessment of novel imaging systems using such phantoms along with computational models for the x-ray image formation process. Due to the dynamic development and complex design of modern medical imaging systems, the simulation of anatomical structures, image acquisition modalities, and the image perception and analysis offers substantial benefits of reduced cost, duration, and radiation exposure, as well as the known ground-truth and wide variability in simulated anatomies. For these reasons, Virtual Clinical Trials (VCTs) have been increasingly accepted as a viable tool for preclinical assessment of x-ray and other breast imaging methods. Activities of TG234 have encompassed the optimization of protocols for simulation studies, including phantom specifications, the simulated data representation, models of the imaging process, and statistical assessment of simulated images. The symposium will discuss the state-of-the-science of VCTs for novel breast imaging systems, emphasizing recent developments and future directions. Presentations will discuss virtual phantoms for intermodality breast imaging performance comparisons, extension of the breast anatomy simulation to the cellular level, optimized integration of the simulated imaging chain, and the novel directions in the observer models design. Learning Objectives: Review novel results in developing and applying virtual phantoms for inter-modality breast imaging performance comparisons; Discuss the efforts to extend the computer simulation of breast anatomy and pathology to the cellular level; Summarize the state of the science in optimized integration of modules in the simulated imaging chain; Compare novel directions in the design of observer models for task based validation of imaging systems. PB: Research funding support from the NIH, NSF, and Komen for the Cure; NIH funded collaboration with Barco, Inc. and Hologic, Inc.; Consultant to Delaware State Univ. and NCCPM, UK. AA: Employed at Barco Healthcare.; P. Bakic, NIH: (NIGMS P20 #GM103446, NCI R01 #CA154444); M. Das, NIH Research grants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Plate motion in sheared granular fault system

Plate motion near the fault gouge layer, and the elastic interplay between the gouge layer and the plate under stick-slip conditions, is key to understanding the dynamics of sheared granular fault systems. Here, a two-dimensional implementation of the combined finite-discrete element method (FDEM), which merges the finite element method (FEM) and the discrete element method (DEM), is used to explicitly simulate a sheared granular gouge fault system. In this work we focus on investigating the influence of normal load, driving shear velocity and plate stiffness on the velocities and displacements in the direction parallel to the shear direction (x-direction) measured at locations on the upper and lower plates just adjacent to the gouge. The simulations show that during slip phases the magnitudes of the measured velocities on the upper and lower plates are proportional to the normal load and may be inversely proportional to the square root of the plate's shear modulus. Whereas, the driving shear velocity does not show distinct influence on the measured velocities. Additionally, large slip velocities are generally associated with large macroscopic friction coefficient drops. For the models subjected to smaller normal loads, larger shear velocities and with stiffer shear plates, the same magnitude of slip velocity could cause a larger drop of macroscopic friction coefficient. During stick phases, the velocities of the upper and lower plates are respectively slightly greater and slightly smaller than half of the driving shear velocity and are both in the same direction of shear. The shear strain rate of the gouge is calculated from this velocity difference between the upper and lower plate during stick phases and thus the gouge effective shear modulus can be calculated. The results show that the gouge effective shear modulus increases proportionally with normal load, while the influence of shear velocity and plate stiffness on gouge effective shear modulus is minor. The simulations address the dynamics of a laboratory-scale fault gouge system and may aid in revealing the complexities of earthquake frictional dynamics.

58 GEOSCIENCES↗
DOE OSTI2022

Crowd Dynamics: Modeling and Control of Multiagent Systems

This review aims to present recent developments in modeling and control of multiagent systems. A particular focus is set on crowd dynamics characterized by complex interactions among agents, also called social interactions, and large-scale systems. Specifically, in a crowd each individual agent interacts with a field generated by the other agents and the environment. These systems can be modeled at the microscopic scale by ordinary differential equations, while an alternative description at the mesoscopic scale is given by a partial differential equation for the propagation of the probability density of the agents. Control actions can be applied at the individual level as well as at the level of the corresponding fields. This article presents and compares different control types, and the specific application to multilane, multiclass traffic is developed in some detail, showing the main tools at work in a hybrid setting with relevant impacts on autonomous driving.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Empirical Comparison of Machine Learning Approaches for Black-Box Modeling of Power Conversion System Dynamics

Inverter-based resources are key components in modern power systems, but accurately modeling their complex behavior can be challenging. Standard, generic converter models often oversimplify inverter dynamics, leading to significant errors in predicting performance. In this work, we compare several data-driven machine learning (ML) approaches for inverter modeling, performing experiments on power conversion systems, systematically varying input conditions, and recording the resulting voltages and currents. The ML models were then trained on this measured data to capture the inverter's dynamic response and to predict the inverter's output current. A performance comparison between the four ML models under study is conducted, laying the foundation for future work on hardware implementation for real-time inference.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future.

koopman operators, machine learning, deep learning↗
DOE OSTI2014

Coarse-grained molecular dynamics simulations of depletion-induced interactions for soft matter systems

Given the ubiquity of depletion effects in biological and other soft matter systems, it is desirable to have coarse-grained Molecular Dynamics (MD) simulation approaches appropriate for the study of complex systems. This paper examines the use of two common truncated Lennard-Jones (Weeks-Chandler-Andersen (WCA)) potentials to describe a pair of colloidal particles in a thermal bath of depletants. The shifted-WCA model is the steeper of the two repulsive potentials considered, while the combinatorial-WCA model is the softer. It is found that the depletion-induced well depth for the combinatorial-WCA model is significantly deeper than the shifted-WCA model because the resulting overlap of the colloids yields extra accessible volume for depletants. For both shifted- and combinatorial-WCA simulations, the second virial coefficients and pair potentials between colloids are demonstrated to be well approximated by the Morphometric Thermodynamics (MT) model. This agreement suggests that the presence of depletants can be accurately modelled in MD simulations by implicitly including them through simple, analytical MT forms for depletion-induced interactions. Although both WCA potentials are found to be effective generic coarse-grained simulation approaches for studying depletion effects in complicated soft matter systems, combinatorial-WCA is the more efficient approach as depletion effects are enhanced at lower depletant densities. The findings indicate that for soft matter systems that are better modelled by potentials with some compressibility, predictions from hard-sphere systems could greatly underestimate the magnitude of depletion effects at a given depletant density.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗