Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Physical 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

The Role of Self-Organized Criticality in the Substorm Phenomenon and its Relation to Localized Reconnection in the Plasma Sheet

Recent observations of the magnetotail plasma sheet have shown it to be a dynamic and turbulent region. Research has found strong turbulence in the plasma sheet at approximately 20 Earth's Radius tailward of Earth; the turbulence is observed at all activity levels. The existence of strong turbulence in the plasma sheet in the region associated with substorm onset might be thought difficult to reconcile with the coherence and repeatability of the substorm cycle. We review a variety of evidence that strongly suggests the magnetotail is driven, through magnetic flux transfer, into a state of "self-organized criticality" (SOC). It is an important property of physical systems that evolve into SOC that they self-organize into a unique, global dynamic state. This global state is inevitable, and repeatable. In this state, however, small-spatiotemporal-scale system fluctuations are unpredictable and can be only described statistically. This is the basis, we think, for the global coherence and repeatability of the substorm phenomenon in the turbulent plasma sheet. At, or near, substorm onset the plasma sheet can be described by a global SOC state containing significant small scale turbulence. In several recent studies, "sandpile" models were driven into SOC and then shown to reproduce various measures of substorm activity. We discuss the plasma physical foundation of these sandpile models. The evolution of simple continuum plasma sheet models into SOC-like states of many small reconnection events in the turbulent plasma sheet under the will be demonstrated. We view the substorm phenomenon as an avalanche assumption that the plasma sheet is in a SOC state.

Klimas, A. J.↗
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Developing new and understanding old approximations in TDDFT

When a system has evolved far from a ground-state, the adiabatic approximations commonly used in time-dependent density functional theory calculations completely fail in some applications, while giving qualitatively good predictions in others, and sometimes even quantitative predictions. It is not clearly understood why this is so, and developing practical approximations going beyond the adiabatic approximation remains a challenge. This paper explores three different lines of investigation. First, an expression for the exact time-dependent exchange–correlation potential suggests that the accuracy of an adiabatic approximation is intimately related to the deviation between the natural orbital occupation numbers of the physical system and those of the Kohn–Sham system, and we explore this on some exactly-solvable model systems. The exact expression further suggests a path to go beyond the adiabatic approximations, and in the second part we discuss a newly proposed class of memory-dependent approximations developed in this way. Finally, we derive a new expression for the exact exchange–correlation potential from a coupling-constant path integration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2022

Application of FARM to an IES scenario within the FORCE ecosystem

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON software module in the evaluation of the optimal dispatch by evaluating feasible set-points for the different IES unit components. Set-points are required to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). This problem is addressed by adopting a two-stage approach. First, the HERON power dispatcher determines set-points that meet the constraints on the former variables (e.g., power levels and power ramp rate limits). These constraints are called explicit constraints. Then, FARM adjusts these set-points to ensure the respect of the limits on the latter variables given the knowledge of the system physics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). The original version of the FARM software module (FARM-Alpha) was released by Argonne National Laboratory in January 2021. In the latest version of the code released in July 2022 (FARM-Delta), the Reference Governor (RG) algorithm was upgraded to a Multi-Input Multi-Output version from its original Single-Input Single-Output form. The RG algorithm acts to enforce constraints. With this improvement an IES unit is now treated as a single dynamic system from the standpoint of control. The crosstalk among components in an IES unit is now fully considered thereby ensuring a true optimization is obtained for those units that have multiple set-points. In this report, the capabilities of FARM-Delta operating within the FORCE ecosystem are demonstrated for an IES test case. The specific configuration of IES unit for this case was selected by the IES team with consultation from the Advanced Reactor IES Expert Group. A full TEA analysis that invoked HERON, HYBRID, FARM, and RAVEN was performed and serves to demonstrate how the latest modification to FARM algorithms (i.e., state variable selection, state-space matrices derivation, set-point verification) can shape setpoints that might otherwise compromise the health of equipment through accelerated wear and tear. In this specific test case, it was demonstrated that these algorithms ensure a more efficient utilization of steam resources to be shared by two different subsystems, namely Balance of Plant (BOP) and High-Temperature Steam Electrolysis (HTSE). Finally, some code improvements that can further enhance the user-friendliness are suggested.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Thermal Radiator Pointing for International Space Station

In order to provide thermal radiation environments that result in adequate beat rejection, the single-phase, liquid ammonia (NH3) heat rejection system on the International Space Station (ISS) requires that its two thermal radiator wings be dynamically rotated as the ISS travels through its orbit. This paper discusses the closed-loop, thermal radiator pointing system that is used on ISS to ensure adequate heat rejection by the radiators, while preventing freezing of the ammonia under low heat loads and cold-environmental conditions. Although initial designs used an open-loop approach for radiator pointing, concerns about performance robustness, algorithm complexity, memory requirements, and sustaining support drove the development of a more robust, simpler, closed-loop system. Hence, the challenge of the closed-loop system was to utilize existing sensors, actuators and computers to fit into the existing hardware and software architecture of the ISS. Using a proportional-integral (PI) control architecture with limited output and an anti-windup integrator, the temperature of the ammonia coming out of the radiator is measured and controlled by adjusting the radiator wing orientation. The radiator wing orientation for the local minimum environment is fed forward to the control system, and the closed-loop controller is used to generate a bias off of that local minimum environment in order to heat up the ammonia when necessary to avoid freezing. In the earth's shadow, the controller is suspended and the radiator wing is oriented to face the earth, the local maximum thermal environment which further prevents freezing of the ammonia. This control architecture is shown to provide adequate heat rejection and avoid freezing of the ammonia, even though the physical system consists of large transport delays and time-varying dynamics which change dramatically due to orbit motion and variable heat loads.

Green, Scott↗
DOE OSTI2026

Developing a Digital Twin for SRF Cavity Assembly at Fermilab

When assembling Superconducting Radio Frequency (SRF) Cavities, maintaining an environment devoid of particulates like dust and other small particles is essential. If a single spec of dust enters the cavity a significant degradation of performance can occur. To avoid a cavity failure Fermilab assembles the SRF cavities within a ISO-4 (Class 10) environment. This environment though is still susceptible to foreign contaminants when technicians enter and new components are added to the cleanroom. To reduce the risk even more Fermilab has introduced a cobot manipulator into the cleanroom environment to speed up the assembly time which will reduce the time that the technicians operate in the cleanroom. But, this still leaves the potential of contaminants to enter the cleanroom if new components need to be tested within the cleanroom. This project aims to lay the groundwork to develop a Digital Twin environment of the cleanroom to aid in manufacturing processes and testing. In its simplest form, a digital twin is a bidirectional link between a physical system and its digital counterpart or twin. NVIDIA Isaac Sim is used as the digital twin foundation for the digital representation of the cleanroom, specifically for the UR16e assembly area. A simulated UR16e was used to validate the performance of Isaac Sim as a testing environment by comparing the tool center points (TCP) positional data between the simulated and digital representation of the UR16e. Due to a new vision based robotic assembly process being introduced to the cleanroom a digital representation of the physical camera was tested and validated to ensure that it will produce close to the same outcome as the physical environment. The TCP comparison results showed a peak translational error of approximately 0.1mm and rotational errors of up to 8 between the simulated and digital UR16es. While the camera validation performed with high repeatability across multiple runs, it still requires minor tuning before it can accurately replicate a physical camera.

Imburgia, Joseph [Northern Illinois U.]↗
NASA NTRS2004

MEANS User Documentation

MEANS is an acronym for the MIT Extensible Air Network Simulation. MIT is the Massachusetts Institute of Technology, where MEANS was developed. MEANS models the flow of aircraft in a simulation of the US National Airspace System. It can optionally follow the flow of passengers as well. The flight model takes the form of several queues through which the aircraft pass, as well as delays at certain times; the passengers can be transported via flights. MEANS is an event-based model. This means that events are scheduled at some point in the future, and once an event has executed, the simulation t h e advances to that of the next event. This is a different approach than some simulations (particularly those of physical systems), where time is advanced in fixed increments and the state of the system is updated at each step.This documentation is a "user guide" for MEANS - it describes how to USE means, not write new modules. For programming instructions, see the programming documentation, available as a separate document.This document assumse no programming knowledge. It will describe how to run MEANS, understand the data formats, and use such utility programs as already exist. For more detailed information on the internal workings of MEANS, and how to programmatically add to it, refer to the programming documentation.

Melconian, Terran↗
DOE OSTI2021

Gradient sensing via cell communication

Experimental evidence lends support to the conjecture that cell-to-cell communication plays a role in the gradient sensing of chemical species by certain chains of cells. Models have been formulated to explore this idea. For cells with no identifiable sensing structure, Mugler et al. [Proc. Natl. Acad. Sci. (U.S.A.) 113, E689 (2016)] have defined a particular local excitation, global inhibition (LEGI) model that pits nearest-neighbor communication against local reactions in a noisy environment to suggest how this sensing capability might arise in a physical system. In this study, we generalize the nearest-neighbor communication mechanism in the aforementioned LEGI model in order to explore the extent to which the gradient sensing characteristics depend on the parametrization of the communication itself, as well as on the cell size, the radius of influence of neighboring cells, and the influence of the background noise. Using our generalization and a collection of particular candidate communication models, we find that the precision of gradient sensing is indeed sensitive to the particular communication model, and we derive physical and analytic explanations for these results. The framework established and the associated results should prove useful in understanding the appropriateness of particular cell-to-cell communication models in gradient sensing studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Data driven learning of robust nonlocal models

Nonlocal models use integral operators that embed length-scales in their definition. However, the integrands in these operators are difficult to define from the data that are typically available for a given physical system, such as laboratory mechanical property tests. In contrast, molecular dynamics (MD) does not require these integrands, but it suffers from computational limitations in the length and time scales it can address. To combine the strengths of both methods and to obtain a coarse-grained, homogenized continuum model that efficiently and accurately captures materials' behavior, we propose a learning framework to extract, from MD data, an optimal nonlocal model as a surrogate for MD displacements. Our framework guarantees that the resulting model is mathematically well-posed, physically consistent, and that it generalizes well to settings that are different from the ones used during training. The efficacy of this approach is demonstrated with several numerical tests for single layer graphene both in the case of perfect crystal and in the presence of thermal noise.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

On applications of quantum computing to plasma simulations

Quantum computing is gaining increased attention as a potential way to speed up simulations of physical systems, and it is also of interest to apply it to simulations of classical plasmas. However, quantum information science is traditionally aimed at modeling linear Hamiltonian systems of a particular form that is found in quantum mechanics, so extending the existing results to plasma applications remains a challenge. Here, we report a preliminary exploration of the long-term opportunities and likely obstacles in this area. First, we show that many plasma-wave problems are naturally representable in a quantumlike form and thus are naturally fit for quantum computers. Second, we consider more general plasma problems that include non-Hermitian dynamics (instabilities, irreversible dissipation) and nonlinearities. We show that by extending the configuration space, such systems can also be represented in a quantumlike form and thus can be simulated with quantum computers too, albeit that requires more computational resources compared to the first case. Third, we outline potential applications of hybrid quantum–classical computers, which include analysis of global eigenmodes and also an alternative approach to nonlinear simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Modeling and Diagnostic Software for Liquefying-Fuel Rockets

A report presents a study of five modeling and diagnostic computer programs considered for use in an integrated vehicle health management (IVHM) system during testing of liquefying-fuel hybrid rocket engines in the Hybrid Combustion Facility (HCF) at NASA Ames Research Center. Three of the programs -- TEAMS, L2, and RODON -- are model-based reasoning (or diagnostic) programs. The other two programs -- ICS and IMS -- do not attempt to isolate the causes of failures but can be used for detecting faults. In the study, qualitative models (in TEAMS and L2) and quantitative models (in RODON) having varying scope and completeness were created. Each of the models captured the structure and behavior of the HCF as a physical system. It was noted that in the cases of the qualitative models, the temporal aspects of the behavior of the HCF and the abstraction of sensor data are handled outside of the models, and it is necessary to develop additional code for this purpose. A need for additional code was also noted in the case of the quantitative model, though the amount of development effort needed was found to be less than that for the qualitative models.

Poll, Scott↗
DOE OSTI2023

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
NASA NTRS2012

Model-Based Verification and Validation of Spacecraft Avionics

Verification and Validation (V&V) at JPL is traditionally performed on flight or flight-like hardware running flight software. For some time, the complexity of avionics has increased exponentially while the time allocated for system integration and associated V&V testing has remained fixed. There is an increasing need to perform comprehensive system level V&V using modeling and simulation, and to use scarce hardware testing time to validate models; the norm for thermal and structural V&V for some time. Our approach extends model-based V&V to electronics and software through functional and structural models implemented in SysML. We develop component models of electronics and software that are validated by comparison with test results from actual equipment. The models are then simulated enabling a more complete set of test cases than possible on flight hardware. SysML simulations provide access and control of internal nodes that may not be available in physical systems. This is particularly helpful in testing fault protection behaviors when injecting faults is either not possible or potentially damaging to the hardware. We can also model both hardware and software behaviors in SysML, which allows us to simulate hardware and software interactions. With an integrated model and simulation capability we can evaluate the hardware and software interactions and identify problems sooner. The primary missing piece is validating SysML model correctness against hardware; this experiment demonstrated such an approach is possible.

MBV&V↗