Simultaneous control of temperature and humidity in a confined space. I - Mathematical modeling of the dynamic behavior of temperature and humidity in a confined space.
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Nakanishi, E.↗
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Results for “behavioral modeling”
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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Elastic model of the traction behavior of two traction lubricants
In the analysis of rolling-sliding concentrated contacts, such as gears, bearings and traction drives, the traction characteristics of the lubricant are of prime importance. The elastic shear modulus and limiting shear stress properties of the lubricant dictate the traction/slip characteristics and power loss associated with an EHD contact undergoing slip and/or spin. These properties can be deducted directly from the initial slope m and maximum traction coefficient micron of an experimental traction curve. In this investigation, correlation equations are presented to predict m and micron for two modern traction fluids based on the regression analysis of 334 separate traction disk machine experiments. The effects of contact pressure, temperature, surface velocity, ellipticity ratio are examined. Problems in deducing lubricant shear moduli from disk machine tests are discussed. Previously announced in STAR as N83-20116
Loewenthal, S. H.↗
Experimental, physical and numerical modeling of lunar regolith and lunar regolith structures
An extensive series of laboratory strength and deformation experiments have been performed on a lunar regolith simulant. Results of these experiments are compared to results from experiments on real lunar regolith from the Apollo and Luna missions to illustrate the suitability of this material in capturing the engineering properties lunar regolith. In addition, these results are used to calibrate a constitutive model used to describe its stress-strain behavior. This model, in conjunction with numerical analysis techniques, is used to predict the response i.e. material parameters) of lunar simulant under 1/6-g and low confining stress conditions. These tools are also used to predict the displacement response of a lunar soil embankment structure used to cover a first generation human habitat module, which might be used to accommodate the first astronauts revisiting the moon. These predictions are compared to physical models of this structure, which are tested in a geotechnical centrifuge in order to satisfy scaling relationships between prototype and model.
Perkins, Steven W.↗
Attendee Response to Guiding Questions of the September 2014 DOE Workshop on North Slope of Alaska Priorities
We advocate for several modeling approaches suitable to prove understanding of and capability to model aerosol behavior in nucleating droplets and ice and to model regional cloud systems at the mesoscale. The full complement of data that is ideal for particular study types is increasingly well understood; the ASR ISDAC Science Plan provides an excellent example of listing the data required for each study type targeted by that campaign. Whereas discussion here focused on the microscale and mesoscale, other studies should obviously focus on the regional, seasonal and global scale. When considering ice microphysics alone, laser focus on unconstrained model components could be usefully intensified (e.g., collision-coalescence kernels). Similarly, a focus on boundary layer structure would be deeply valuable to a wide range of model types. The expense of observations motivates efficient planning for observational data sets to support specific study approaches; the concept of closure is often useful (e.g., the surface energy budget).
nucleation↗
Modeling Pulsed Magnetic Core Behavior in LTSpice
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Surrogate Models for Analyzing Performance Behavior of HPC Applications Using the RAJA Performance Suite
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Computer science↗
Multiscale modeling of twinning and detwinning behavior of HCP polycrystals
A multiscale scheme is presented here to capture the twinning and detwinning mechanisms during cyclic loading of HCP polycrystals. In the current framework, the crystal plasticity finite element (CPFE) method is employed at the microstructural scale. The response of twinned regions at a material point is calculated using a novel homogenization scheme via a Taylor-type model. The homogenized behavior is then used in the CPFE to obtain the response of a polycrystal. An efficient rate-independent crystal plasticity model that has been incorporated into the open source PRISMS-Plasticity software is introduced here to capture the response of the twinned region. Accordingly, the corresponding algorithmic tangent modulus is derived. Finally, this multiscale scheme is validated against data available for the cyclic response of Mg alloy ZK60A. The results show that the multiscale model can capture the key elements of the sample response observed during the experiment.
36 MATERIALS SCIENCE↗
Relaxation processes in a low-order three-dimensional magnetohydrodynamics model
The time asymptotic behavior of a Galerkin model of 3D magnetohydrodynamics (MHD) has been interpreted using the selective decay and dynamic alignment relaxation theories. A large number of simulations has been performed that scan a parameter space defined by the rugged ideal invariants, including energy, cross helicity, and magnetic helicity. It is concluded that time asymptotic state can be interpreted as a relaxation to minimum energy. A simple decay model, based on absolute equilibrium theory, is found to predict a mapping of initial onto time asymptotic states, and to accurately describe the long time behavior of the runs when magnetic helicity is present. Attention is also given to two processes, operating on time scales shorter than selective decay and dynamic alignment, in which the ratio of kinetic to magnetic energy relaxes to values 0(1). The faster of the two processes takes states initially dominant in magnetic energy to a state of near-equipartition between kinetic and magnetic energy through power law growth of kinetic energy. The other process takes states initially dominant in kinetic energy to the near-equipartitioned state through exponential growth of magnetic energy.
Stribling, Troy↗
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior
The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.
54 ENVIRONMENTAL SCIENCES↗
A heuristic finite-state model of the human driver in a car-following situation
An approach to modeling human driver behavior in single-lane car following which is based on a finite-state decision structure is considered. The specific strategy at each point in the decision tree was obtained from observations of typical driver behavior. The synthesis of the decision logic is based on position and velocity thresholds and four states defined by regions in the phase plane. The performance of the resulting assumed intuitively logical model was compared with actual freeway data. The match of the model to the data was optimized by adapting the model parameters using a modified PARTAN algorithm. The results indicate that the heuristic model behavior matches actual car-following performance better during deceleration and constant velocity phases than during acceleration periods.
Burnham, G. O.↗
Reducing software mass through behavior control
Attention is given to the tradeoff between communication and computation as regards a planetary rover (both these subsystems are very power-intensive, and both can be the major driver of the rover's power subsystem, and therefore the minimum mass and size of the rover). Software techniques that can be used to reduce the requirements on both communciation and computation, allowing the overall robot mass to be greatly reduced, are discussed. Novel approaches to autonomous control, called behavior control, employ an entirely different approach, and for many tasks will yield a similar or superior level of autonomy to traditional control techniques, while greatly reducing the computational demand. Traditional systems have several expensive processes that operate serially, while behavior techniques employ robot capabilities that run in parallel. Traditional systems make extensive world models, while behavior control systems use minimal world models or none at all.
Miller, David P.↗
Long Term Degradation of Polyimide Composites
The durability of polymer matrix composites exposed to harsh environments is a major concern. Surface degradation and damage are observed in polyimide composites used in air at 125-300 C. It is believed that diffusion of oxygen into the material and oxidative chemical reactions are responsible. Previous work has characterized and modeled diffusion behavior, and thermogravimetric analyses (TGAS) have been carried out in nitrogen, air, and oxygen to provide quantitative information on thermal and oxidative reactions. However, the model developed using these data did not successfully extrapolate TGA data down to conditions seen in service. A test program that focuses on lower temperatures and makes use of isothermal tests was undertaken to achieve a better understanding of the degradation reactions under use conditions. A new, low-cost technique was developed to collect chemical degradation data for isothermal tests lasting over 200 hours in the temperature range 125-300 C. Results indicate complex behavior not captured by the previous model, including the presence of weight-adding reactions. Weight gain reactions dominated in the 125-225 C temperature range, while weight loss reactions dominated beyond 225 C. The data obtained from isothermal tests and earlier TGAs is used to develop an advanced model of the material behavior.
Patekar, Kaustubh A.↗
Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning
Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.
Microscopy↗
Ion temperature anisotropy in the tokamak scrape-off layer
Understanding tokamak exhaust-power heat loads on divertor plates depends critically on having a realistic model of the scrape-off layer (SOL) plasma. The Braginskii fluid model is often solved to understand the SOL plasma behavior. This model is based on the collisional limit for transport along the magnetic field B → . The ions and electron gyrofrequencies are assumed to be much larger than the Coulomb collision frequencies, which are nonetheless, sufficiently large to yield common parallel and perpendicular temperatures for each species, i.e. the temperatures are assumed to be isotropic. Additionally, in certain circumstances such as encountered for the tokamak H-mode, the ion temperature can be quite anisotropic. In this work, the anisotropy effects are implemented in the two-dimensional (2D) transport code UEDGE. Various geometries (1D slab, 2D slab and a toroidal tokamak geometry) are used to study the 2D structure of ion temperature anisotropy and its effects on plasma transport in detail. Results show that the effects of ion temperature anisotropy on the plasma parallel transport are substantial near the magnetic X-point, which leads to different steady state density profiles in the divertor regions. The extra mirror force introduced by ion temperature anisotropy can be one of the main forces contributing to the plasma flow in the SOL.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
SRF Cavity Instability Detection with Machine Learning at CEBAF
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
Carpenter, A.↗
SRF Cavity Instability Detection with Machine Learning at CEBAF
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
Carpenter, A.↗
Magnetic flux relaxation in YBa2Cu3O(7-x) thin film: Thermal or athermal
The magnetic flux relaxation behavior of YBa2Cu3O(7-x) thin film on LaAlO3 for H parallel c was studied in the range of 4.2-40 k and 0.2-1.0 T. Both the normalized flux relaxation rate (S) and the net flux pinning energy (U) increase continuously from 1.3 x 10 exp -2 to 3.0 x 10 exp -2 and from 70-240 meV respectively, as the temperature (T) increases from 10 to 40 K. This behavior is consistent with the thermally activated flux motion model. At low temperatures, however, S is found to decrease much more slowly as compared with kT, in contradiction to the thermal activation model. This behavior is discussed in terms of the athermal quantum tunneling of flux lines. The magnetic field dependence of U, however, is not completely understood.
Vitta, Satish↗
Magnetic flux relaxation in YBa2Cu3)(7-x) thin film: Thermal or athermal
The magnetic flux relaxation behavior of YBa2Cu3O(7-x) thin film on LaAlO3 for H is parallel to c was studied in the range 4.2 - 40 K and 0.2 - 1.0 T. Both the normalized flux relaxation rate S and the net flux pinning energy U increase continuously from 1.3 x 10(exp -2) to 3.0 x 10(exp -2) and from 70 to 240 meV respectively, as the temperature T increases from 10 to 40 K. This behavior is consistent with the thermally activated flux motion model. At low temperatures, however, S is found to decrease much more slowly as compared with kT, in contradiction to the thermal activation model. This behavior is discussed in terms of the athermal quantum tunneling of flux lines. The magnetic field dependence of U, however, is not completely understood.
Vitta, Satish↗