SEARCH · Engineering Papers

Results for “Physics 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.

SourceResearch connectionFrom yearThrough year

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

At least 1,027 records · Page 57

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Tidal Love numbers of novel and admixed celestial objects

A sub-fraction of dark matter or new particles trapped inside celestial objects can significantly alter their macroscopic properties. Here, we investigate the new physics imprint on celestial objects by using a generic framework to solve the Tolman-Oppenheimer-Volkoff (TOV) equations for up to two fluids. We test the impact of populations of new particles on celestial objects, including the sensitivity to self-interaction sizes, new particle mass, and net population mass. Applying our setup to neutron stars and boson stars, we find rich phenomenology for a range of these parameters, including the creation of extended atmospheres. These atmospheres are detectable by their impact on the tidal love number, which can be measured at upcoming gravitational wave experiments such as Advanced LIGO, the Einstein Telescope, and LISA. We release our calculation framework as a publicly available code at this URL, allowing the TOV equations to be generically solved for arbitrary new physics models in novel and admixed celestial objects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2010

Development of Crystal-Tolerant High-Level Waste Glasses

Twenty five glasses were formulated. They were batched from HLW AZ-101 simulant or raw chemicals and melted and tested with a series of tests to elucidate the effect of spinel-forming components (Ni, Fe, Cr, Mn, and Zn), Al, and noble metals (Rh2O3 and RuO2) on the accumulation rate of spinel crystals in the glass discharge riser of the high-level waste (HLW) melter. In addition, the processing properties of glasses, such as the viscosity and TL, were measured as a function of temperature and composition. Furthermore, the settling of spinel crystals in transparent low-viscosity fluids was studied at room temperature to access the shape factor and hindered settling coefficient of spinel crystals in the Stokes equation. The experimental results suggest that Ni is the most troublesome component of all the studied spinel-forming components producing settling layers of up to 10.5 mm in just 20 days in Ni-rich glasses if noble metals or a higher concentration of Fe was not introduced in the glass. The layer of this thickness can potentially plug the bottom of the riser, preventing glass from being discharged from the melter. The noble metals, Fe, and Al were the components that significantly slowed down or stopped the accumulation of spinel at the bottom. Particles of Rh2O3 and RuO2, hematite and nepheline, acted as nucleation sites significantly increasing the number of crystals and therefore decreasing the average crystal size. The settling rate of ≤10-μm crystal size around the settling velocity of crystals was too low to produce thick layers. The experimental data for the thickness of settled layers in the glasses prepared from AZ-101 simulant were used to build a linear empirical model that can predict crystal accumulation in the riser of the melter as a function of concentration of spinel-forming components in glass. The developed model predicts the thicknesses of accumulated layers quite well, R2 = 0.985, and can be become an efficient tool for the formulation of the crystal-tolerant HLW glasses for higher waste loading. A physical modeling effort revealed that the Stokes and Richardson-Zaki equations can be used to adequately predict the accumulation rate of spinel crystals of different sizes and concentrations in the glass discharge riser of HLW melters. The determined shape factor for the glass beads was only 0.73% lower than the theoretical shape factor for a perfect sphere. The shape factor for the spinel crystals matched the theoretically predicted value to within 10% and was smaller than that of the beads, given the larger drag force caused by the larger surface area-to-volume ratio of the octahedral crystals. In the hindered settling experiments, both the glass bead and spinel suspensions were found to follow the predictions of the Richardson-Zaki equation with the exponent n = 3.6 and 2.9 for glass beads and spinel crystals, respectively.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

DOE OSTI2011

Uncertainty quantification of US Southwest climate from IPCC projections.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) made extensive use of coordinated simulations by 18 international modeling groups using a variety of coupled general circulation models (GCMs) with different numerics, algorithms, resolutions, physics models, and parameterizations. These simulations span the 20th century and provide forecasts for various carbon emissions scenarios in the 21st century. All the output from this panoply of models is made available to researchers on an archive maintained by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) at LLNL. I have downloaded this data and completed the first steps toward a statistical analysis of these ensembles for the US Southwest. This constitutes the final report for a late start LDRD project. Complete analysis will be the subject of a forthcoming report.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

Systems Health Managent↗

DOE OSTI2013

Exploiting Third Generation Quarks for New Physics Discoveries at the Energy Frontier

The K-State team under PI’s supervision includes a graduate student Mikhail Makouski (supported by this grant), and a post-doc Sadia Khalil (not supported by this grant). The group’s work at CMS during the reported period has resulted in three publications, plus one public physics analysis summary (CMS PAS), which is being prepared for submission to a journal. The K-State group’s effort is top quark physics and searches for beyond-standard-model physics in $t\overline{t}$ final states. The KSU team performed the most precise measurement of the $t\overline{t}$ cross section in the lepton + jets channel, and for the first time excluded the fourth generation of the standard model in the perturbative regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Stripe order in the underdoped region of the two-dimensional Hubbard model

Competing inhomogeneous orders are a central feature of correlated electron materials, including the high-temperature superconductors. The two-dimensional Hubbard model serves as the canonical microscopic physical model for such systems. Multiple orders have been proposed in the underdoped part of the phase diagram, which corresponds to a regime of maximum numerical difficulty. By combining the latest numerical methods in exhaustive simulations, we uncover the ordering in the underdoped ground state. We find a stripe order that has a highly compressible wavelength on an energy scale of a few kelvin, with wavelength fluctuations coupled to pairing order. The favored filled stripe order is different from that seen in real materials. Our results demonstrate the power of modern numerical methods to solve microscopic models, even in challenging settings.

Zheng, Bo-Xiao↗

DOE OSTI2010

Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.

Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters. A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Lattice Quantum Chromodynamics: SPI, mapping, site ordering, and QPX in Lattice QCD code on Mira (ALCF-2 Early Science Program Technical Report)

Lattice QCD is a numerical method to simulate QCD (Quantum Chromodynamics) including non-perturbative effects. Among other methods, lattice QCD is the only successful non-perturbative method that can be systematically improved from first principles. Lattice QCD plays an important role in High Energy Particle Physics (flavor physics, spectroscopy, and beyond the Standard Model physics) and Nuclear Physics (nucleon/nuclear spectrum & structure, quark-gluon plasma, and the QCD equation of state). Moreover, recently there are active developments in applying lattice gauge theory to quantum field theory in general, for example the theoretical description of graphene or cold atom systems. We define the QCD action on a finite and discrete ‘lattice’ like grid, so that we can compute the path integral (partition function) on a computer. A current lattice size is 96 3 ×192, and the corresponding number of degrees of freedom for the path integral is about 6×10 8 . Thus, it requires enormous computing resources. In this report, we describe our overall efforts to improve lattice QCD software on Mira, which include a new communication library using the Message Unit (MU) SPI (System Programming Interface), better node mapping strategy, lattice site reordering, and using the quad floating point (QPX) intrinsics.

Na, Heechang↗

DOE OSTI2015

V&V Framework

A Verification and Validation (V&V) framework is presented for the development and execution of coordinated modeling and experimental program s to assess the predictive capability of computational models of complex systems through focused, well structured, and formal processes. The elements of the framework are based on established V&V methodology developed by various organizations including the Department of Energy, National Aeronautics and Space Administration, the American Institute of Aeronautics and Astronautics, and the American Society of Mechanical Engineers. Four main topics are addressed: 1) Program planning based on expert elicitation of the modeling physics requirements, 2) experimental design for model assessment, 3) uncertainty quantification for experimental observations and computational model simulations, and 4) assessment of the model predictive capability. The audience for this document includes program planners, modelers, experimentalist, V &V specialist, and customers of the modeling results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Fast Running Urban Dispersion Model for Radiological Dispersal Device (RDD) Releases: Model Description and Validation

Aeolus is an efficient three-dimensional computational fluid dynamics code based on finite volume method developed for predicting transport and dispersion of contaminants in a complex urban area. It solves the time dependent incompressible Navier-Stokes equation on a regular Cartesian staggered grid using a fractional step method. It also solves a scalar transport equation for temperature and using the Boussinesq approximation. The model also includes a Lagrangian dispersion model for predicting the transport and dispersion of atmospheric contaminants. The model can be run in an efficient Reynolds Average Navier-Stokes (RANS) mode with a run time of several minutes, or a more detailed Large Eddy Simulation (LES) mode with run time of hours for a typical simulation. This report describes the model components, including details on the physics models used in the code, as well as several model validation efforts. Aeolus wind and dispersion predictions are compared to field data from the Joint Urban Field Trials 2003 conducted in Oklahoma City (Allwine et al 2004) including both continuous and instantaneous releases. Newly implemented Aeolus capabilities include a decay chain model and an explosive Radiological Dispersal Device (RDD) source term; these capabilities are described. Aeolus predictions using the buoyant explosive RDD source are validated against two experimental data sets: the Green Field explosive cloud rise experiments conducted in Israel (Sharon et al 2012) and the Full-Scale RDD Field Trials conducted in Canada (Green et al 2016).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Developing a System for Testing Computational Social Models using Amazon Mechanical Turk

The US faces persistent, distributed threats from malevolent individuals, groups and organizations around the world. Computational Social Models (CSMs) help anticipate the dynamics and behaviors of these actors by modeling the behavior and interactions of individuals, groups and organizations. For strategic planners to trust the results of CSMs, they must have confidence in the validity of the models. Establishing validity before model use will enhance confidence and reduce the risk of error. One problem with validation is designing an appropriate controlled test of the model, similar to the testing of physical models. Lab experiments can do this, but are often limited to small numbers of subjects, with low subject diversity and are often in a contrived environment. Natural studies attempt to test models by gathering large-scale observational data (e.g., social media) however this loses the controlled aspect. We propose a new approach to run large-scale, controlled online experiments on diverse populations. Using Amazon Mechanical Turk, a crowdsourcing tool, we will draw large populations into controlled experiments in a manner that was not possible just a few years ago.

42 ENGINEERING↗

DOE OSTI2024

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Iyegbekedo, Ikponmwosa↗

DOE OSTI2016

The impact of non-uniform thermal structure on the interpretation of exoplanet emission spectra

The determination of atmospheric structure and molecular abundances of planetary atmospheres via spectroscopy involves direct comparisons between models and data. While varying in sophistication, most model spectra comparisons fundamentally assume one-dimensional (1D) model physics. However, knowledge from general circulation models and of solar system planets suggests that planetary atmospheres are inherently three-dimensional in their structure and composition. We explore the potential biases resulting from standard “1D” assumptions within a Bayesian atmospheric retrieval framework. Specifically, we show how the assumption of a single 1D thermal profile can bias our interpretation of the thermal emission spectrum of a hot Jupiter atmosphere that is composed of two thermal profiles. We retrieve spectra of unresolved model planets as observed with a combination of the Hubble Space Telescope Wide Field Camera 3 (WFC3)+Spitzer Infrared Array Camera (IRAC) as well as the James Webb Space Telescope (JWST) under varying differences in the two thermal profiles. For WFC3+IRAC, there is a significantly biased estimate of CH{sub 4} abundance using a 1D model when the contrast is 80%. For JWST, two thermal profiles are required to adequately interpret the data and estimate the abundances when contrast is greater than 40%. We also apply this preliminary concept to the recent WFC3+IRAC phase curve data of the hot Jupiter WASP-43b. We see similar behavior as present in our simulated data: while the H{sub 2}O abundance determination is robust, CH{sub 4} is artificially well-constrained to incorrect values under the 1D assumption. Our work demonstrates the need to evaluate model assumptions in order to extract meaningful constraints from atmospheric spectra and motivates exploration of optimal observational setups.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

Modeling of the surface static displacements and fault plane slip for the 1979 Imperial Valley earthquake

Three-dimensional finite element modeling techniques are used to synthesize geodetic and seismological results for 1979 Imperial Valley earthquake. The strategy pursued consists of two principal steps. In the first step, the seismologically-derived coseismic fault slip is taken as a function of position in the fault plane and is applied directly to the three-dimensional dislocation model. In the second step, a physical model of stresses and constitutive parameters is perturbed so as to reproduce the observed fault slip. Hence, the principal features of the coseismic slip pattern are explained by a stress-driven fault model in which: (1) a spatially unresolved asperity is found equivalent to a stress drop of 18 MPa averaged over an area of 15 sq km, and (2) driving stress is essentially absent on the fault segment overlapping the 1940 earthquake rupture zone.

Slade, M. A.↗

NASA NTRS1987

Composite load spectra for select space propulsion structural components

The objective of this program is to develop generic load models to simulate the composite load spectra (CLS) that are induced in space propulsion system components representative of the space shuttle main engines (SSME). These models are being developed through describing individual component loads with an appropriate mix of deterministic and state-of-the-art probabilistic models that are related to key generic variables. Combinations of the individual loads are used to synthesize the composite loads spectra. A second approach for developing the composite loads spectra load model simulation, the option portion of the contract will develop coupled models which combine the individual load models. Statistically varying coefficients of the physical models will be used to obtain the composite load spectra.

Newell, J. F.↗

NASA NTRS1989

Computing viscous reactive flow over blunt winged vehicles

A hypersonic flow simulation methodology is developed for analyzing thermochemical nonequilibrium and viscous phenomena associated with space-flight vehicles at realistic conditions. The computer code consists of grid generation, Navier-Stokes and species models, and equation solvers for a complete configuration at arbitrary angles of attack. Provisions are implemented in the code to consider chemically frozen or equilibrium chemistry, inviscid model, conic configurations and simplifications of physical parameters for verifying solution consistency and accuracy. Implications of physical modeling and input parameters on a blunt-cone flow are investigated in details. Applications to USA Orbiter-like vehicles have emphasized the leeside canopy flow as well as the bow-wing shock interaction problem at Mach 22 and 40 deg angle of attack. Comparison of windward centerline pressures and heating rates with available wind-tunnel and flight data is in progress.

Li, C. P.↗