Search NASA⌕ Search

SEARCH · Search NASA

Results for “Complex 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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Magnetohydrodynamics with physics informed neural operators

Abstract The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with the TensorLY package. Our results indicate that PINOs can accurately capture the physics of MHD simulations that describe laminar flows with Reynolds numbers Re ⩽ 250 . We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of MHD simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Cost function for low-dimensional manifold topology assessment

Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

42 ENGINEERING↗
DOE OSTI2025

Building confidence in models for complex barrier systems for radionuclides

The modeling and simulation of the Cement-clay Interaction-Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include "skin effects" developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.

Sarsenbayev, Dauren↗
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2024

Quantitative Predictive Theories through Integrating Quantum, Statistical, Equilibrium, and Nonequilibrium Thermodynamics

Today's thermodynamics is largely based on the combined law for equilibrium systems and statistical mechanics derived by Gibbs in 1873 and 1901, respectively, while irreversible thermodynamics for nonequilibrium systems resides essentially on the Onsager Theorem as a separate branch of thermodynamics developed in 1930s. Between them, quantum mechanics was invented and was quantitatively solved in terms of density functional theory (DFT) in 1960s. Furthermore, these three scientific domains operate based on different principles and are very much separated from each other. In analogy to the parable of the blind men and the elephant articulated by Perdew, they individually represent different portions of a complex system and thus are incomplete by themselves alone, resulting in the lack of quantitative agreement between their predictions and experimental observations. Over the last two decades, the author's group has developed a multiscale entropy approach (recently termed as zentropy theory) that integrates DFT-based quantum mechanics and Gibbs statistical mechanics and is capable of accurately predicting entropy and free energy of complex systems. Furthermore, in combination with the combined law for nonequilibrium systems developed by Hillert, the author developed the theory of cross phenomena beyond the phenomenological Onsager Theorem. The zentropy theory and theory of cross phenomena jointly provide quantitative predictive theories for systems from electronic to any observable scales as reviewed in the present work.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

R ( 3780 ) Resonance Interpreted as the 1 D 1 3 -Wave Dominant State of Charmonium from Precise Measurements of the Cross Section of e + e − → Hadrons

We report the precise measurements of the cross section of e + e − → hadrons at center-of-mass energies from 3.645 to 3.871 GeV. We thereby perform the most precise study of the cross sections and find a complex system composed of three resonances of R ( 3760 ) , R ( 3780 ) , and R ( 3810 ) . For the first time, we measure the R ( 3810 ) electronic width to be ( 19.4 ± 7.4 ± 12.1 ) eV . For the R ( 3760 ) resonance, we measure the mass to be ( 3751.9 ± 3.8 ± 2.8 ) MeV / c 2 , the total width to be ( 32.8 ± 5.8 ± 8.7 ) MeV , and the electronic width to be ( 184 ± 75 ± 86 ) eV . For the R ( 3780 ) resonance, we measure its mass to be ( 3778.7 ± 0.5 ± 0.3 ) MeV / c 2 , total width to be ( 20.3 ± 0.8 ± 1.7 ) MeV , and electronic width to be ( 265 ± 67 ± 83 ) eV . Forty-seven years ago, the ψ ( 3770 ) resonance was discovered, and was subsequently interpreted as the 1 D 1 3 -wave dominant state of charmonium. However, our analysis of the total-hadron cross sections indicates that the ψ ( 3770 ) is not a single state, but a complex system composed of the R ( 3760 ) , R ( 3780 ) , and R ( 3810 ) resonances. Among these, we interpret the R ( 3780 ) is a resonance dominated by the 1 D 1 3 charmonium state. Published by the American Physical Society 2024

Ablikim, M.↗
DOE OSTI2021

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self-assembled heterogeneous monolayer films of doped-semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine-learning-based analyses of hyperspectral EELS datasets are demonstrated. Additionally, in the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non-linear autoencoders are used to create a highly-reduced latent-space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-space representation of the quasiparticle self-consistent GW self-energy and its application to defect calculations

The quasiparticle self-consistent (QS) GW (G for Green's function, W for screened Coulomb interaction) approach incorporates the corrections of the quasiparticle energies from their Kohn-Sham density functional theory (DFT) eigenvalues by means of an energy-independent and Hermitian self-energy matrix usually given in the basis set of the DFT eigenstates. By expanding these into an atom-centered basis set (specifically here the linearized muffin-tin orbitals) a real space representation of the self-energy corrections becomes possible. In this work, We show that this representation is relatively short-ranged. This offers opportunities to construct the self-energy of a complex system from parts of the system by a cut-and-paste method. Specifically for a point defect, represented in a large supercell, the self-energy can be constructed from those of the host and a smaller defect-containing cell. The self-energy of the periodic host can be constructed simply from a GW calculation for the primitive cell. We show for the case of the As Ga in GaAs that the defect part can already be well represented by a minimal eight-atom cell and allows us to construct the self-energy for a 64-atom cell in good agreement with direct QSGW calculations for the large cell. Using this approach to an even larger 216-atom cell shows the defect band approaches an isolated defect level. The calculations also allow us to identify a second defect band which appears as a resonance near the conduction band minimum. The results on the extracted defect levels agree well with Green's function calculations for an isolated defect and with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2020

Experimental demonstration and performance evaluation of a complex fenestration system for daylighting and thermal harvesting

Two significant design strategies for mitigating building energy consumption are daylight redirection and solar energy harvesting. Effective daylighting implementation enhances the amount of useful natural light within a space and offsets the need for electric lighting. Solar energy harvesting systems can mitigate energy costs from mechanical systems by managing incoming thermal loads or capturing solar energy that can be used to supplement thermal systems in the building. While there are many available façade-based technologies that can perform daylighting or solar thermal energy harvesting, there remains a limitation in available systems that can perform both of them simultaneously. This paper, describes the design and experimental demonstration of a Liquid Filled Prismatic Louver (LFPL) system, which combines both energy-saving strategies. The LFPL system was installed in a south-facing room in New York City and evaluated for indoor daylight penetration and potential for thermal energy harvesting. Daylight redirection is achieved through the prismatic louver geometry and proper orientation, whereas thermal energy harvesting is achieved through IR absorption from the liquid (e.g., water) within the prisms. Daylighting performance was evaluated by illuminance measurements at key locations within the space, whereas thermal harvesting performance was evaluated through temperature measurements and thermal imaging analysis. We show that the LFPL system, with all prismatic elements oriented at the same angle, achieve a 2-fold and 8-fold enhancement in daylight redirection to the ceiling, for prism orientations of 10° and 20°, respectively. We also demonstrate the system’s capability to adjust to specific lighting needs, within the space, through the dynamic individual orientation of prismatic elements; thus, achieving a concentrated ceiling illuminance enhancement of ~100 times and ~200 times at 2.5 and 4.3 m away from the window sill, providing workplane illuminance enhancement of 6 and 2 times more than in the case of the control room with regular windows. Furthermore, we illustrate a reduction of potential heating loads at locations close to the window from the combination of infrared absorption in the water volume and the redirection of the incoming solar radiation, leading to a reduction of the workplane temperature by an average of 7–10 °C.

14 SOLAR ENERGY↗
DOE OSTI2020

Time-dependent relaxation of observables in complex quantum systems

Abstract We consider time-dependent relaxation of observables in quantum systems of chaotic and regular type. Using statistical arguments and exact numerical solutions we show that the spread of the initial wave function in the Hilbert space and the main characteristics of evolution of observables have certain generic features. The study compares examples of regular dynamics, a completely chaotic case of the Gaussian orthogonal ensemble, a bosonic system with random interactions, and a fully realistic case of the time evolution of various initial non-stationary states in the nuclear shell model. In the case of the Gaussian orthogonal ensemble we show that the survival probability obtained analytically also fully defines the relaxation timescale of observables. This is not the case in general. Using the realistic nuclear shell model and the quadrupole moment as an observable we demonstrate that the relaxation time is significantly longer than defined by the survival probability of the initial state. The full analysis does not show the presence of an analog of the Lyapunov exponent characteristic for examples of classical chaos.

Volya, Alexander (ORCID:0000000217656466)↗
DOE OSTI2026

Data driven drift correction for complex optical systems

To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam trajectory. Maintaining the optimal trajectory over several hours of an experiment with the needed precision necessitates active drift control. Here, we outline time varying Bayesian optimization (TVBO) as a data driven approach for robust drift correction, and illustrate its application for a split and delay optical system composed of six crystals and twelve input dimensions. Using numerical simulations, we exhibit the application of TVBO for linear drift, non-smooth temporal drift as well as constrained TVBO for multi-objective control settings, representing real-life operating conditions. This approach can be easily adapted to other X-ray beam conditioning and guidance systems, including multi-crystal monochromators and grazing-incidence mirrors, to maintain sub-micrometer and nanoradian beam stability over the course of an experiment spanning several hours.

Bayesian optimization↗