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Results for “Generalized predictive control”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Persistent compositions of non-stoichiometric compounds with low bulk diffusivity: A theory and application to Nb3Sn superconductors

Non-stoichiometric compounds may develop a composition gradient when they are formed by reactive diffusional processes. This paper reports an interesting phenomenon that in compounds with low bulk diffusivities, which rely mainly on grain boundary diffusion for their growth, the final bulk compositions may be far from equilibrium, with a very low bulk diffusivity leading to fixed (persistent) compositions – one such example is Nb 3 Sn, a superconductor. We investigated the microchemistry at the reactive interface using atom probe tomography to clarify the diffusion reaction mechanism for this low-bulk-diffusivity case and thus propose a theory for what determines the compound composition, using Nb 3 Sn as an example for concreteness. Using certain approximations, we derive an explicit analytical equation that illustrates what factors determine its composition profile. We compare our model with the known facts of Nb 3 Sn and see good agreement. In particular, this model predicts that internal oxidation may lead to higher Sn contents than conventional, non-oxidized Nb 3 Sn. Our measurements show that this is indeed true, and that the internally-oxidized Nb 3 Sn also has higher upper critical fields, achieving up to 28.2 T at 4.2 K. We discuss the general applicability of this model to non-stoichiometric compounds with low bulk diffusivity, and propose it as a tool to help in the design and processing of such materials for compositional control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Orbital-spin locking and its optical signatures in altermagnets

Altermagnets, magnetic materials with zero magnetization and spin-split band structure, have gained tremendous attention recently for their rich physics and potential applications. Here, we report on a microscopic tight-binding model that unveils a unique coupling between orbitals and spins in d-wave altermagnets, which gives rise to momentum-dependent and spin-selective optical absorption. This coupling promotes the controlled optical excitation of up or down spins depending on the polarization direction of linearly polarized light. Such an effect originates from the coupling of orbitals to the sublattice degree of freedom through the crystal field, which is then coupled to spins through the antiferromagnetic interaction. Our crystal field analysis, which is general to any type of altermagnet, helps understand the onset of altermagnetism from a microscopic point of view, and we use our results to propose clear magneto-optical signatures of our predictions. Our findings shine light on the interplay between orbitals and spins in altermagnets, thus paving the way towards novel orbitronic and optospintronic devices.

Vila, Marc↗
DOE OSTI2023

Full Technical Report: High-Rate Carbide Growth by Evaporation

Boron carbide (B 4 C) is an attractive inertial confined fusion (ICF) ablator and a unique ultra-hard material with numerous current and potential applications. What makes B 4 C attractive as an ablator is its relatively high density and the ability to form a stable glassy phase combined with excellent chemical and mechanical stability. Despite these advantages, the fabrication of B 4 C ablators has remained a challenge. Magnetron sputtering, our current frontrunner approach for B 4 C deposition, exhibits low deposition rates (< 2 μm/h) and undesirable nodular defects within the film structure that are believed to generate from the dusty plasma around the sputtering source. The goals of this project were to (1) establish alternative plasma-free, electron beam evaporation (EBE) capabilities for the deposition of thin films that are needed in mission-critical programs and; (2) demonstrate the feasibility to deposit B 4 C films at high rates by EBE in a bottom-up geometry and characterize their stoichiometry. In this feasibility study, we initially developed the capability to convert a general-use vacuum chamber into an electron beam evaporator as needed, or on demand. Then, we successfully used this setup to deposit films made of boron carbide and various other materials. By adjusting the deposition parameters and the evaporation source material, we were able to demonstrate the feasibility of reaching deposition rates as high as 8 μm/h, which is about 4 times the maximum rates obtained through sputtering methods. We predict that these values can still be further improved by implementing enhanced thermal control measures for the target source and optimizing electron beam parameters (ie. current, sweeping and frequency) since ceramic materials with similar densities to B 4 C can exhibit deposition rates of about twice this value. We anticipate that the skills and capabilities developed throughout this project will prove relevant not only to the HED and ICF campaigns but also to numerous existing and prospective applications focused on the development of ultra-hard carbide coatings. The fresh set of tools here developed will also facilitate the preparation and research on future new target materials of interest beyond B 4 C.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of net direct current on the properties of radio frequency sheaths: simulation and cross-code comparison

In order to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, computational tools which can model radio frequency (RF) sheaths are needed. In particular, models for the effective surface impedance and DC rectified sheath potentials may be coupled with full wave RF simulation codes to predict self-consistent wave fields near surfaces and the resulting power dissipation and plasma-material interactions from ion sputtering. In this study, previous work assuming zero net DC current flow through the sheath is generalized to allow the surface to collect net positive or negative current, as is often observed in experiments. The waveforms, DC potential and RF admittance are investigated by means of analytical theory, nonlinear fluid and particle-in-cell (PIC) codes. Cross-code comparisons provide detailed model verification and elucidate the roles of ion and electron kinetics. When the sheath draws negative (positive) DC current, the voltage rectification is reduced (increased) compared with the zero-current case, and the magnitude of both the real and imaginary parts of the admittance are increased (reduced). In conclusion, a previous four-input parametrization of the sheath rectification and admittance properties is generalized to include a fifth parameter describing the DC sheath current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality

Background: The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates. Methods: This study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter’s predicted rates across the nation’s counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally. Results: Our spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation’s most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift. Conclusions: The initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Symmetric Rydberg controlled- Z gates with adiabatic pulses

We analyze neutral atom Rydberg C Z gates based on adiabatic pulses applied symmetrically to both atoms. Analysis with smooth pulse shapes and Cs atom parameters predicts the gates can create Bell states with fidelity F > 0.999 using adiabatic rapid passage (ARP) pulses. With globally optimized adiabatic pulse shapes, in a two-photon excitation process, we generate Bell states with fidelity F = 0.997. The analysis fully accounts for spontaneous emission from intermediate and Rydberg states, including the Rydberg lifetime in a room temperature environment, but does not include errors arising from laser noise. In conclusion, the gate protocols do not require individual addressing and are shown to be robust against Doppler shifts due to atomic motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy

A machine learning approach is introduced to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). This method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which is used to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. Empirical analyses are presented that demonstrate the efficacy and generality of the approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crystal phases of charged interlayer excitons in van der Waals heterostructures

Throughout the years, strongly correlated coherent states of excitons have been the subject of intense theoretical and experimental studies. This topic has recently boomed due to new emerging quantum materials such as van der Waals (vdW) bound atomically thin layers of transition metal dichalcogenides (TMDs). We analyze the collective properties of charged interlayer excitons observed recently in bilayer TMD heterostructures. We predict strongly correlated phases—crystal and Wigner crystal—that can be selectively realized with TMD bilayers of properly chosen electron-hole effective masses by just varying their interlayer separation distance. Our results can be used for nonlinear coherent control, charge transport and spinoptronics application development with quantum vdW heterostuctures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Controlling laser-dressed resonance line shape using attosecond extreme-ultraviolet pulse with a spectral minimum

High-harmonic generation from a gas target exhibits sharp spectral features and rapid phase variation near the Cooper minimum. By applying spectral filtering, shaped isolated attosecond pulses can be generated where the pulse is split into two in the time domain. Using such shaped extreme-ultraviolet (XUV) pulses, we theoretically study attosecond transient absorption (ATA) spectra of helium 2s2p autoionizing state which is resonantly coupled to the 2s 2 dark state by a time-delayed infrared laser. Our simulations show that the asymmetric 2s2p Fano line shape can be readily tuned into symmetric Lorentzian within the time delay of a few tens of attoseconds. Such efficient control is due to the destructive interference in the generation of the 2s2p state when it is excited by a strongly shaped XUV pulse. This is to be compared to prior experiments where tuning the line shape of a Fano resonance would take tens of femtoseconds. We also show that the predicted ATA spectral line shape can be observed experimentally after propagation in a gas medium. Our results suggest that strongly shaped attosecond XUV pulses offer the opportunity for controlling and probing fine features of narrow resonances on the few-ten attoseconds timescale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Predictive turbulence-driven flux model of scrape-off layer widths across confinement regimes in tokamaks

Reliable scrape-off layer (SOL) profile decay lengths predictions are needed to design and operate future tokamaks. The present manuscript describes a new model based on turbulent transport that is able to predict SOL widths for both L-mode and H-mode plasmas. The model is based upon the sheared-spectral filament paradigm (Peret et al (WEST Team) 2022 Phys. Plasmas 29 072306), however, incorporating the effects of thermal transport in order to calculate the parallel heat fluxes. The effects of magnetic shear and ExB shear on the cross-field transport are crucial to explain the shorter SOL decay lengths found in H-mode. The model is validated against a database of thousands of DIII-D L-mode and H-mode SOL profiles. We also calculate SOL decay length predictions in terms of plasma and engineer control parameters, which are in agreement with the multi-machine empirical H-mode scaling (Eich et al (ASDEX Upgrade Team and JET EFDA Contributors) 2013 Nucl. Fusion 53 093031), however, with an additional device geometry dependence. ITER SOL width predictions by the model are 3 times higher than the empirical scaling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Polymorphic Metastability in Colloidal Semiconductor Nanocrystals

Metastable polymorphs of inorganic solids often possess material properties not present in the corresponding thermodynamic polymorphs, making them targets for the development of new functional materials. In contrast with isolating metastable bulk materials, syntheses of metastable polymorphs on the nanoscale are aided by fast non-equilibrium reaction kinetics and the favorable thermodynamic influence of surface energies, giving rise to greater ease of access to metastable high-temperature polymorphs and, in some cases, new polymorphs that do not exist in the bulk. The syntheses of metastable semiconductor nanocrystals are of interest for their potentially unique optoelectronic and physicochemical properties. However, in many material systems, synthesizing nanocrystalline products away from thermodynamic equilibrium in a predictable manner remains an outstanding challenge. This review outlines direct synthetic methodologies that have been developed to enable control over the nucleation and growth of metastable polymorphs of semiconductor nanocrystals by tailoring reaction conditions, precursor kinetics, ligand and surface effects, and other synthetic levers. The case studies reviewed herein expound on the direct syntheses of metastable ZnSe, Cu 2 SnSe 3 , CuInSe 2 , Ag 2 Se, and AgInSe 2 nanocrystals, and although there remain numerous examples of metastable nanocrystal syntheses outside of these metal chalcogenide systems, the concepts discussed are of general utility to the field of metastable nanocrystal syntheses as a whole. Explicit examples in which new functional properties are afforded by metastable polymorphs of the aforementioned material systems are presented within the context of applications for solar cells, photonics, and optical sensing. Finally, the factors that affect the kinetic persistence of metastable nanocrystalline polymorphs are discussed at length for these material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reversible Phasonic Control of a Quantum Phase Transition in a Quasicrystal

Periodic driving can tune the quasistatic properties of quantum matter. A well-known example is the dynamical modification of tunneling by an oscillating electric field. Here we show experimentally that driving the phasonic degree of freedom of a cold-atom quasicrystal can continuously tune the effective quasidisorder strength, reversibly toggling a localization-delocalization quantum phase transition. Measurements agree with fit-parameter-free theoretical predictions, and illuminate a fundamental connection between Aubry-André localization in one dimension and dynamic localization in the associated two-dimensional Harper-Hofstadter model. These results open up new experimental possibilities for dynamical coherent control of quantum phase transitions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition

Black carbon (BC) absorbs solar radiation, leading to a strong but uncertain warming effect on climate. A key challenge in modeling and quantifying BC’s radiative effect on climate is predicting enhancements in light absorption that result from internal mixing between BC and other aerosol components. Modeling and laboratory studies show that BC, when mixed with other aerosol components, absorbs more strongly than pure, uncoated BC; however, some ambient observations suggest more variable and weaker absorption enhancement. We show that the lower-than-expected enhancements in ambient measurements result from a combination of two factors. First, the often used spherical, concentric core-shell approximation generally overestimates the absorption by BC. Second, and more importantly, inadequate consideration of heterogeneity in particle-to-particle composition engenders substantial overestimation in absorption by the total particle population, with greater heterogeneity associated with larger model–measurement differences. We show that accounting for these two effects—variability in per-particle composition and deviations from the core-shell approximation—reconciles absorption enhancement predictions with laboratory and field observations and resolves the apparent discrepancy. Furthermore, our consistent model framework provides a path forward for improving predictions of BC’s radiative effect on climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Investigation of the end-gas autoignition process in natural gas engines and evaluation of the methane number index

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the wide range of chemical reactivity in pipeline quality NG, represented by the Methane Number (MN) (65< MN<95). Understanding the underlying phenomena responsible for engine knock can support the development of predictive tools capable of identifying knock onset/intensity as well as a fuel’s propensity to knock, allowing engine manufacturers to expand the knock envelope and design more efficient/robust SI NG engines. Additionally, there is an opportunity for increased efficiency by controlling levels of end-gas autoignition if this can be predicted and controlled. This work focuses on the development of a novel methodology to understand/predict a fuel’s propensity to knock. This methodology is based on the charge fraction undergoing autoignition, namely fractional end-gas autoignition (F-EGAI), and was developed based on first order laminar flame speeds and ignition delay analysis combined with a 0-D homogeneous batch reactor model. This methodology proved to be suitable to predict a fuel’s propensity to knock, even under conditions when light knock was observed. The simple modeling approach was used to explain the results from a series of MN tests with multiple NG compositions exhibiting a wide range of reactivity compositions and providing insight on why fuels of very different chemical compositions can have the same MN. Finally, a CFD model was developed was used to confirm the methodology capability and provide further insights in the physical and chemical phenomena behind end gas autoignition.

42 ENGINEERING↗
DOE OSTI2021

State predictive information bottleneck

We report the ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗