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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Megadrought: A Series of Unfortunate La Niña Events?

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The PSZ-MCMF catalogue of Planck clusters over the DES region

ABSTRACT We present the first systematic follow-up of Planck Sunyaev–Zeldovich effect (SZE) selected candidates down to signal-to-noise (S/N) of 3 over the 5000 deg2 covered by the Dark Energy Survey. Using the MCMF cluster confirmation algorithm, we identify optical counterparts, determine photometric redshifts, and richnesses and assign a parameter, fcont, that reflects the probability that each SZE-optical pairing represents a random superposition of physically unassociated systems rather than a real cluster. The new PSZ-MCMF cluster catalogue consists of 853 MCMF confirmed clusters and has a purity of 90 per cent. We present the properties of subsamples of the PSZ-MCMF catalogue that have purities ranging from 90 per cent to 97.5 per cent, depending on the adopted fcont threshold. Halo mass estimates M500, redshifts, richnesses, and optical centres are presented for all PSZ-MCMF clusters. The PSZ-MCMF catalogue adds 589 previously unknown Planck identified clusters over the DES footprint and provides redshifts for an additional 50 previously published Planck-selected clusters with S/N>4.5. Using the subsample with spectroscopic redshifts, we demonstrate excellent cluster photo-z performance with an RMS scatter in Δz/(1 + z) of 0.47 per cent. Our MCMF based analysis allows us to infer the contamination fraction of the initial S/N>3 Planck-selected candidate list, which is ∼50 per cent. We present a method of estimating the completeness of the PSZ-MCMF cluster sample. In comparison to the previously published Planck cluster catalogues, this new S/N>3 MCMF confirmed cluster catalogue populates the lower mass regime at all redshifts and includes clusters up to z∼1.3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Dirty bosons on the Cayley tree: Bose-Einstein condensation versus ergodicity breaking

Building on large-scale quantum Monte Carlo simulations, we investigate the zero-temperature phase diagram of hard-core bosons in a random potential on site-centered Cayley trees with branching number K=2. In order to follow how the Bose-Einstein condensate (BEC) is affected by the disorder, we focus on both the zero-momentum density, probing the quantum coherence, and the one-body density matrix (1BDM) whose largest eigenvalue monitors the off-diagonal long-range order. We further study its associated eigenstate which brings useful information about the real-space properties of this leading eigenmode. Upon increasing randomness, we find that the system undergoes a quantum phase transition at finite disorder strength between a long-range ordered BEC state, fully ergodic at large scale, and a new disordered Bose glass regime showing conventional localization for the coherence fraction while the 1BDM displays a nontrivial algebraic vanishing BEC density together with a nonergodic occupation in real space. These peculiar properties can be analytically captured by a simple phenomenological description on the Cayley tree which provides a physical picture of the Bose glass regime.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Conductivity of superconductors in the flux flow regime

Here, we develop a theory of conductivity of type-II superconductors in the flux flow regime taking into account random spatial fluctuations of the system parameters, such as the gap magnitude Δ(r) and the diffusion coefficient $\textit{D}$(r). We find a contribution to the conductivity that is proportional to the inelastic relaxation time $τ_{\text{in}}$, which is much longer than the elastic relaxation time. This contribution is due to Debye-type relaxation, and it can be much larger than the conventional flux flow conductivity due to Bardeen and Stephen. The new contribution is expected to dominate in clean superconductors at low temperatures and in magnetic fields much smaller than $H_{\text{c2}}$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Data From Experiments on Bubbling Fluidization of Zeolite in a Rectangular Bubbling Fluidized Bed

Fluidization experiments were conducted in a lab-scale rectangular bubbling fluidized bed with the objective of generating a high-quality dataset for model validation and artificial intelligence/machine learning (AI/ML) training. Zeolite was chosen as the bed material, and the fluidizing medium was air as supplied by a compressor. Three different flow rates at the inlet were chosen such that the particles were fluidized but not elutriated from the system. The test matrix involved randomization and replicates to provide uncertainty estimates as well as four different batches of zeolite as the bed material. The quantities of interest obtained from this study were statistics of differential pressures, interface heights, and particle velocities. Considering all the components of the elaborate test plan, the results obtained were consistent and reproducible. Characterization tests were performed to estimate particle properties including size, density, coefficient of friction, coefficient of restitution, and minimum fluidization velocity. In addition, the angle of repose from granular discharge experiments has been reported to account for rolling friction, though its effect on the overall process is expected to be negligible.

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

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
DOE OSTI2022

A modified electrolyte non-random two-liquid model with analytical expression for excess enthalpy: Application to the MEA-H 2 O-CO 2 system

We report accurate thermodynamic properties of electrolyte systems are critical for the design and operation of many chemical processes. A comprehensive description of the thermodynamic framework for multi-electrolyte mixed solvent systems is presented, where the parameter structure of the symmetric electrolyte-Non-Random Two Liquid (e-NRTL) model is reformulated and a thermodynamically consistent and analytically derived formulation for the excess enthalpy is developed from the e-NRTL model. The refined parameter structure of the e-NRTL model avoids numerical singularities of the analytical formulation for the excess enthalpy in the absence of ionic species and extends the derived excess enthalpy formulation to non-electrolyte systems. The thermodynamic framework is demonstrated for the MEA-H 2 O-CO 2 case study using experimental data on thermodynamic quantities for the binary MEA-H 2 O system and the ternary MEA-H 2 O-CO 2 system. The model is implemented in Pyomo and will be available for release in the Institute for the Design of Advanced Energy Systems (IDAES) computational platform.

Monoethanolamine↗
DOE OSTI2021

Sketching Algorithms in Distributed Systems

In this position paper, we discuss exciting recent advancements in sketching algorithms applied to distributed systems. That is, we look at randomized algorithms that simultaneously reduce the data dimensionality, offer potential privacy benefits, while maintaining verifiably high levels of algorithm accuracy and performance in multi-node computational setups. We look at next steps and discuss the applicability to real systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator: Preprint

To reduce the variance of rare event probability estimator, genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. It is shown that a random perturbation strategy may be effective for some systems but may also fail for others, thereby preventing variance reduction in the probability estimate. To address this limitation, it is proposed to use a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗
DOE OSTI2024

Disorder Enhanced Thermalization in Interacting Many-Particle System

We introduce an extension of the non-equilibrium dynamical mean field theory to incorporate the effects of static random disorder in the dynamics of a many-particle system by integrating out different disorder configurations resulting in an effective time-dependent density-density interaction. We use this method to study the non-equilibrium transient dynamics of a system described by the Fermi Anderson-Hubbard model following an interaction and disorder quench. The method recovers the solution of the disorder-free case for which the system exhibits qualitatively distinct dynamical behaviors in the weak-coupling (prethermalization) and strong-coupling regimes (collapse-and-revival oscillations). However, we find that weak random disorder promotes thermalization. In the weak coupling regime, the jump in the quasiparticle weight in the prethermal regime is suppressed by random disorder while in the strong-coupling regime, random disorder reduces the amplitude of the quasiparticle weight oscillations. These results highlight the importance of disorder in the dynamics of realistic many-particle systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗
DOE OSTI2020

Strong pinning and slow flux creep relaxation in Co-doped CaFe 2 As 2 single crystals

In this paper we report on measurements of critical current densities J c and flux creep rates S of freestanding Ca(Fe 1–x Co x ) 2 As 2 (x ≈ 0.033) single crystals with T c ≈ 15.7 K by performing magnetization measurements. The magnetic field dependences of J c at low temperature display features related to strong pinning. In addition, we find that the system displays small flux creep rates. The characteristic glassy exponent, μ, and the pinning energy, U 0 , display exceptional high values for pristine crystals. We find that for magnetic fields between 0.3 T and 1 T, μ decreases from ≈ 2.8 to ≈ 2 and U 0 remains ≈ 300 K. Analysis of the pinning force indicates that the mechanism is similar to the observed in polycrystalline systems in which grain boundaries and random disorder produce the vortex pinning. Considering the large U 0 observed in the single crystal, we attempt to improve the pinning by adding random point disorder by 3 MeV proton irradiation with a fluence of 2 × 10 16 proton/cm 2 . The results show that, unlike other iron-based superconductors, the superconducting fraction is sharply reduced by irradiation. This fact indicates that the superconductivity in the system is extremely fragile to an increment in the disorder. The superconducting volume fraction in the irradiated crystal systematically recovers after removal disorder by thermal annealing, which evidences as to the observation of critical state in curves of magnetization versus magnetic field. No features related to a reentrant antiferromagnetic transition are observed for the irradiated sample.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Many-Body Level Statistics of Single-Particle Quantum Chaos

We consider a noninteracting many-fermion system populating levels of a unitary random matrix ensemble (equivalent to the q = 2 complex Sachdev-Ye-Kitaev model)—a generic model of single-particle quantum chaos. We study the corresponding many-particle level statistics by calculating the spectral form factor analytically using algebraic methods of random matrix theory, and match it with an exact numerical simulation. Despite the integrability of the theory, the many-body spectral rigidity is found to have a surprisingly rich landscape. In particular, we find a residual repulsion of distant many-body levels stemming from single-particle chaos, together with islands of level attraction. These results are encoded in an exponential ramp in the spectral form factor, which we show to be a universal feature of nonergodic many-fermion systems embedded in a chaotic medium.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Classical field approximation of ultralight dark matter: Quantum break times, corrections, and decoherence

The classical field approximation is widely used to better understand the predictions of ultralight dark matter. Here, in this work, we use the truncated Wigner approximation method to test the classical field approximation of ultralight dark matter. This method approximates a quantum state as an ensemble of independently evolving realizations drawn from its Wigner function. The method is highly parallelizable and allows the direct simulation of quantum corrections and decoherence times in systems many times larger than have been previously studied in reference to ultralight dark matter. Our study involves simulation of systems in 1, 2, and 3 spatial dimensions. We simulate three systems, the condensation of a Gaussian random field in three spatial dimensions, a stable collapsed object in three spatial dimensions, and the merging of two stable objects in two spatial dimensions. We study the quantum corrections to the classical field theory in each case. We find that quantum corrections grow exponentially during nonlinear growth with the timescale being approximately equal to the system dynamical time. In stable systems the corrections grow quadratically. We also find that the primary effect of quantum corrections is to reduce the amplitude of fluctuations on the de Broglie scale in the spatial density. Finally, we find that the timescale associated with decoherence due to gravitational coupling to baryonic matter is at least as fast as the quantum corrections due to gravitational interactions. These results are consistent with the predictions of the classical field theory being accurate.

79 ASTRONOMY AND ASTROPHYSICS↗