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Results for “Hierarchical Feedback”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Redox-enabled electronic interrogation and feedback control of hierarchical and networked biological systems

Abstract Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity. E. coli ’s stress response regulon, oxyRS , is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data

Abstract. Geological records of past environmental change provide crucial insights into long-term climate variability, trends, non-stationarity, and nonlinear feedback mechanisms. However, reconstructing spatiotemporal fields from these records is statistically challenging due to their sparse, indirect, and noisy nature. Here, we present PaleoSTeHM, a scalable and modern framework for spatiotemporal hierarchical modeling of paleo-environmental data. This framework enables the implementation of flexible statistical models that rigorously quantify spatial and temporal variability from geological data while clearly distinguishing measurement and inferential uncertainty from process variability. We illustrate its application by reconstructing temporal and spatiotemporal paleo-sea-level changes across multiple locations. Using various modeling and analysis choices, PaleoSTeHM demonstrates the impact of different methods on inference results and computational efficiency. Our results highlight the critical role of model selection in addressing specific paleo-environmental questions, showcasing the PaleoSTeHM framework's potential to enhance the robustness and transparency of paleo-environmental reconstructions.

58 GEOSCIENCES↗
DOE OSTI2021

A Kinematic Perspective on the Formation Process of the Stellar Groups in the Rosette Nebula

Stellar kinematics is a powerful tool for understanding the formation process of stellar associations. Here, we present a kinematic study of the young stellar population in the Rosette nebula using recent Gaia data and high-resolution spectra. We first isolate member candidates using the published mid-infrared photometric data and the list of X-ray sources. A total of 403 stars with similar parallaxes and proper motions are finally selected as members. The spatial distribution of the members shows that this star-forming region is highly substructured. The young open cluster NGC 2244 in the center of the nebula has a pattern of radial expansion and rotation. We discuss its implication on the cluster formation, e.g., monolithic cold collapse or hierarchical assembly. On the other hand, we also investigate three groups located around the border of the H ii bubble. The western group seems to be spatially correlated with the adjacent gas structure, but their kinematics is not associated with that of the gas. The southern group does not show any systematic motion relative to NGC 2244. These two groups might be spontaneously formed in filaments of a turbulent cloud. The eastern group is spatially and kinematically associated with the gas pillar receding away from NGC 2244. This group might be formed by feedback from massive stars in NGC 2244. Our results suggest that the stellar population in the Rosette Nebula may form through three different processes: the expansion of stellar clusters, hierarchical star formation in turbulent clouds, and feedback-driven star formation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Anisotropic Satellite Galaxy Quenching: A Unique Signature of Energetic Feedback by Supermassive Black Holes?

The quenched fraction of satellite galaxies is aligned with the orientation of the halo's central galaxy, such that on average, satellites form stars at a lower rate along the major axis of the central. This effect, called anisotropic satellite galaxy quenching (ASGQ), has been found in observational data and cosmological simulations. Analyzing the IllustrisTNG simulation, Martín-Navarro et al. recently argued that ASGQ is caused by anisotropic energetic feedback and constitutes "compelling observational evidence for the role of black holes in regulating galaxy evolution." In this Letter, we study the causes of ASGQ in state-of-the-art galaxy formation simulations to evaluate this claim. We show that cosmological simulations predict that on average, satellite galaxies along the major axis of the dark matter halo tend to have been accreted at earlier cosmic times and are hosted by subhalos of larger peak halo masses. As a result, a modulation of the quenched fraction with respect to the major axis of the central galaxy is a natural prediction of hierarchical structure formation. We show that ASGQ is predicted by the UniverseMachine galaxy formation model, a model without anisotropic feedback. Furthermore, we demonstrate that even in the IllustrisTNG simulation, anisotropic satellite accretion properties are the main cause of ASGQ. Ultimately, we argue that ASGQ is not a reliable indicator of supermassive black hole feedback in galaxy formation simulations and, thus, should not be interpreted as such in observational data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A declining major merger fraction with redshift in the local Universe from the largest-yet catalogue of major and minor mergers in SDSS

It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that uses linear discriminant analysis to identify major and minor mergers. Here, we apply this technique to 1.3 million galaxies from the SDSS DR16 photometric catalogue and present the probability that each galaxy is a major or minor merger, splitting the classifications by merger stages (early, late, post-coalescence). We present publicly available imaging predictor values and all of the above classifications for one of the largest-yet samples of galaxies. We measure the major and minor merger fraction (f merg ) and build a mass-complete sample of galaxies, which we bin as a function of stellar mass and redshift. For the major mergers, we find a positive slope of fmerg with stellar mass and negative slope of f merg with redshift between stellar masses of 10.5 < M * (log M ⊙ ) < 11.6 and redshifts of 0.03 < z < 0.19. We are able to reproduce an artificial positive slope of the major merger fraction with redshift when we do not bin for mass or craft a complete sample, demonstrating the importance of mass completeness and mass binning. Further, we determine that the positive trend of the major merger fraction with stellar mass is consistent with a hierarchical assembly scenario. The negative trend with redshift requires that an additional assembly mechanism, such as baryonic feedback, dominates in the local Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Atlantic Multidecadal Oscillation: Key Drivers and Climate Impacts (Final Technical Report)

The goal of this project was to investigate the Atlantic multidecadal variability (AMV) using a hierarchical modeling framework to advance our process-level understanding on the respective roles of, and the feedback between, the key drivers of the AMV, in particular the ocean circulation associated with the Atlantic meridional overturning circulation and stochastic atmospheric forcing, primarily due to the North Atlantic Oscillation. The focus was primarily on the AMV due to natural variability, but the investigation will be extended to the possible modulation on the main characteristics of AMV under external climate forcing. Furthermore, we examined the impact of the AMV, with a special emphasis on the possible augmentation of predictability the AMV may bring to the regional weather and climate of North America, Western Europe, and the Arctic. Under this goal, we have published 15 peer-reviewed journal papers, and presented our results in more than 30 conferences over the project duration.

58 GEOSCIENCES↗
DOE OSTI2022

Supporting hierarchical soil biogeochemical modeling: version 2 of the Biogeochemical Transport and Reaction model (BeTR-v2)

Abstract. Reliable soil biogeochemical modeling is a prerequisite for credible projections of climate change and associated ecosystem feedbacks. This recognition has called for frameworks that can support flexible and efficient development and application of new or alternative soil biogeochemical modules in Earth system models (ESMs). The the Biogeochemical Transport and Reaction model version 1 (BeTR-v1) code (i.e., CLM4-BeTR) is one such framework designed to accelerate the development and integration of new soil biogeochemistry formulations into ESMs and to analyze structural uncertainty in ESM simulations. With a generic reactive transport capability, BeTR-v1 can represent multiphase (e.g., gaseous, aqueous, and solid), multi-tracer (e.g., nitrate and organic carbon), and multi-organism (e.g., plants, bacteria, and fungi) dynamics. Here, we describe the new version, Biogeochemical Transport and Reaction model version 2 (BeTR-v2), which adopts more robust numerical solvers for multiphase diffusion and advection and coupling between biogeochemical reactions and improves code modularization over BeTR-v1. BeTR-v2 better supports different mathematical formulations in a hierarchical manner by allowing the resultant model be run for a single topsoil layer or a vertically resolved soil column, and it allows the model to be fully coupled with the land component of the Energy Exascale Earth System Model (E3SM). We demonstrate the capability of BeTR-v2 with benchmark cases and example soil biogeochemical (BGC) implementations. By taking advantage of BeTR-v2's generic structure integrated in E3SM, we then found that calibration could not resolve biases introduced by different numerical coupling strategies of plant–soil biogeochemistry. These results highlight the importance of numerically robust implementation of soil biogeochemistry and coupling with hydrology, thermal dynamics, and plants – capabilities that the open-source BeTR-v2 provides. We contend that Earth system models should strive to minimize this uncertainty by applying better numerical solvers.

58 GEOSCIENCES↗
DOE OSTI2021

Modelica-based system modeling for studying control-related faults in chiller plants and boiler plants serving large office buildings

System modeling is critical when studying operation faults in chiller plants and boiler plants. However, current fault models have difficulties faithfully representing the operation of chiller plants and boiler plants under the effects of those faults, especially the control-related ones. In this study, we present a systematic method to develop high-fidelity models for approximating the behaviors of chiller plants and boiler plants under faulty conditions. Compared to existing ones, the resulting fault models have two advantages: first, they better characterize the dynamic patterns in the system operation. In those models, control architecture and control logic are faithfully implemented. Thus, they can be used to study control-related faults, such as incorrect staging control due to sensor bias and mistuned feedback control. Second, they are readily extensible and can support large-scale investigations to explore different faulty conditions/scenarios. Those models are established in a hierarchical structure while modules in each layer can be redeclared and parameterized at upper layers. In such a case, modifications to the models can be realized through model modifiers and the process can be easily streamlined with scripts. We applied the proposed models in a comprehensive fault impact evaluation of the 13 control-related faults of chiller and boiler plants. In this evaluation, the proposed plant model is coupled with the EnergyPlus thermal load model to study the impact of various faulty scenarios. Based on the evaluation results, we identified the faults that have the most significant impacts on the operation of the chiller and boiler plants, respectively. We also found that the relationship between the impacts of the studied faults and the severity level of the faults are highly non-linear

Huang, Sen↗
DOE OSTI2021

Superconducting neural networks with disordered Josephson junction array synaptic networks and leaky integrate-and-fire loop neurons

Fully coupled randomly disordered recurrent superconducting networks with additional open-ended channels for inputs and outputs are considered the basis to introduce a new architecture to neuromorphic computing in this work. Various building blocks of such a network are designed around disordered array synaptic networks using superconducting devices and circuits as an example, while emphasizing that a similar architectural approach may be compatible with several other materials and devices. A multiply coupled (interconnected) disordered array of superconducting loops containing Josephson junctions [equivalent to superconducting quantum interference devices (SQUIDs)] forms the aforementioned collective synaptic network that forms a fully recurrent network together with compatible neuron-like elements and feedback loops, enabling unsupervised learning. This approach aims to take advantage of superior power efficiency, propagation speed, and synchronizability of a small world or a random network over an ordered/regular network. Additionally, it offers a significant factor of increase in scalability. Here, a compatible leaky integrate-and-fire neuron made of superconducting loops with Josephson junctions is presented, along with circuit components for feedback loops as needed to complete the recurrent network. Several of these individual disordered array neural networks can further be coupled together in a similarly disordered way to form a hierarchical architecture of recurrent neural networks that is often suggested as similar to a biological brain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Controlling colloidal crystals via morphing energy landscapes and reinforcement learning

We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10 3 particles with mean process times scaling as N 0.5 . Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Two-Phase Turbulence Statistics from High Fidelity Dispersed Droplet Flow Simulations in a Pressurized Water Reactor (PWR) Sub-Channel with Mixing Vanes

In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam. Experiments have accredited particular significance to droplet collision with the spacer-grids and mixing vane structures and their consequent positive feedback to the heat transfer recorded in the immediate downstream vicinity. Enabled by high-performance computing (HPC) systems and a massively parallel finite element-based flow solver—PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis)—this work presents high fidelity interface capturing, two-phase, adiabatic simulations in a PWR sub-channel with spacer grids and mixing vanes. Selected flow conditions for the simulations are informed by the experimental data found in the literature, including the steam Reynolds number and collision Weber number (Wec={40,80}), and are characteristic of the DFFB regime. Data were collected from the simulations at an unprecedented resolution, which provides detailed insights into the continuous phase turbulence statistics, highlighting the effects of the presence of droplets and the comparative effect of different Weber numbers on turbulence in the surrounding steam. Further, axial evolution of droplet dynamics was analyzed through cross-sectionally averaged quantities, including droplet volume, surface area and Sauter mean diameter (SMD). The downstream SMD values agree well with the existing empirical correlations for the selected range of Wec. The high-resolution data repository from the simulations herein is expected to be of significance to guide model development for system-level thermal hydraulic codes.

Saini, Nadish↗
DOE OSTI2023

Understanding Dynamics and Thermodynamics of ENSO and Its Complexity Simulated by E3SM and Other Climate Models

Despite the seeming success of most state-of-the-art climate models in simulating the El Niño-Southern Oscillation (ENSO), there is strong evidence that models achieve realistic levels of ENSO activity do so often for wrong reasons. This is owing to an often occurred near cancelation of large errors in terms of contributions to ENSO growth rate from coupled dynamic and thermodynamic feedback processes. Climate models remain deficient in simulating the observed ENSO’s spatial and temporal complexity that involves interplays of coupled dynamic and thermodynamic feedbacks, interactions across multiple scales, nonlinear processes in the tropical atmosphere and ocean system, biases in mean sate and physical processes, and influences external to equatorial Pacific coupled ENSO dynamics. Our proposed research aims at advancing predictive and process-level understandings of ENSO simulated in E3SM and other climate models under current and future climate conditions with two main objectives: (i) better understanding the aforementioned broad range interactive processes and sources that control fundamental properties of ENSO in E3SM and CIMP6 outputs as well as in observational (reanalysis) data sets, using a hierarchical of coupled dynamical frameworks consisting of theoretical analysis, intermediate complexity modeling, and coupled dynamic diagnostics; (ii) to use this understanding to explore pathways towards improving E3SM’s capability of simulating ENSO and its complexity. More specifically, we will focus on four main thrusts of research: (1) ENSO’s dynamic and thermodynamic feedbacks; (2) the across-scale interactions of ENSO with annual cycle and MJO/WWB/TIW (Madden Julian Oscillation/Westerly Wind Burst/Tropical Instability Wave) activity; (3) key nonlinear processes of ENSO involving atmospheric convective thresholds, nonlinear ocean dynamic heating, and thermocline outcropping; and (4) the impacts of climate mean-state biases/changes and perturbed physical processes on simulated ENSO and its complexity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hierarchical Ion Interactions in the Direct Air Capture of CO 2 at Air/Aqueous Interfaces

The direct air capture (DAC) of CO 2 using aqueous solvents is plagued by slow kinetics and interfacial barriers that limit effectiveness in combating climate change. Functionalizing air/aqueous surfaces with charged amphiphiles shows promise in accelerating DAC; however, insight into these interfaces and how they evolve in time remains poorly understood. Specifically, competitive ion interactions between DAC reagents and reaction products feedback onto the interfacial structure, thereby modulating interfacial chemical composition and overall function. In this work, we probe the role of glycine amino acid anions (Gly - ), an effective CO 2 capture reagent, that promotes the organization of cationic oligomers at air/aqueous interfaces. These surfaces are probed with vibrational sum frequency generation spectroscopy and molecular dynamics simulations. Our findings demonstrate that the competition for surface sites between Gly - and captured carbonaceous anions (HCO 3 - , CO 3 2 -, carbamates) drives changes in surface hydration, which in turn tunes oligomer ordering. This phenomenon is related to a hierarchical ordering of anions at the surface that are electrostatically attracted to the surface and their ability to compete for interfacial water. These results point to new ways to tune interfaces for DAC via stratification of ions based on relative surface propensities and specific ion effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. We report the proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

The eco-evolutionary role of fire in shaping terrestrial ecosystems

Fire is an inherently evolutionary process, even though much more emphasis has been given to ecological responses of plants and their associated communities to fire. Here, in this work, we synthesize contributions to a Special Feature entitled ‘Fire as a dynamic ecological and evolutionary force’ and place them in a broader context of fire research. Topics covered in this Special Feature include a perspective on the impacts of novel fire regimes on differential forest mortality, discussions on new approaches to investigate vegetation-fire feedbacks and resulting plant syndromes, synthesis of fire impacts on plant–fungal interactions, and a meta-analysis of arthropod community responses to fire. We conclude by suggesting pathways forward to better understand the ecological and evolutionary consequences of fire. These include developing ecological and evolutionary databases for fire ecology, integrating hierarchical genetic structure or phylogenetic structure, and developing new experimental frameworks that limit context-dependent outcomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

97 MATHEMATICS AND COMPUTING↗