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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Topological solitons in square-root graphene nanoribbons controlled by electric fields

Conjugational defects, also known as solitons, play an important role in the electronic, magnetic, and optical properties of materials. Understanding solitons can uncover intriguing physics and provide insights for designing quantum materials with tailored band structures and electronic properties. Here we propose a framework to create and control solitons via topological phase transitions in a class of graphene nanoribbons (GNRs) called square-root GNRs, using a transverse electric field. To demonstrate the experimental feasibility, we design and synthesize a representative GNR with a bottom-up approach, with first-principles calculations revealing topological soliton states at the domain wall induced by the electric field. In conclusion, the framework introduced in this work can potentially enable direct manipulation of solitons and provide a platform to study them systematically.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Functional Mock-up Interface - Machine Learning Center (FMI-MLC) v1

The Functional Mock-up Interface - Machine Learning Center is a tool to interface the Python-Tensorflow library with building simulation models, to develop and train machine learning-based controls. In particular, this framework allows the use of reinforcement learning through the standardized Functional Mock-up Interface, an open industry standard for co-simulation. First application was the development of a controller for dynamic façade and heating ventilation and air conditioning (HVAC) systems.

Gehbauer, Christoph↗
DOE OSTI2020

Guest-Controlled Incommensurate Modulation in a Meta-Rigid Metal–Organic Framework Material

Structural transitions of host systems in response to guest binding dominate many chemical processes. We report an unprecedented type of structural flexibility within a meta-rigid material, MFM-520, which exhibits a reversible periodic-to-aperiodic structural transition resulting from a drastic distortion of a [ZnO 4 N] node controlled by the specific host–guest interactions. The aperiodic crystal structure of MFM-520 has no three-dimensional (3D) lattice periodicity but shows translational symmetry in higher-dimensional (3 + 2)D space. In this work, we have directly visualized the aperiodic state which is induced by incommensurate modulation of the periodic framework of MFM-520·H 2 O upon dehydration to give MFM-520. Filling MFM-520 with CO 2 and SO 2 reveals that, while CO 2 has a minimal structural influence, SO 2 can further modulate the structure incommensurately. MFM-520 shows exceptional selectivity for SO 2 under flue-gas desulfurization conditions, and the facile release of captured SO 2 from MFM-520 enabled the conversion to valuable sulfonamide products. MFM-520 can thus be used as a highly efficient capture and delivery system for SO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

POWER ELECTRONICS GRID TIED SYSTEM FINAL REPORT

Across the country, electric utilities are grappling with the persistent hurdles of integrating Distributed Energy Resources (DERs). Managing these assets safely and effectively is a complex endeavor, complicated by varying ownership structures, management philosophies, and the diversity of the technologies themselves. Consequently, the industry has seen a proliferation of bespoke system designs, control strategies, and communication frameworks—forcing utilities to spend significant time and resources developing one-off integration solutions. This project addressed these integration hurdles through a scalable demonstration of intelligent devices designed to coordinate and control diverse resources in low-voltage applications. This concept minimized the need for complex integration by transforming the separate DERs into a dispatchable virtual power plant (VPP) with integrated resiliency functions (called a Node). By collaborating with a utility partner, the project focused on developing rapidly implementable use cases that bridged the gap between theoretical control and real-world deployment

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Evaluation of Data Lake Design for the Accelerator Control System

In this modern world, the user expects faster processing and real-time response to operate accelerator control devices. The existing framework with its infrastructure does not have the ability to satisfy these future needs. Therefore, modernization is required to reach industry standards and develop a modular framework that can provide flexibility and dynamic scalability. The data lake architecture, comprising three layers, ingestion, processing, and data consumption, provides flexibility and scalability to meet current and future demands for the accelerator control system.

Jaikar, Amol [Fermilab]↗
DOE OSTI2020

Scheduling and Control of Flexible Building Loads for Grid Services Based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Wu, Di↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2025

Customizable Aperture Geometry in Metal–Organic Frameworks for Kinetic Hydrocarbon Separation

Precise control of aperture dimensions is crucial in adsorptive separations of hydrocarbons, as it directly affects key parameters such as selectivity, capacity, diffusion, and recyclability. The development of metal–organic frameworks (MOFs) has enabled the fine-tuning of local pore environments to address important hydrocarbon separations. However, customizing the aperture geometry to tune the kinetic separation performance remains challenging. Here, we deploy a mixed-linker synthesis strategy, combining long and short linkers on fcu net Zr-MOFs with equilateral triangular apertures to construct isoreticular multivariate MOFs, NU-415 and NU-416, with tailored isosceles triangular apertures suitable for the separation of hexane isomers. Sorption, liquid batch separation, and X-ray diffraction measurements demonstrate significantly improved selectivity, capacity, stability, and recyclability of NU-415 and NU-416 compared with Zr-muconate and MOF-801. Notably, both NU-415 and NU-416 achieve uptake capacities of 2.2 mmol g –1 in 1 min with an n-hexane to 2,2-dimethylbutane selectivity over 200 in an equimolar ternary mixture at ambient conditions, comparable to leading reported materials. Mechanistic studies confirm that separation performance is predominantly governed by significant kinetic differences rather than by thermodynamics. Furthermore, the successful customization of aperture geometry not only enables superior linear to monobranched hexane selectivity in NU-415 but also demonstrates the mixed-linker synthesis strategy as a promising solution for precise and predictable pore architecture control in MOFs.

Adsorption↗
DOE OSTI2025

Breakdown of the Static Dielectric Screening Approximation of Coulomb Interactions in Atomically Thin Semiconductors

Coulomb interactions in atomically thin materials are remarkably sensitive to variations in the dielectric screening of the environment, which can be used to control exotic quantum many-body phases and engineer exciton potential landscapes. For decades, static or frequency-independent approximations of the dielectric response, where increased dielectric screening is predicted to cause an energy redshift of the exciton resonance, have been sufficient. These approximations were first applied to quantum wells and were more recently extended with initial success to layered transition metal dichalcogenides (TMDs). Here, we use charge-tunable exciton resonances to investigate screening effects in TMD monolayers embedded in materials with low-frequency dielectric constants ranging from 4 to more than 1000, a range of 2 orders of magnitude larger than in previous studies. In contrast to the redshift predicted by static models, we observe a blueshift of the exciton resonance exceeding 30 meV in higher dielectric constant environments. We explain our observations by introducing a dynamical screening model based on a solution to the Bethe-Salpeter equation (BSE). When dynamical effects are strong, we find that the exciton binding energy remains mostly controlled by the low-frequency dielectric response, while the exciton self-energy is dominated by the high-frequency one. Our results supplant the understanding of screening in layered materials and their heterostructures, introduce a knob to tune selected many-body effects, and reshape the framework for detecting and controlling correlated quantum many-body states and designing optoelectronic and quantum devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Digital Twin-Enabled Adaptive Control for Hydroelectric Systems: Turbine Governor and Voltage Regulation

This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗