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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spectroscopic Studies of Charge-Transfer Character and Photoresponses of F 4 TCNQ-Based Donor–Acceptor Complexes

F 4 TCNQ (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane) is used widely as a hole-doping agent in photoresponsive organic semiconducting materials, yet relatively little is known about the photoresponses of the F 4 TCNQ· - anion generated via doping. Furthermore, there is still relatively little systematic exploration of how the properties of the local material or chemical environment impacts the driving force for generating these charge-transfer complexes. Here we present spectroscopic and photophysical studies of F 4 TCNQ in charge-transfer complexes (CTCs) with the electron donor N,N’-Diphenyl-N-N’-di-p-tolylbenzene-1,4-diamine (MPDA) both in dichloroethane solution and polystyrene matrices. Integer charge transfer (ICT) between donor and acceptor occurs readily in dichloroethane solvent to form F 4 TCNQ· - :MPDA + CTCs, due to a ~150 mV difference in MPDA + /MPDA and F 4 TCNQ/F 4 TCNQ· - reduction potentials. Ultrafast spectroscopic studies of the CTC as well as electrochemically generated F 4 TCNQ· - and MDPA + reveal that the photoresponses of these CTCs are dominated by that of the dopant anion, including rapid deactivation (800 fs) after excitation to the anion D 1 excited state, followed by slower (~10 ps) vibrational cooling in the anion D 0 state. Excitation to the higher-lying D 2 state results in a rapid relaxation to the D 1 state, in contrast to direct D 2 -D 0 relaxation previously observed for F 4 TCNQ· - in gas phase. CTCs embedded in polystyrene (PS) matrices are observed to lose their integer charge-transfer character upon evaporation of solvent, as evidenced by changes to electronic and vibrational absorption features associated with F 4 TCNQ· - . This change is attributed to the loss of solvent stabilization of the ion pair formed through the charge-transfer reaction. Ultrafast spectral measurements reveal that the photoresponses of the partial charge-transfer (PCT) species embedded in PS are still highly similar to those of the ICT species and unlike that of neutral F 4 TCNQ, implying the electronic properties of the PCT state are likewise dominated by properties of the reduced acceptor molecule. We conclude that excitation of ICT or PCT states introduces optical losses for photoresponses of doped organic semiconductor materials due to the large anion absorption cross section and its rapid, dissipative deactivation dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Shafie‐Khah, Miadreza↗
DOE OSTI2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
DOE OSTI2020

A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization: Preprint

In this paper, we present a reinforcement-learning based distributed approach to wind farm energy capture maximization using yaw control, also known as wake steering. In order to maximize the power output of a wind farm, it is often necessary for individual turbines to decrease their own power output through yaw misalignment so as to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate factors that are difficult to model or changing conditions. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multi-agent environment that allows individual turbines to act so as to optimize overall wind farm output and react to unforeseen disturbances.

controls↗
DOE OSTI2020

From cellulose to 1,2,4-benzenetriol via catalytic degradation over a wood-based activated carbon catalyst

In this study, spherical P-doped activated carbon (CSP) was obtained from poplar bark by microwave carbonization with phosphoric acid as the dopant and activating agent. Phosphorus bonded with four oxygen atoms to form a phosphate-like structure rather than directly bonding with C atoms in CSP; such groups can provide Lewis acid active sites with moderately strong acidity. The Lewis acid and total acid content increased with increasing phosphoric acid doping ratio. The use of phosphoric acid as an activating agent promotes the formation of a developed porous structure. CSP 1 (mass ratio of raw material to acid = 1 : 1) had a specific surface area of 1115 m 2 g –1 with developed micro–mesoporous structures, mainly micropores. Investigation of the activity of CSP in catalytic cellulose hydrolysis during hydrothermal degradation showed that Brønsted acid sites promote hydrolysis and dehydration of cellulose, Lewis acid sites promote subsequent reactions such as isomerization, rearrangement, and oligomerization, and the developed pores can provide reaction sites and facilitate reactant transportation. The optimal reaction conditions, i.e. , 1 h at 230 °C and 1 MPa with CSP 1 (0.4 g) as the catalyst, gave a cellulose conversion of >99.9%, with 42.30% selectivity for 1,2,4-benzenetriol. This study provides an effective method for preparing 1,2,4-benzenetriol from cellulose with biomass-based activated carbon as the catalyst, which enables green synthesis of 1,2,4-benzenetriol and promotes the use of biomass waste resources.

1,2,4-benzenetriol↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2022

Unique W-Shape Y6 isomer as effective solid additive for High-Performance PM6:Y6 polymer solar cells

The current top-performing polymer solar cell (PSC) systems are mostly based on PM6:Y6 host blend. To date, numerous materials have been explored as the third component for these systems to form ternary blends or as additives. Vitrification agents are a group of additives proved to be useful in affecting the morphology of organic semiconductors. Here to design a suitable vitrification agent for non-fullerene electron acceptor Y6, an isomer strategy was explored where the thienothiophene wings of the Y6 molecule were inversed to form a W-shaped Y6 isomer of i-Y6. It was found that i-Y6 crystallized poorly with a different packing style than Y6 and could blend well in amorphous phase of Y6. These properties enabled i-Y6 to finely tune the morphology of PM6:Y6 blend at low additive dosages. The power conversion efficiency (PCE) of PM6:Y6 based PSC was raised from 16.827% to 17.433% at a dosage of Y6:i-Y6 ratio of 24:1. This work demonstrated isomerization as a viable strategy for developing solid additives for high performance PSC blends and vitrification agents as effective additives for tuning morphology and improving performances of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Light yield quenching and quenching remediation in liquid scintillator detectors

Quenching of light emission from an LAB based scintillator by the addition of organic amines and carboxylic acids is examined. Chemical functional groups of the quenching agents play an important role in this reduction. It is shown that "salt" formation at a 1:1 mole ratio in a mixed amine-acid system, reduces quenching by a factor of ~ 2. Supporting NMR spectra are presented. Finally, this "quenching neutralization" has the potential to reduce the light loss incurred when metals complexed with quenching agents are loaded into organic scintillators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Corrosion Behavior of Hydrophobic Coatings in Aqueous CO2 Environments

Internal corrosion is caused by gas impurities such as CO2, moisture, and H2S, leading to material degradation. To combat this, the National Energy Technology Laboratory (NETL) developed a novel hydrophobic coating to reduce surface hydrophobicity and enhance the corrosion resistance of steel. NETL’s approach uses multi-layer hydrophobic coatings, a novel and promising coating that could potentially revolutionize the industry's approach to internal corrosion mitigation. This work aims to investigate the corrosion performance of the hydrophobic coating and determine the water uptake. Electrochemical corrosion experiments were carried out on bare X65 carbon steel without and with coating in 3.5 wt.% NaCl saturated with CO2 at 20 °C to follow the water uptake as a function of exposure time. Linear polarization resistance (LPR) was used to determine the corrosion rate for carbon steel immersed in a NaCl electrolyte saturated with CO2. Electrochemical impedance spectroscopy (EIS) of uncoated and coated bare carbon steel was investigated. The analyses of impedance models and water uptake behaviors of hydrophobic coatings were studied for 200 hours during the corrosion process. The water uptake was estimated using the Brasher and Kingsbury relation. The results showed that the superhydrophobic coating that was developed used innovative nano-based materials to act as protection layers on the surface of metallic parts against mechanical aggressors, corrosion, and fouling agents.

corrosion behavior↗
DOE OSTI2024

Structure Development in Cross-Linked, Soybean Oil-based Waterborne Polyurethanes

Development of waterborne polyurethanes (WPU) using bio-based sources represents a step towards sustainable materials science and industry. We synthesized bio-based cationic water-dispersed crosslinked polyurethanes from high oleic soybean oil (HOSO) polyol, isophorone diisocyanate, and methyldiethanol amine, with varying ionic group contents after neutralization with acetic acid. Our primary objective was to analyze how crosslinking affects the dispersion process and film properties in multifunctional systems. The synthesis-structure-property relationship is elucidated through comprehensive analyses of the products at different stages of the synthesis. The dispersion of the WPU particles in water must occur prior to gelation during the final preparation, leading to incomplete conversion and the formation of imperfect networks. Insight into the synthesis process and polymer structure was gained by simulating polymer network parameters. Morphological analyses using synchrotron-based X-ray scattering and atomic force microscopy revealed a hierarchical structure within the WPU films. Importantly, all the films prepared in this study, without using coalescence agents, have low water absorption and high water contact angles, demonstrating their potential for textile and leather coatings and other applications.

bio-based polymers↗
DOE OSTI2024

chatHPC: Empowering HPC users with large language models

The ever-growing number of pre-trained large language models (LLMs) across scientific domains presents a challenge for application developers. While these models offer vast potential, fine-tuning them with custom data, aligning them for specific tasks, and evaluating their performance remain crucial steps for effective utilization. However, applying these techniques to models with tens of billions of parameters can take days or even weeks on modern workstations, making the cumulative cost of model comparison and evaluation a significant barrier to LLM-based application development. To address this challenge, we introduce an end-to-end pipeline specifically designed for building conversational and programmable AI agents on high performance computing (HPC) platforms. Our comprehensive pipeline encompasses: model pre-training, fine-tuning, web and API service deployment, along with crucial evaluations for lexical coherence, semantic accuracy, hallucination detection, and privacy considerations. Here, we demonstrate our pipeline through the development of chatHPC, a chatbot for HPC question answering and script generation. Leveraging our scalable pipeline, we achieve end-to-end LLM alignment in under an hour on the Frontier supercomputer. We propose a novel self-improved, self-instruction method for instruction set generation, investigate scaling and fine-tuning strategies, and conduct a systematic evaluation of model performance. The established practices within chatHPC will serve as a valuable guidance for future LLM-based application development on HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Force-triggered, Bio-based, Sealants for Prefabricated Building Components: Towards Improved Efficiency, Performance and Sustainability

The prefabricated building construction industry has made extensive progress in expediting the manufacture of prefabricated components at offsite plants. However, this progress has not translated to the assembly of the prefabricated components at the construction site. Case in point, sealing the joints between components to prevent air leaks requires the manual application of tape, caulk, or spray foam at the jobsite, and performance is highly dependent on the skills of the installer. To reduce assembly time and improve the airtightness and waterproofness of prefabricated components, we developed a sealant that can be installed at the plant and have its curing reaction triggered at the jobsite. Additionally, we used this opportunity to explore the use of bio-based feedstocks that are abundant and not used for food. We evaluated a series of force-triggered, bio-based, high strength, and fast curing sealants, consisting of a one-part heterogeneous system. These sealants are derived from formulations with ≥80% of bio-based components, consisting of a cardanol derived diepoxy that is microencapsulated in a polymer shell and embedded in a cardanol derived amine curing agent. The microcapsule shell allows separation of the reactive species in the one-part sealant allowing a fast-curing system to remain unreacted until the right trigger is applied. When the microcapsules are activated and broken by force, the highly reactive species mix and cure, exhibiting peel strengths up to 143 ppi. The open-air shelf stability of the sealant complexes was demonstrated by peel strength values of ~16 ppi when triggering the curing reaction even after being exposed for 8 months to open air and humidity. The successful on-demand triggering of curing reactions and the shelf stability provide efficacy of these force-triggered sealants for installation on prefabricated components, storage for months prior to delivery, and assembly at the jobsite. These force-triggered bio-based sealants for prefabricated buildings could result in lower installation time and cost as well as better performance than tapes and caulks at the jobsite.

Cortes Guzman, Karen [ORNL] (ORCID:000000028793468↗
DOE OSTI2021

Crystal structure of a far-red–sensing cyanobacteriochrome reveals an atypical bilin conformation and spectral tuning mechanism

Significance Phytochromes are well-known far-red-light sensors found in plants that trigger adaptive responses to facilitate competition for light capture with neighboring plants. Red- and far-red sensing are also critical for cyanobacteria living in the far-red–enriched shade of plants. This work reports the crystal structure of a far-red–sensing cyanobacteriochrome, a distant cyanobacterial relative of phytochrome. These studies shed insights into the molecular basis of far-red-sensing by phycobilin-based photoreceptors. Owing to the deep tissue penetration of far-red light, far-red–sensing cyanobacteriochromes are promising protein scaffolds for developing genetically encoded photoswitches, optoacoustic contrast agents, and fluorescent probes for in situ imaging and optogenetic applications.

59 BASIC BIOLOGICAL SCIENCES↗