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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Local stress within a granular molecular solvent matrix, a mechanism for individual ion hydration

The activity of water is important in many natural systems and directed processes. To better understand water activity, the hydration of individual ions is approximated for a recently developed speciation-based solution model. Here a matrix of values for hydration of complete salts (anion and cation) was used as source data to calculate individual ion hydration values. Some electrolytes were excluded from calculations of individual ion hydration due to the prevalence of ion pairing and resulting liberation of water upon ion pairing. Evaluation of the periodic trends emerging from individual ion hydration values suggests that ionic volume, relative to the volume of molecular water, dictates both modeled hydration requirements (related to vapor-liquid-equilibrium-derived activity) as well as properties historically described by hydrodynamic radius (ion diffusion and solution viscosity). When water is conceptualized as a granular three-dimensional matrix, solutes may function as substitutional defects (dilation or constriction) inducing local compressive or tensile stress. These results have implications on the mechanism of the Hofmeister series and series reversals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Topological Relationship–Based Flow Direction Modeling: Mesh–Independent River Networks Representation

River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model's spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model's spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship-based river network representation method for spatially distributed hydrologic models. This novel method uses (a) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (b) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (a) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (b) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Discrete modeling of a transformer with ALEGRA

We report progress on a task to model transformers in ALEGRA using the “Transient Magnetics” option. We specifically evaluate limits of the approach resolving individual coil wires. There are practical limits to the number of turns in a coil that can be numerically modeled, but calculated inductance can be scaled to the correct number of turns in a simple way. Our testing essentially confirmed this “turns scaling” hypothesis. We developed a conceptual transformer design, representative of practical designs of interest, and that focused our analysis. That design includes three coils wrapped around a rectangular ferromagnetic core. The secondary and tertiary coils have multiple layers. The tertiary has three layers of 13 turns each; the secondary has five layers of 44 turns; the primary has one layer of 20 turns. We validated the turns scaling of inductance for simple (one-layer) coils in air (no core) by comparison to available independent calculations for simple rectangular coils. These comparisons quantified the errors versus reduced number of turns modeled. For more than 3 turns, the errors are <5%. The magnetic field solver failed to converge (within 5000 iterations) for >10 turns. Including the core introduced some complications. It was necessary to capture the core surfaces in thin grid sheaths to minimize errors in computed magnetic energy. We do not yet have quantitative benchmarks with which to compare, but calculated results are qualitatively reasonable.

42 ENGINEERING↗
DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗
DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CRCNS US-France Research Proposal: Collaborative Research: Encoding reward expectation in Drosophilia

The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory. Early and most current studies use basic behavior conditioning protocols to study learning in controlled laboratory settings. More recently, the ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. In fact, the connectome has revealed many types of new connections that had until now been overlooked. Within this context, the thesis of this proposal is that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, such as have been developed in vertebrates, and combining those studies with information from the connectome guided by computational modelling. We propose to push beyond the boundaries of existing conditioning protocols for fruit flies to investigate more complex memory representations. In particular, we will investigate the function of reinforcement pathways in relation to the absence of expected reinforcement. More specifically, we propose a series of experiments designed to investigate the memory representations in fruit flies when an expected consequence of a Conditioned Stimulus (CS) fails to occur. Although studies have evaluated how this failure can establish extinction memory for the CS, our studies will go beyond studying extinction. Specifically, we predict that in Drosophila when a CS is associated with a failed expectation of an appetitive food reinforcement it will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. We combine these studies with manipulations of reinforcement pathways in the CNS inspired from the connectome, iteratively knitted in with established computational models. Intellectual Merit: The concept of reinforcement expectation and incentive contrast have been influential in the development of studies of associative learning in mammals. These questions are particularly challenging to answer in vertebrates because they require exquisite cellular, temporal, and genetic specificity of experimental manipulations. The recent development of work with identified neurons and their connectomes makes the larval and adult fly brains ripe as models for pushing our understanding of neural bases for these higher- order conditioning phenomena. Broader Impacts: Public health: These analyses and the conceptual framework of prediction error processing underlying them have a profound impact on our understanding of reinforcement-related behavior in humans, including monetary rewards and the mnemonic consequences of traumatic experiences, and for pathologies of the dopamine reinforcement system. Educational: This project will provide interdisciplinary training for postdoctoral researchers, Ph.D. and undergraduate students. The PIs will act as co-supervisors or mentors of students working in the different labs via face-to-face and internet-based technologies. We will also work with ASU’s award-winning Ask- A-Biologist program. This is an online science program designed to enrich the learning experiences of students of all ages and to provide classroom material for use by K-12 teachers. We will develop an extension of a game developed under a prior NSF award, and the new game will include modules to teach K-12 students about how insects learn. We will also integrate into the AAB site a program developed by a collaborator (B Gerber) at the Leibniz Institut für Neurobiologie, Magdeburg, and now in use in schools in Germany, to teach K-12 students how to train animals using the fruit fly larval learning paradigm. Underrepresented groups: All PIs will work with their university offices of Academic Diversity and Equal Opportunity for reaching underrepresented students.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Particle–Continuum Coupling and its Scaling Regimes: Theory and Applications

Abstract This work is motivated by the goal of designing simulation software for technical devices that, at their functional core, rely on atomistic‐scale processes embedded in a larger‐scale fluid environment. The core of the problem is the conceptual and technical approach for coupling particle and continuum representations of a fluid. The state of the art for key aspects including physical modeling, mathematical formalization, computational implementation, and applications, is discussed and organized in a consistent picture across the relevant physical regimes.

Delle Site, Luigi↗
DOE OSTI2026

Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry

Accurate and efficient gradients of molecular energy with respect to nuclear degrees of freedom are essential for geometry optimization and molecular dynamics, including simulations that go beyond the Born–Oppenheimer regime. A common approach involves deriving analytical formulas for new electronic structure methods, which is often conceptually difficult and requires tedious coding. Here, we implement analytical, semi-numerical, and automatic differentiation (AD)-based gradient pathways for semiempirical Hamiltonian models in the PYSEQM software package, leveraging both graphics processing unit (GPU) and central processing unit (CPU) architectures. We further extend these capabilities to excited states calculated using the configuration interaction singles and time-dependent Hartree–Fock ansätze. We benchmark wall time, peak memory usage, and accuracy across three molecular families of varying chemical complexity, including systems of up to a thousand atoms. For ground-state simulations, analytical and AD gradients achieve near-identical GPU runtimes, while semi-numerical gradients are slower on GPU but remain competitive on CPU. For excited states, both analytical and custom AD approaches using implicit differentiation show similar performance and low memory requirements, whereas gradients with full AD are memory-limited. AD gradients match analytical ones in accuracy across all tested systems, aided by a quaternion-based diatomic frame rotation for two-center quantities that ensures smooth energy surfaces. Overall, automatic differentiation emerges as a practical alternative to analytical gradients in semiempirical quantum chemistry, offering high accuracy while allowing seamless integration in AI-driven workflows and popular packages, such as PyTorch and JAX. Our results provide actionable guidance for selecting optimal gradient strategies in large-scale ground- and excited-state molecular dynamics simulations.

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

Advanced Gas Turbine and sCO 2 Combined Cycle Power System (Final Report)

The team of Southwest Research Institute (SwRI), Hanwha Power Systems Americas (HPSA), and Solar Turbines Incorporated (Solar) has worked to develop a modular, highly efficient combined-cycle power system consisting of a supercritical carbon dioxide (sCO 2 )-based waste-heat recovery system (WHRS) coupled to a Solar Titan 130 gas turbine (GT). During Phase 1 of the project, the project team used advanced thermodynamic cycle and techno-economic modeling to find an optimal sCO 2 WHRS bottoming cycle and machinery configuration for a Solar Titan 130. In addition to this, a conceptual design of the machinery and overall system layout was created and technology gaps were identified.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

DME-Propane Ignition Delay Time Measurements at Mixing Controlled Compression Ignition Engine-Relevant Conditions

The blend of dimethyl ether (DME, CH 3 OCH 3 ) and propane (C 3 H 8 ) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Undiluted experiments spanning temperatures of 700 to 1100 K and pressures of 55 to 84 bar for various blends (100% CH 3 OCH 3 / 0% C 3 H 8, 100% C 3 H 8 / 0% CH 3 OCH 3 , 60% CH 3 OCH 3 / 40% C 3 H 8 ) of DME and propane were combusted in synthetic air (21% O 2 / 79% N 2 ). Some experiments were conducted at higher pressures (90-120 bar) to understand model performance at these conditions. Comparisons of IDT were made with the predictions of recent chemical kinetic mechanisms for DME-propane mixture, including the Aramco3.0, NUIG, and Dames et al. mechanisms. All mechanisms overpredicted IDT compared to experimental values. Sensitivity analysis was conducted with Dames et al. model, and critical reactions sensitive to IDT of DME-propane mixture near 100 bar are outlined.

Mohammed, Zuhayr Pasha↗
DOE OSTI2022

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Global root traits (GRooT) database

Motivation: Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides readyto- use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks. Main types of variables contained: GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain: Global coverage with data from arid, continental, polar, temperate and tropical biomes. Data on root traits were derived from experimental studies and field studies. Time period and grain: Data were recorded between 1911 and 2019. Major taxa and level of measurement: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values. Software format: GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SHOCK TUBE IGNITION STUDIES OF RENEWABLE DIESEL FUELS FOR MEDIUM AND HEAVY-DUTY TRANSPORTATION

Currently extensive research on alternative fuels is being conducted due to their increasing demand to reduce greenhouse emissions. One renewable fuel studied in this work is dimethyl ether (DME) blended with propane(C3H8) as a potential mixture for heavy-duty engines used in semi-trucks. The blend has the potential to drastically reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. To develop the use of mixture, one must conduct detailed conceptual and simulation studies before progressing to detail studies in CFD, engine modifications, and live testing. For simulations, accurate high-fidelity chemical kinetic models are necessary. However, the validity of the chemical kinetic mechanism for operating conditions of a heavy-duty mixing-controlled compression (MCCI) engine was widely unknown until recent work presented here and published. In this work, we studied the ignition of DME and propane blends in a shock tube under MCCI engine conditions. Ignition delay time (IDT) gathered behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Testing was conducted for undiluted varieties spanning from temperatures of 700 to 1100 K at pressures ranging from 55 to 84 bar for various blends (100% CH3OCH3, 100% C3H8, 60% CH3OCH3/ 40% C3H8) of DME and propane were combusted in synthetic air (21% O2/ 79% N2). Several experiments were conducted at higher pressures (90-120 bar) to improve the model performance and accuracy. The ignition delay times (IDTs) were compared to recent mechanisms, including Aramco3.0, NUIG, and Dames et al. A common trend among the mechanisms was overpredicted experimental IDTs. Further studies were conducted by a sensitivity analysis using the Dames et al. model, and critical reactions sensitive to IDTs of DME-propane mixture near 60 bar are outlined. Chemical analysis was conducted on the NTC region to explain chemical kinetics which is critical for developing MCCI heavy duty engines.

Mohammed, Zuhayr Pasha↗
DOE OSTI2020

Multi-Physics Simulations of Molten Chloride Fast Reactor using Nek5000 and PROTEUS-NODAL

This report documents the FY20 work to demonstrate a specific multi-physics capability within NEAMS tools to model molten chloride fast reactors (MCFR). Specifically, the CFD code Nek5000 and the neutronics code PROTEUS-NODAL were coupled off-line to calculate the delayed neutron precursor distribution within the flowing fuel salts in an MCFR core. An MCFR benchmark problem and a 3D test problem based on a conceptual MSR design were successfully solved through the coupled simulation of Nek5000 and PROTEUS-NODAL. In the coupled simulations for these test problems, Nek5000 provided high-quality flow field information, and PROTEUS-NODAL performed the neutron diffusion calculation along with the precursor drift model. The impact of precursor drift on the core status was also investigated. Additionally, the precursor drift model implemented in PROTEUS-NODAL was verified by making use of the reference precursor distribution generated from Nek5000. Results of these test problems suggest that the multi-physics capability of Nek5000 and PROTEUS-NODAL was properly developed for tracking delayed neutron precursor drift.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 1. Characterizing Thermal–Hydrological–Mechanical Processes

Abstract Bentonite clay is widely used in engineered barrier systems for the permanent disposal of high‐level radioactive waste due to its low permeability, high swelling capacity, and thermal stability. However, the complex thermal‐hydrological‐mechanical (THM) processes induced by heating from decaying radioactive waste and hydration from surrounding rock can lead to heterogeneous changes that are difficult to measure and predict. This study develops an Electrical Resistivity Tomography (ERT)‐based framework for monitoring THM processes, progressing from sample‐scale to bench‐scale tests, to inform field‐scale applications. Sample‐scale tests analyzed small bentonite samples under controlled variations in water content, temperature, and porosity to establish fundamental resistivity relationships. Bench‐scale tests involved larger bentonite columns subjected to heating (up to 200°C) and hydration under controlled pressure, simulating repository conditions. ERT measurements, complemented by X‐ray CT imaging, temperature monitoring, and tracing sensors, revealed coupled THM processes, such as hydration‐induced compression, swelling, and thermal gradients, leading to complex resistivity patterns. The results demonstrate the potential of ERT for capturing THM‐induced resistivity changes, though challenges remain in upscaling and quantitative analysis. This study evaluates laboratory test capabilities and proposes future improvements for understanding THM‐induced resistivity responses. A conceptual framework for ERT implementation in field‐scale monitoring is presented, synthesizing findings from both scales and exploring how ERT data can inform long‐term modeling and reduce prediction uncertainties. Overall, this ERT‐based framework offers a robust method for monitoring bentonite buffers, aiding in early issue detection and supporting the safe long‐term disposal of radioactive waste in geological repositories, while highlighting the need for future development. Plain Language Summary Bentonite clay is crucial in engineered barrier systems (EBS) for containing high‐level radioactive waste due to its ability to absorb water, swell, seal and remain stable under high temperatures. When bentonite absorbs water and heats up from radioactive decay, it experiences complex changes in its physical and mechanical properties. Understanding these changes is important for ensuring the long‐term safety and effectiveness of EBS. This study used Electrical Resistivity Tomography (ERT), a non‐invasive method that measures electrical conductivity to monitor these changes during laboratory experiments. The ERT data revealed significant variations in resistivity corresponding to changes in water content, temperature, and density, providing detailed spatial and temporal insights into the behavior of bentonite. These findings enhance our ability to predict the long‐term performance of bentonite barriers, ensuring the safe containment of radioactive waste. By improving our understanding of bentonite's behavior, this research supports the development of more reliable and effective barrier systems for radioactive waste disposal, protecting the environment and public health. Key Points ERT monitoring was employed to capture resistivity changes in bentonite during controlled heating and hydration experiments, providing insights into THM processes ERT data reveal significant resistivity changes correlated with water content, temperature, and mechanical effects, enhancing the understanding of THM dynamics in bentonite This study explores the potential of the framework for application in field‐scale EBS monitoring, emphasizing the need for integrating additional geophysical methods for comprehensive subsurface imaging

Chen, Hang↗
DOE OSTI2025

When do molecular polaritons behave like optical filters?

This review outlines several linear optical effects featured by molecular polaritons arising in the collective strong light–matter coupling regime. Under weak laser irradiation and when the single-molecule light–matter coupling can be neglected (often in the limit when the number of molecules per photon mode is large), we show that the excited-state molecular dynamics under collective strong coupling can be exactly replicated without the cavity using a shaped (or “filtered”) laser, whose field amplitude is enhanced by the cavity quality factor, shining on the bare molecules. As a consequence, the absorption within a cavity can be understood as the overlap between the polariton transmission and the bare molecular absorption, suggesting that polaritons act in part as optical filters. This framework demystifies and provides a straightforward explanation for a large class of experiments and theoretical models in molecular polaritonics, highlighting that the same effects can be achieved without the cavity with shaped laser pulses. With a few modifications, this simple conceptual picture can also be adapted to understand the incoherent nonlinear response of polaritonic systems. This review establishes a clear distinction between polaritonic phenomena that can be fully explained through classical linear optics and those that require a quantum electrodynamics approach. It also highlights the need to differentiate between effects that necessitate polaritons (i.e., hybrid light–matter states) and those that can occur in the weak coupling regime. Here, we further discuss that certain quantum optical effects like fluorescence can be partially described as optical filtering, whereas some others like cavity-induced Raman scattering go beyond this. Further exploration in these areas is needed to uncover novel polaritonic phenomena beyond optical filtering.

Schwennicke, Kai [University of California, San Di↗
DOE OSTI2025

Turbulence suppression at extreme plasma densities on DIII-D and EAST

Recent high-poloidal-beta (high-βP) experiments on DIII-D and EAST have made coordinated breakthroughs for high confinement quality at high density near the Greenwald limit. Density gradient amplification of turbulence suppression at high βP can explain both of these achievements. Experiments on DIII-D have achieved Greenwald fraction (fGr = line-averaged density/Greenwald density) above 1 simultaneously with normalized energy confinement (H98y2) around 1.5, as required in fusion reactor designs but never before verified in tokamak experiments with the divertor configuration. A synergy between increased H98y2 and fGr is observed with strong gas puffing, due to the build-up of an internal transport barrier at large radius in the temperature and density channels. Transport simulations reveal that the favorable trend of reduced turbulent energy transport at higher density is only expected when increasing the density gradient at high local safety factor and high β, thus at high βP to ensure strong α-stabilization. These conditions are crucial to many conceptual designs for steady-state reactors. New experiments on EAST have nearly doubled the ion temperature at fGr ∼ 0.9, consistent with predict-first modeling results based on the same physics revealed from the DIII-D analysis. All previous EAST long-pulse H-modes have Ti ≪ Te near plasma axis. Transport modeling indicates that the profiles are limited by ion-temperature-gradient modes at mid-radius. The modeling also suggested potential solutions, including reducing magnetic shear, enhancing density gradients, and higher impurity concentration. Following this guidance, EAST experiments directly show a strong enhancement of Ti achieved with a combination of a second plasma current ramp-up, a density gradient increase, and a Zeff perturbation by a short pulse (100 ms) of impurity injection, as predicted by the earlier modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗