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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Novel Dynamic Polymer Synthesis via Chlorinated Solvent Quenched Depolymerization

Dynamic polymers with both physical interactions and dynamic covalent bonds exhibit superior performance while achieving such dry polymers in an efficient manner remains a challenge. Herein, we report a novel organic solvent quenched polymer synthesis using a natural molecule, i.e., thioctic acid (TA), which has both dynamic disulfide bond and carboxylic acid. The effects of the solvent types and concentrations along with reaction times on the proposed reaction have been thoroughly explored for polymer synthesis. Solid-state proton nuclear magnetic resonance ( 1 H NMR) and first-principles simulations are carried out to investigate the reaction mechanism. It shows that the chlorinated solvent can efficiently stabilize and mediate the depolymerization of poly(TA) which is more kinetically favorable upon lowering the temperature. Attributed to the numerous dynamic covalent disulfide bonds and noncovalent hydrogen bonding, the obtained poly(TA) shows high extensibility, self-healable and re-processable properties. It can also be employed as an efficient adhesive even on a Teflon surface and 3D printed using the fused deposition modeling (FDM) technique. This new polymer synthesis approach on using organic solvents as catalysts along with the unique reaction mechanism provides a new pathway for efficient polymer synthesis, especially for those multi-functional dynamic polymers.

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

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗
DOE OSTI2020

Resilient Autonomous Wind Farms

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm level, taking into account individual wind turbine information to achieve wind-farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have experienced significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms that can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as from local meteorological stations, nearby radars, sodars, and lidars, and supervisory control and data acquisition (SCADA) data. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and ensuring more efficient overall control of farmwide optimizations through mechanisms such as wake steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach that distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks that seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft-Byzantine-Fault-Tolerant algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will...

fault tolerance↗
DOE OSTI2020

Plasmon Energy Transfer in Hybrid Nanoantennas

Plasmonic metal nanoparticles exhibit large dipole moments upon photoexcitation and have the potential to induce electronic transitions in nearby materials, but fast internal relaxation has to date limited the spatial range and efficiency of plasmonic mediated processes. In this work, we use photo-electrochemistry to synthesize hybrid nanoantennas comprised of plasmonic nanoparticles with photoconductive polymer coatings. Here, we demonstrate that the formation of the conductive polymer is selective to the nanoparticles and that polymerization is enhanced by photoexcitation. In situ spectroscopy and simulations support a mechanism in which up to 50% efficiency of nonradiative energy transfer is achieved. These hybrid nanoantennas combine the unmatched light-harvesting properties of a plasmonic antenna with the similarly unmatched device processability of a polymer shell.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Indoor air quality in California homes with code-required mechanical ventilation

Data were collected in 70 detached houses built in 2011-2017 in compliance with the mechanical ventilation requirements of California's building energy efficiency standards. Each home was monitored for a 1-week period with windows closed and the central mechanical ventilation system operating. Pollutant measurements included time-resolved fine particulate matter (PM 2.5 ) indoors and outdoors and formaldehyde and carbon dioxide (CO 2 ) indoors. Time-integrated measurements were made for formaldehyde, NO 2 , and nitrogen oxides (NO X ) indoors and outdoors. Operation of the cooktop, range hood, and other exhaust fans was continuously recorded during the monitoring period. Onetime diagnostic measurements included mechanical airflows and envelope and duct system air leakage. All homes met or were very close to meeting the ventilation requirements. On average, the dwelling unit ventilation fan moved 50% more airflow than the minimum requirement. Pollutant concentrations were similar to or lower than those reported in a 2006-2007 study of California new homes built in 2002-2005. Mean and median indoor concentrations were lower by 44% and 38% for formaldehyde and 44% and 54% for PM 2.5 . Ventilation fans were operating in only 26% of homes when first visited, and the control switches in many homes did not have informative labels as required by building standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using electron channeling contrast imaging to inform and improve the growth of high-efficiency GaAs solar cells on nanopatterned GaAs substrates

Patterned substrates provide opportunities for reducing the cost of high-efficiency III-V devices by incorporating mechanically weak layers beneficial for substrate reuse (e.g. by spalling). In this work, the functionality of electron channeling contrast imaging (ECCI) as a tool to efficiently understand and mitigate defect formation is exemplified by developing a process in which high-quality III-V material can be grown on nanopatterned GaAs substrates. Reactive ion etching used in the patterning process was found to damage the GaAs substrate surface, leading to the formation of stacking faults in the epitaxial material as observed by ECCI. Etching the patterned substrates in a 1 NH4OH: 1 H2O2: 50 DI H2O solution for 10 s prior to growth removed the substrate surface damage and stacking faults were no longer present. Growth of solar cell device structures initially produced samples with many macroscale flaws creating shunts in the devices, which complicated the assessment of material quality by device measurements. However, ECCI revealed that the epitaxial material surrounding macroscale flaws was free from any crystallographic defects such as stacking faults and threading dislocations. With this knowledge, we focused on refining the patterning process to eliminate the macroscale flaws. Solar cells were then grown on the improved nanopatterned substrates and exhibited device structures with defect densities less than 5 x 105 cm -2 and average conversion efficiency of 24.8%, nearly identical to devices grown on unpatterned epi-ready substrates (25.0%).

14 SOLAR ENERGY↗
DOE OSTI2022

Reverse β-oxidation pathways for efficient chemical production

Abstract Microbial production of fuels, chemicals, and materials has the potential to reduce greenhouse gas emissions and contribute to a sustainable bioeconomy. While synthetic biology allows readjusting of native metabolic pathways for the synthesis of desired products, often these native pathways do not support maximum efficiency and are affected by complex regulatory mechanisms. A synthetic or engineered pathway that allows modular synthesis of versatile bioproducts with minimal enzyme requirement and regulation while achieving high carbon and energy efficiency could be an alternative solution to address these issues. The reverse β-oxidation (rBOX) pathways enable iterative non-decarboxylative elongation of carbon molecules of varying chain lengths and functional groups with only four core enzymes and no ATP requirement. Here, we describe recent developments in rBOX pathway engineering to produce alcohols and carboxylic acids with diverse functional groups, along with other commercially important molecules such as polyketides. We discuss the application of rBOX beyond the pathway itself by its interfacing with various carbon-utilization pathways and deployment in different organisms, which allows feedstock diversification from sugars to glycerol, carbon dioxide, methane, and other substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Microwave-Assisted Gasification of Biochar: Effect of Operational Parameters and Biochar Composition on Syngas Production

The utilization of microwave-assisted gasification for biomass/plastic is a promising route toward clean energy production, contributing to a reduction in carbon footprint. This method facilitates the conversion of biomass into syngas with enhanced hydrogen (H2) yield, surpassing conventional heating approaches. However, the gasification of biochar, a byproduct resulting from the initial rapid pyrolysis of biomass, appears as a rate-determining step in biomass gasification. To ensure high conversion efficiency, particularly in pilot or larger scales, the maintenance of high biochar reactivity is essential, which can be achieved by introducing catalysts to minimize biochar formation. Furthermore, given the susceptibility of biochar to microwave heating, gaining a comprehensive understanding of its behavior in the presence of microwave-active catalysts under microwave conditions is crucial to obtain valuable insights into the underlying mechanisms, thereby improving overall biomass gasification efficiency. In the previous study, magnetite (Fe3O4) was selected for microwave-assisted gasification for biomass/plastic due to its dual role as a catalyst and microwave absorber and demonstrated considerably enhanced hydrogen production. Herein, Fe3O4 is rationally modified with metal promoters and their synergistic effect toward biochar gasification performance is investigated. The data shows that metal promoted Fe3O4 shows a higher syngas yield than that of pristine Fe3O4 in biochar.

Mai, Duy Hien↗
DOE OSTI2023

Accelerating Manufacturing of Powder-Processed Strong, Lightweight and Thermally Stable Bulk Nanocrystalline Al Alloys via Engineered Interface States

The overarching goal of this program is to develop an integrated materials by design framework enabling a simple, scalable, and efficient manufacturing route for thermally stable nanocrystalline Al-based alloys with high specific strength over a range of service temperatures. The central scientific principle underpinning this program is the elucidation, and ultimately implementation in novel alloy design, of unique interfacial structures and chemistries driven by segregation in multi-component systems that give rise to unprecedented processability and properties. This program targeted nanocrystalline Al-based ternary alloys produced by mechanical alloying of powders followed by compaction and sintering with hot pressing, which are inherently scalable and inexpensive manufacturing routes, but when applied to nanocrystalline alloys are usually plagued by low density and/or poor retention of the intended nanostructures during processing. This program successfully demonstrated manufacturing of a bulk (>1 cm 3 ) nanocrystalline Al-based alloy with a combination of thermal stability, high temperature strength retention, and specific strength that exceeds the current-state-of-the-art of Al alloys. It is anticipated that the greatest technological impact will occur by demonstration of rapid and scalable manufacturing of bulk nanocrystalline Al alloys with exceptional thermal stability and mechanical performance, thus enabling increased energy efficiency in high performance power generation systems via: (i) replacement of existing components with lightweight Al-based alloys and (ii) increasing operating temperatures with property retention. The program focused on the development of a simple and efficient manufacturing route for thermally stable nanocrystalline Al-based alloys with high specific strength over a range of service temperatures. The program is expected to have direct impact on scalable and economic manufacturing of bulk nanocrystalline alloys. This program profited from the recent discovery by the project team of segregation-driven amorphous intergranular films in nanocrystalline metallic alloys that can be kinetically frozen-in at sub-solidus temperatures. These interfacial states (also known as amorphous complexions) comprise a large fraction of the total material in nanocrystalline alloys and are responsible for two beneficial characteristics that anchor the proposed work: (1) activated sintering leading to high density and (2) retained nanostructures leading to high strengths that approach a substantial fraction of the theoretical limit. More specifically, these engineered interfaces led to a three-fold benefit: (i) high-throughput materials processing with low energy budget and cost, (ii) exceptional thermal stability, and (iii) high specific strength at both room temperature and intermediate temperatures experienced during service.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Supercritical Reforming of Wet Ethanol for High Efficiency Direct-injection Heavy-duty Compression-ignition Engines

The purpose of the research was to investigate the potential of using a mature bio-fuel in heavy-duty compression ignition engines. By co-optimizing both the fuel characteristics and engine system the potential for a superior outcome was demonstrated. The use of wet bio-ethanol eliminates the majority of the energy intensive distilling and dehydrating fuel production processes, which moves the fuel towards carbon neutral and also lowers the fuel costs. The relatively high water content of the resulting fuel is leveraged in the proposed novel combustion system by incorporating an integrated high efficiency exhaust waste heat recovery system. This results in significantly higher thermal efficiency. In addition the combustion system features low criteria pollutant emissions and the potential to reduce the initial cost of the engine system hardware. Substantial societal benefits are demonstrated through the co-optimization of the fuel and engine system. A computational proof-of-concept study has been performed to demonstrate the potential benefits of a novel wet ethanol heavy-duty compression ignition combustion system featuring integrated exhaust waste heat recovery. A combined in-cylinder closed cycle 3D computational fluid dynamics (CFD) - 1D engine system simulation approach was used. The models were validated to baseline engine data using diesel fuel and then applied to the wet ethanol study. The original concept was to maximize exhaust waste heat recovery through supercritical reforming of the wet ethanol fuel. Phase I simulation results indicated that the optimal solution for maximum engine efficiency gains were realized through maximizing thermo-mechanical recuperation with negligible fuel reformation. The results show the potential to achieve impressive gains in brake thermal efficiency (BTE) over the base diesel engine. The potential to increase BTE up to 20.9% over the base diesel engine was demonstrated, with even larger gains possible through reduced in-cylinder heat transfer losses. The majority of the efficiency gains were realized through integrated high efficiency exhaust waste heat recovery. The concept also has the potential to achieve future ultra-low NO x emissions standards and negligible engine-out soot emissions. The mixing controlled combustion of high temperature wet ethanol features relatively low engine-out NO x emissions without the need for exhaust gas recirculation (EGR). The soot free combustion enabled by the relatively high oxygen content of ethanol also allows for the use of stoichiometric mixing controlled combustion, which is not practical with diesel fuel. When stoichiometric combustion is used a simple passive 3-way catalyst can be used for exhaust emissions after-treatment and near zero tailpipe emissions. The Phase I simulations results have defined the system layout and requirements in preparation for the Phase II experimental proof-of-concept study. The potential applications of the research include most current applications of diesel engines. The Phase I study focused on heavy-duty on-highway class 8 trucks. However, virtually any application that requires highly efficient clean power generation would benefit from the novel engine system proposed. The results indicate substantial fuel cost savings and reduced greenhouse gas emissions with similar or reduced initial system hardware costs compared to modern diesel engine systems.

09 BIOMASS FUELS↗
DOE OSTI2024

Defining Reactivity–Deconstructability Relationships for Copolymerizations Involving Cleavable Comonomer Additives

The incorporation of cleavable comonomers as additives into polymers can imbue traditional polymers with controlled deconstructability and expanded end-of-life options. The efficiency with which cleavable comonomer additives (CCAs) can enable deconstruction is sensitive to their local distribution within a copolymer backbone, which is dictated by their copolymerization behavior. While qualitative heuristics exist that describe deconstructability, comprehensive quantitative connections between CCA loadings, reactivity ratios, polymerization mechanisms, and deconstruction reactions on the deconstruction efficiency of copolymers containing CCAs have not been established. Here, in this work, we broadly define these relationships using stochastic simulations characterizing various polymerization mechanisms (e.g., controlled/living, free-radical, and reversible ring-opening polymerizations), reactivity ratio pairs (spanning 2 orders of magnitude between 0.01 and 100), CCA loadings (2.5% to 20%), and deconstruction reactions (e.g., comonomer sequence-dependent deconstruction behavior). We show general agreement between simulated and experimentally observed deconstruction fragment sizes from the literature, demonstrating the predictive power of the methods used herein. These results will guide the development of more efficient CCAs and inform the formulation of deconstructable materials.

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

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Incremental Threshold Scheme Enabled IoT Group Key Management

Cyber landscape evolves rapidly. Internet of Things (IoT) and Edge Computing (EC) have rapidly become an integral part of the modern computing infrastructure. It is expected that there will be more than 50 billion active and connected IoT devices by 2025 [1]. Pervasive IoT/EC creates unprecedented opportunities bridging the gap between previously segregated cyber and physical spaces. However, this progress also brings along new security challenges. IoT devices typically have limited computation, communication, and storage resources. This leads to security architecture designs such as using symmetric keys for group communication. While secure and efficient in stable network settings, symmetric key solutions are ill-adapted for IoT's highly dynamic device mobility behavior and frequent group membership turnover. Whenever IoT members leave a group, the known symmetric keys cannot be made forgotten, posing a serious vulnerability. This leads to frequent re-groupings that require expensive re-authentication, key regeneration, and key redistribution in order to maintain IoT/EC security. We present a novel symmetric key management framework that integrate an Incremental Threshold Scheme (ITS) cryptographical function into communication protocol's key rotation mechanism to allow for secure and efficient symmetric key communication group member node revocation. This ITS-enabled key management framework alleviates the need of frequent and expensive re-grouping and re-keying needed by today's large and dynamic IoT/EC operations. We further applied this ITS-enabled key management framework to a distributed IoT/EC-integrated publish and subscribe framework for applicability validation.

Li, Mingyan↗
DOE OSTI2025

Dynamical Sketching for Enhanced Communication Efficiency in Federated Learning

Federated learning (FL) has revolutionized distributed machine learning by enabling collaborative model training without sharing local data. However, communication efficiency and privacy guarantees remain significant challenges. This paper introduces a dynamic sketching mechanism in FL, optimizing the trade-off between communication efficiency and model accuracy. By dynamically selecting the sketch matrix size, our approach adapts to the evolving characteristics of the data and the model, ensuring optimal performance across diverse scenarios. We leverage Bayesian optimization to systematically tune the sketch parameters, achieving an effective balance between resource efficiency and model performance. Experimental results on the MNIST dataset using a convolutional neural network (CNN) architecture validate the proposed method's efficiency and scalability. Our dynamic sketching approach significantly outperforms fixed-size sketching techniques, achieving higher compression ratios (up to 62x) and providing better privacy guarantees while maintaining high model accuracy. These findings highlight the robustness and versatility of our approach and make it a valuable solution for privacy-preserving, communication-efficient federated learning.

Afrose, Sharmin [ORNL]↗
DOE OSTI2021

Mechanistic investigation of a visible light mediated dehalogenation/cyclisation reaction using iron( iii ), iridium( iii ) and ruthenium( ii ) photosensitizers

The mechanism of a visible light-driven dehalogenation/cyclization reaction was investigated using ruthenium(II), iridium(III) and iron(III) photosensitizers by means of steady-state photoluminescence, time-resolved infrared spectroscopy, and nanosecond/femtosecond transient absorption spectroscopy. The nature of the photosensitizer was found to influence the product distribution such that the dehalogenated, non-cyclized products were only detected for the iron photosensitizer. Strikingly, with the iron photosensitizer, large catalytic yields required a low dielectric solvent such as dichloromethane, consistent with a previous publication. This low dielectric solvent allowed ultrafast charge-separation to outcompete geminate charge recombination and improved cage escape efficiency. Finally, the identification of reaction mechanisms unique to the iron, ruthenium, and iridium photosensitizer represents progress towards the long-sought goal of utilizing earth-abundant, first-row transition metals for emerging energy and environmental applications.

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

Photon pumping in a weakly-driven quantum cavity–spin system

Highlights: • We study photon frequency conversion in a driven spin coupled to a cavity mode. • Quantized frequency conversion is excpected in the strong-drive adiabatic limit. • A new photon pumping effect is established in the accessible weak drive regime. • Pumping is linked to the delocalization of the corresponding Floquet states. • Quantum coherence is preserved in both the strong and ultraweak-drive limits. We investigate the photon pumping effect in a topological model consisting of a periodically driven spin-1/2 coupled to a quantum cavity mode out of the adiabatic limit. In the strong-drive adiabatic limit, a quantized frequency conversion of photons is expected as the temporal analog of the Hall current. We numerically establish a novel photon pumping phenomenon in the experimentally accessible nonadiabatic driving regime for a broad region of the parameter space. The photon frequency conversion efficiency exhibits strong fluctuations and high efficiency that can reach up 80% of the quantized value for commensurate frequency combinations. We link the pumping properties to the delocalization of the corresponding Floquet states which display multifractal behavior as the result of hybridization between localized and delocalized sectors. Finally we demonstrate that the quantum coherence properties of the initial state are preserved during the frequency conversion process in both the strong and ultra-weak-drive limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Analysis of THz generation by multicolor laser pulses with various frequency ratios

Here, terahertz (THz) generation in a gas medium is simulated by quantum calculations with multicolor intense laser pulses of various frequency ratios. By correlating THz radiation with the above-threshold-ionization photoelectron spectrum, we confirm that our previously proposed free-free transition model for the generation of THz radiation is also applicable to multicolor pulses. THz photon emissions can proceed through transitions between continuous states with similar energies that have opposite parities. This mechanism predicts that THz waves can be efficiently generated at special two-color laser frequency ratios when the multiphoton mixing condition is satisfied. Applying this model to multicolor fields (sawtooth wave shape), we provide a quantum-mechanical interpretation for the reason for THz radiation enhancement. A scheme combining multiple lasers to raise THz radiation satisfying multiphoton mixing conditions is also proposed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Near-field acoustical holography and acoustic power analysis of a simulated, highly heated supersonic jet

Although near-field acoustical holography (NAH) and acoustic intensity analysis have previously been used to investigate the apparent jet noise sources produced by military aircraft, explicit connections to supersonic jet characteristics cannot be made due to a lack of information about the exhaust plume. Here, to begin to bridge this gap and better understand the source information yielded by NAH, the current study instead applies NAH to a virtual measurement of the near-field pressures of a highly heated laboratory-scale supersonic jet generated by large-eddy simulation (LES). The holographic reconstructions of the pressure, particle velocity, and acoustic intensity are found to match the LES-generated acoustic field well and are used to calculate the acoustic power of the jet. The jet's calculated overall acoustic power is compared to the free-stream mechanical power, resulting in an acoustic efficiency of 1.5%. Ray-tracing of the acoustic intensity to the jet centerline generates an axial distribution of the acoustic power origin, showing that almost all the power originates from the supersonic portion of the flow and with the distribution peak upstream of the potential core tip. Holographic reconstruction of the pressures along the nozzle lipline captures the general spectral shape of the LES-generated pressures, though it underestimates the amplitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗