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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Empirical Noise Modeling of Internally Mixed Exhaust Systems

There appears to be no non-proprietary methods to predict the noise of internally mixed exhaust systems, and no guidance for how to adapt known jet noise models for these configurations. This paper surveys literature and historical databases acquired at NASA Glenn's Aero-Acoustic Propulsion Lab to give such guidance. The core premise is that an exhaust system with well-designed mixer produces noise that is to first approximation the same as a fully-mixed jet flow. Refinement of what is meant by a “fully mixed jet” can lead to more accurate prediction of the main jet noise. Additional noise is often generated within the nozzle, typically at high frequencies, whose source mechanism(s) are not obvious. However, a noise prediction method can be established that captures some aspects of the excess noise and provide an estimate of the total jet noise. Explorations of source mechanisms associated with the internal mixer have led to a new noise model which includes the effects of having an external plug nozzle, a feature desirable for near-term supersonic aircraft. Statistical analysis of historical data is provided to estimate the uncertainty in using this method given the variations found that cannot be directly computed without detailed mixer geometry.

Noise Prediction↗
DOE OSTI2023

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. Here, in this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Cassini VIMS Preliminary Exploration of Titan's Surface Hemispheric Albedo Dichotomy

We present preliminary evidence that suggests a hemispheric albedo dichotomy on Titan, the largest planetary satellite in the Solar System. We have also studied the photometric properties of several dark circular features on Titan's surface to test if they might be of impact origin. The evidence is derived from photometric analysis of selected surface regions taken at different Titanian longitudes and solar phase angles using images from the Cassini Saturn Orbiter Visual and Infrared Mapping Spectrometer (VIMS). The VIMS instrument is able to image Titan's surface at spectral windows (e.g. 2.02 microns) in its atmosphere where methane, the principal atmospheric absorber is transparent. Additional information is included in the original extended abstract.

Nelson, R. M.↗
NASA NTRSDate not supplied

Brief Overview of Subsonic Single Aft Engine (SUSAN) Transport Aircraft Concept and Trade Space Exploration

The SUSAN concept uses a 20-megawatt Electrified Aircraft Propulsion system to enable advance Propulsion Airframe Integration in transport category aircraft. Alternative fuels will be used to reduce the amount of emissions per energy used. By combining these features there is the potential to reduce aircraft emissions by 50 percent per passenger/mile while retaining the size, speed, and range of large regional jets. The SUSAN is has a 750-mile economic mission, a 2500-mile design range and a maximum capacity of 180 passengers. The SUSAN configuration utilizes a single aft mounted engine and distributed electric wing-mounted thrusters on a tube and wing arrangement with a T-tail empennage. The SUSAN Electrofan employs a hybrid powertrain to enable: single turbofan operation on a large transport category aircraft; increased aerodynamic and propulsive efficiency through placement of electric engines; optimized turbofan sizing and efficiency through control and electric boosting, reduced control surface sizing through thrust augmentation. A single use battery is employed as the power source in case of turbofan failure. The design study also considers the constraints of operating within the current airport, airspace, and economic constraints. Forward work includes optimizing the overall aircraft configuration and including certain hard to model features like boundary layer ingestion or natural laminar flow across all appliable subsystems. Additional work forward work is a more extensive analysis of the configuration using alternative fuels.

Ralph Jansen↗
DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hydrogenation of calcite and change in chemical bonding at high pressure: Diamond formation above 100GPa

Synchrotron X-ray diffraction (XRD) and Raman spectroscopy in laser heated diamond anvil cells and first principles molecular dynamics (FPMD) calculations have been used to investigate the reactivity of calcite and molecular hydrogen (H 2 ) at high pressures up to 120 GPa. We find that hydrogen reacts with calcite starting below 0.5 GPa at room temperature forming chemical bonds with carbon and oxygen. This results in the unit cell volume expansion; the hydrogenation level is much higher for powdered samples. Single-crystal XRD measurements at 8–24 GPa reveal the presence of previously reported III, IIIb, and VI calcite phases; some crystallites show up to 4% expansion, which is consistent with the incorporation of ≤ 1 hydrogen atom per formula unit. At 40–102 GPa XRD patterns of hydrogenated calcite demonstrate broadened features consistent with the calcite VI structure with incorporated hydrogen atoms. Above 80 GPa, the C–O stretching mode of calcite splits suggesting a change in the coordination of C–O bonds. Laser heating at 110 GPa results in the formation of C–C bonds manifested in the crystallization of diamond recorded by in situ XRD at 300 K and 110 GPa and by Raman spectroscopy on recovered samples commenced with C 13 calcite. We explored several theoretical models, which show that incorporation of atomic hydrogen results in local distortions of CO 3 groups, formation of corner-shared C–O polyhedra, and chemical bonding of H to C and O, which leads to the lattice expansion and vibrational features consistent with the experiments. In conclusion, the experimental and theoretical results support recent reports on tetrahedral C coordination in high-pressure carbonate glasses and suggest a possible source of the origin of ultradeep diamonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

The Hybrid Propellant Module (HPM): A New Concept for Space Transfer in the Earth's Neighborhood and Beyond

The safe, affordable and effective transfer of ever-larger payloads and eventually personnel beyond Low Earth Orbit (LEO) is a major challenge facing future commercial development and human exploration of space. Without reusable systems, sustained exploration or large scale development beyond LEO appears to be economically non-viable. However, reusable systems must be capable of both good fuel efficiency and "high utilization of capacity", or else economic costs will remain unacceptably high. Various options exist that can provide high fuel efficiency - for example, Solar Electric Propulsion Systems (SEPS) - but only at the cost of low thrust and concomitant long transit times. Chemical propulsion systems offer the potential for high thrust and short transit times - including both cryogenic and non-cryogenic options - but only at the cost of relatively low specific impulse (Isp). Nuclear thermal propulsion systems offer relatively good thrust-to-weight and Isp - but involve public concerns that may be insurmountable for all except the most-critical of public purposes. Fixed infrastructures have been suggested as one approach to solving this challenge; for example, rotating tether approaches. However, these systems tend to suffer from high initial costs or unacceptable operational constraints. A new concept has been identified - the Hybrid Propellant Module (HPM) - that integrates the best features of both chemical and solar electric transportation architectures. The HPM approach appears to hold promise of solving the issues associated with other approaches, opening a new family of capabilities for future space exploration and development of near-Earth space and beyond. This paper provides a summary overview of the challenge of Earth neighborhood transportation and discusses how various systems concepts might be applied to meet the needs of these architectures. The paper describes a new approach, the HPM, and illustrates the application of the concept for a typical mission concept. The paper concludes with a discussion of needed technologies and a possible timeline for the development and evolution of this class of systems concepts.

Mankins, John C.↗
DOE OSTI2022

Easy-plane anisotropic-exchange magnets on a honeycomb lattice: Quantum effects and dealing with them

We provide analytical and numerical insights into the phase diagram and other properties of the extended Kitaev-Heisenberg model on the honeycomb lattice in the easy-plane limit, in which interactions are only between spin components that belong to the plane of magnetic ions. This parameter subspace allows for a much-needed systematic quantitative investigation of spin excitations in the ordered phases and of their generic features. Specifically, we demonstrate that in this limit one can consistently take into account magnon interactions in both zero-field zigzag and field-polarized phases. For the nominally polarized phase, we propose a regularization of the unphysical divergences that occur at the critical field and are plaguing the 1/S approximation in this class of models. For the explored parameter subspace, all symmetry-allowed terms of the standard parametrization of the extended Kitaev-Heisenberg model, such as K, J, and Γ, are significant, making the offered consideration relevant to a much wider parameter space. Furthermore, the dynamical structure factor near the paramagnetic critical point illustrates this relevance by showing features that are reminiscent of the ones observed in α–RuCl 3 , underscoring that they are not unique and should be common to a wide range of parameters of the model and, by extension, to other materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu 2 type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The aging proteostasis decline: From nematode to human

The aging proteostasis decline manifests in a failure of aging cells and organisms to properly respond to proteotoxic challenges. This proteostasis collapse has long been considered a hallmark of aging in nematodes, and has recently been shown to occur also in human cells upon entry to senescence, opening the way to exploring the phenomenon in the broader context of human aging. Cellular senescence is part of the normal human physiology of aging, with senescent cell accumulation as a prominent feature of aged tissues. Being highly resistant to cell death, senescent cells, as they accumulate, become pro-inflammatory and promote disease. Here we discuss the causes of human senescence proteostasis decline, in view of the current literature on nematodes, on the one hand, and senescence, on the other hand. We review two major aspects of the phenomenon: (1) the decline in transcriptional activation of stress-response pathways, and (2) impairments in proteasome function. We further outline potential underlying mechanisms of transcriptional proteostasis decline, focusing on reduced chromatin dynamics and compromised nuclear integrity. Finally, we discuss potential strategies for reinforcing proteostasis as a means to improve organismal health and address the relationship to senolytics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

TRIM46 contributes to high glucose-induced ferroptosis and cell growth inhibition in human retinal capillary endothelial cells by facilitating GPX4 ubiquitination

Increased permeability of retinal capillary endothelial cells is a key feature in the progression of diabetic retinopathy (DR). Precisely why and how diabetes causes dysfunction in retinal capillary endothelial cells is not well understood, making it challenging to explore more advanced therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

An experimental and computational study of thin-layer Rayleigh–Taylor instability development during deceleration with and without an externally applied magnetic field

The importance of mitigating the Rayleigh–Taylor instability (RTI) in inertial confinement fusion (ICF) is critical to successfully achieve high gain fusion yield. Consequently, understanding the seed mechanisms of RTI and the potential evolution of RTI in ICF relevant conditions is crucial. Single feature perturbations consistently demonstrate non-linear RTI evolution, for which an experimental platform on OMEGA-EP is developed. Manufacturing defects introduced into the target design require exploration of unanticipated changes to RTI development and an identification of targets that will still render quantifiable physics results. Consequently, it is presented that the inherent 3D nature of experimental targets necessitates 3D modeling for accurate design work and predictive modeling of experimental targets, especially when high resolution imaging diagnostics, like Fresnel Zone Plates, are utilized. A study of the morphology of the RTI evolution due to changing initial conditions and the presence of an externally applied magnetic field are also explored. Experimental data show thin-layer RTI morphology comparable to resistive magneto hydrodynamic 3D results. A discussion on the impacts of an externally applied magnetic field makes the case for continued efforts to observe a magnetic field's impact on RTI morphology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The DESI One-Percent survey: exploring the Halo Occupation Distribution of Emission Line Galaxies with AbacusSummit simulations

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyse these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 $\langle$Mh$\rangle$ ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modelling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. Here we investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modelling impacts only slightly our results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer’s disease

Abstract Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer’s disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer’s disease. However, there is currently no effective CypD inhibitor for Alzheimer’s disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood–brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer’s disease mitochondrial and glycolytic bioenergetics in Alzheimer’s disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer’s disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer’s disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer’s disease- and amyloid-β-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer’s disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer’s disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.

Neurosciences & Neurology↗
DOE OSTI2019

Self-dual ν = 1 bosonic quantum Hall state in mixed-dimensional QED

Here, we consider a (2 + 1)-dimensional Wilson-Fisher boson coupled to a (3 + 1)-dimensional U(1) gauge field. This theory possesses a strong-weak duality in terms of the coupling constant e and is self-dual at a particular value of $\mathcal{e}$. We derive exact relations between transport coefficients for a $\mathcal{ν}$ = 1 quantum Hall state at the self-dual point. Using boson-fermion duality, we map the $\mathcal{ν}$ = 1 bosonic quantum Hall state to a Fermi sea of the dual fermion and observe that the exact relationships between transport coefficients at the bosonic self-dual point are reproduced by a simple random-phase approximation (RPA), coupled with a Drude formula, in the fermionic theory. We explain this success of the RPA by pointing out a cancellation of a parity-breaking term in the fermion theory which occurs only at the self-dual point, resulting in the fermion self-dual theory explored previously. In addition, we argue that the equivalence of the self-dual structure can be understood in terms of electromagnetic duality or modular invariance, and these features are not inherited by the nonrelativistic cousins of the present model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Factors influencing quantum evaporation of helium from polar semiconductors from first principles

While there is much indirect evidence for the existence of dark matter (DM), to date it has evaded detection. Current efforts focus on DM masses over ~GeV—to push the sensitivity of DM searches to lower masses, new DM targets and detection schemes are needed. In this work, we focus on the latter—a novel detection scheme recently proposed to detect 10–100 meV phonons in polar target materials. Previous work showed that well-motivated models of DM can interact with polar semiconductors to produce an athermal population of phonons. This new sensing scheme proposes that these phonons then facilitate quantum evaporation of 3 He from a van der Waals film deposited on the target material. However, a fundamental understanding of the underlying process is still unclear, with several uncertainties related to the precise rate of evaporation and how it can be controlled. In this work, we use ab initio density functional theory calculations to compare the adsorption energies of helium atoms on a polar target material, sodium iodide, to understand the underlying evaporation physics. We explore the role of surface termination, monolayer coverage, and elemental species on the rate of He evaporation from the target material. Using this, we discuss the optimal target features for He-evaporation experiments and their range of tunability through chemical and physical modifications such as applied field and surface termination.

36 MATERIALS SCIENCE↗