Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “statistical mechanics models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

An Acoustic Charge Transport Imager for High Definition Television Applications: Reliability Modeling and Parametric Yield Prediction of GaAs Multiple Quantum Well Avalanche Photodiodes

Reliability modeling and parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs), which are of interest as an ultra-low noise image capture mechanism for high definition systems, have been investigated. First, the effect of various doping methods on the reliability of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiode (APD) structures fabricated by molecular beam epitaxy is investigated. Reliability is examined by accelerated life tests by monitoring dark current and breakdown voltage. Median device lifetime and the activation energy of the degradation mechanism are computed for undoped, doped-barrier, and doped-well APD structures. Lifetimes for each device structure are examined via a statistically designed experiment. Analysis of variance shows that dark-current is affected primarily by device diameter, temperature and stressing time, and breakdown voltage depends on the diameter, stressing time and APD type. It is concluded that the undoped APD has the highest reliability, followed by the doped well and doped barrier devices, respectively. To determine the source of the degradation mechanism for each device structure, failure analysis using the electron-beam induced current method is performed. This analysis reveals some degree of device degradation caused by ionic impurities in the passivation layer, and energy-dispersive spectrometry subsequently verified the presence of ionic sodium as the primary contaminant. However, since all device structures are similarly passivated, sodium contamination alone does not account for the observed variation between the differently doped APDs. This effect is explained by the dopant migration during stressing, which is verified by free carrier concentration measurements using the capacitance-voltage technique.

Hunt, W. D.↗
NASA NTRS1994

A Compositional Relevance Model for Adaptive Information Retrieval

There is a growing need for rapid and effective access to information in large electronic documentation systems. Access can be facilitated if information relevant in the current problem solving context can be automatically supplied to the user. This includes information relevant to particular user profiles, tasks being performed, and problems being solved. However most of this knowledge on contextual relevance is not found within the contents of documents, and current hypermedia tools do not provide any easy mechanism to let users add this knowledge to their documents. We propose a compositional relevance network to automatically acquire the context in which previous information was found relevant. The model records information on the relevance of references based on user feedback for specific queries and contexts. It also generalizes such information to derive relevant references for similar queries and contexts. This model lets users filter information by context of relevance, build personalized views of documents over time, and share their views with other users. It also applies to any type of multimedia information. Compared to other approaches, it is less costly and doesn't require any a priori statistical computation, nor an extended training period. It is currently being implemented into the Computer Integrated Documentation system which enables integration of various technical documents in a hypertext framework.

Mathe, Nathalie↗
NASA NTRS2006

Plasmaspheric Drainage Plumes: Inner-Magnetospheric Coupling from the IMAGE/EUV Perspective

Plasmaspheric drainage plumes appear in the aftermath of periods of enhanced convection/erosion and are interpreted as a near-equatorial signature of the redistribution of thermal plasma along streamlines. Analysis of IMAGE/EUV observations from "FirstLight" through the end of calendar year 2002 reveals that for Kp greater than or equal to 3, there is an 84% probability of observing a plasmaspheric plume in EUV data. We present a statistical analysis of the geomagnetic conditions [Kp, Dst, and solar wind-induced electric field] associated with EUV plume observations. This analysis yields a peak in observational probability when Kp = 4 and Dst = -50 nT. Additionally, EUV pllume observations are associated with a solar wind-induced convection electric field at Earth [Ev, SM] characterized by bi-modal behavior with a positive mode peaked at approximately 4 m V(raised dot) m(sup -1) and a negative model that peaks at -2 mV (raised dot) m(sup -1). Analysis of the time rate of change of the plume-associated Ev, SM indicated that once the mechanism for plume formation is initiated, a slowly changing convection environment is required to allow for sufficient plume development prior to EUV detection.

Adrian, Mark L.↗
DOE OSTI2021

Inverse design of equilibrium cluster fluids applied to a physically informed model

Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. Here, we then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.

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

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Disordered hyperuniform solid state materials

Disordered hyperuniform (DHU) states are recently discovered exotic states of condensed matter. DHU systems are similar to liquids or glasses in that they are statistically isotropic and lack conventional long-range translational and orientational order. On the other hand, they completely suppress normalized infinite-wavelength density fluctuations like crystals and, in this sense, possess a hidden long-range correlation. Very recently, there have been several exciting discoveries of disordered hyperuniformity in solid-state materials, including amorphous carbon nanotubes, amorphous 2D silica, amorphous graphene, defected transition metal dichalcogenides, defected pentagonal 2D materials, and medium/high-entropy alloys. It has been found that the DHU states of these materials often possess a significantly lower energy than other disorder models and can lead to unique electronic and thermal transport properties, which results from mechanisms distinct from those identified for their crystalline counterparts. For example, DHU states can enhance electronic transport in 2D amorphous silica; DHU medium/high-entropy alloys realize the Vegard's law and possess enhanced electronic bandgaps and thermal transport at low temperatures. These unique properties open up many promising potential device applications in optoelectronics and thermoelectrics. Here, we provide a focused review on these important new developments of hyperuniformity in solid-state materials, taking an applied and “materials” perspective, which complements the existing reviews on hyperuniformity in physical systems and photonic materials. As a result, future directions and outlook are also provided, with a focus on the design and discovery of DHU quantum materials for quantum information science and engineering.

2D materials↗
DOE OSTI2021

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Ebola Virus Infection, Processed Experimental Dataset Catalog

Ebola virus (EBOV) is high risk biological agent, classified as a Category A priority pathogen (Flaviviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), known to cause hemorrhagic fever with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Ebola virus [NCBITAXON:186536] (Zaire/Makona or Zaire/Mayinga) experimental infection study. Human host samples types include peripheral blood mononuclear cells isolated from blood plasma ["PBMC", BTO:0001025], human hepatoma carcinoma cells ["HUH", BTO:0001950], human umbilical vein endothelial cells ["HUVEC", BTO:0001949], immortalized human hepatocyte cells ["IHH", BTO:0006147], and human histiocytic lymphoma cells ["U937", BTO:0001412].

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Influenza A Virus Infection, Processed Experimental Dataset Catalog

Influenza A virus (IAV) is a high risk biological agent, classified as a Category C priority pathogen (Orthomyxoviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus [NCBITAXON:11320] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750] and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Omics Lethal Human Viruses Project Profiling of the Host Response to MERS-CoV Infection, Processed Experimental Dataset Catalog

Middle East Respiratory Syndrome coronavirus (MERS-CoV) is classified as a Category C priority pathogen (Coronaviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV [NCBITAXON:1335626] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750], human bronchial epithelial cells ["Calu-3 clone 2B4"; BTO:0002022], primary human fibroblasts ["FB"; BTO:0000452], primary human airway epithelial cells ["HAE"; BTO:0005571], human microvascular endothelial cells ["HMVE"; BTO:0003123], and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Processing-dependent chemical ordering in Cu 3 Au characterized via non-destructive Bragg coherent diffraction imaging

Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. Here, in this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu 3 Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.

Warren, Nathaniel [Pennsylvania State Univ., Unive↗
DOE OSTI2021

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the velocity gradient tensor (VGT) in homogeneous isotropic turbulence (HIT) are developed under the physics-informed machine learning (PIML) framework. We consider the VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and subfilter contributions, which is accomplished by constructing them using the integrity bases and invariants of the VGT. The developed models can be expressed using the extended tensor basis neural network (TBNN) introduced by Ling et al. [J. Fluid Mech. 807, 155 (2016)]. Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number direct numerical simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the “ground-truth” DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into the TBNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2008

The Solid Rocket Motor Slag Population: Results of a Radar-Based Regressive Statistical Evaluation

Solid rocket motor (SRM) slag has been identified as a potential source of man-made orbital debris. The possibility that SRMs (in addition to generating dust particles in the sub-millimeter range) may generate particles up to centimeters in size has caused concern regarding their contribution to the debris environment. Returned surfaces from space do not have sufficient area or exposure time to provide a clear picture of the SRM millimeter and centimeter debris population. Currently, radar observation is probably the only way to collect data showing the debris contribution from SRMs. Such observation is used to sample the debris environment, but it is difficult to obtain accurate orbital elements for the detected debris objects. NASA has developed several models to describe the different orbital debris populations, based on assumed debris production mechanisms to create clouds of debris objects that can be propagated in time. The NASA model, LEGEND (LEO-to-GEO Environment Debris), functions as a time-tested debris model for most debris sources. However, the current LEGEND model does not include contributions from the SRM population. An SRM model has recently been developed by NASA, based on purely theoretical details of SRM production and known SRM launches, but verification with hard data is needed. Because the detections of individual SRM objects cannot be deterministically separated from the total debris observed by radar, the validation of the SRM model can only be done by combining it with the LEGEND breakup model and comparing it with data. By applying observational constraints, the degree of SRM slag contribution to the environment may be estimated. This serves as an observationally sound method from which to calibrate a purely theoretical model into something more realistic. For this study, we use the populations observed by the Haystack radar from 1996 to present. For the SRM debris, we use a historical database of SRM launches, propellant masses, and estimated locations and times of tailoff to produce and propagate the SRM debris clouds. Comparisons with radar data from the ensuing years were made, and the SRM model was altered with respect to size and mass production of slag particles to reflect the populations estimated from the data. The result is a model SRM population that fits within the bounds of the observed environment and estimates of the production and contribution of SRM debris to the environment.

Horstman, Matthew F.↗
NASA NTRS2016

Understanding Oceanic Heavy Precipitation Using Scatterometer, Satellite Precipitation, and Reanalysis Products

The primary aim of this study is to understand the heavy precipitation events over Oceanic regions using vector wind retrievals from space based scatterometers in combination with precipitation products from satellite and model reanalysis products. Heavy precipitation over oceans is a less understood phenomenon and this study tries to fill in the gaps which may lead us to a better understanding of heavy precipitation over oceans. Various phenomenon may lead to intense precipitation viz. MJO (Madden-Julian Oscillation), Extratropical cyclones, MCSs (Mesoscale Convective Systems), that occur inside or outside the tropics and if we can decipher the physical mechanisms behind occurrence of heavy precipitation, then it may lead us to a better understanding of such events which further may help us in building more robust weather and climate models. During a heavy precipitation event, scatterometer wind observations may lead us to understand the governing dynamics behind that event near the surface. We hypothesize that scatterometer winds can observe significant changes in the near-surface circulation and that there are global relationships among these quantities. To the degree to which this hypothesis fails, we will learn about the regional behavior of heavy precipitation-producing systems over the ocean. We use a "precipitation feature" (PF) approach to enable statistical analysis of a large database of raining features.

Winds↗
DOE OSTI2023

Cross sections for neutron-induced reactions from surrogate data: Reexamining the Weisskopf-Ewing approximation for ( n , n ' ) and ( n , 2 n ) reactions

Background: Modeling nuclear reaction networks for nuclear science applications and for simulations of astrophysical environments relies on cross section data for a vast number of reactions, many of which have never been measured. Cross sections for neutron-induced reactions on unstable nuclei are particularly scarce, since they are the most difficult to measure. Consequently, we must rely on theoretical predictions or indirect measurements to obtain the requisite reaction data. For compound nuclear reactions, the surrogate reaction method can be used to determine many cross sections of interest. Purpose: Earlier work has demonstrated that cross sections for neutron-induced fission and radiative neutron capture can be determined from a combination of surrogate reaction data and theory. For the fission case, it was shown that the Weisskopf-Ewing approximation, which significantly simplifies the implementation of the surrogate method, can be employed. Capture cross sections cannot be obtained, and require a detailed description of the surrogate reaction process. Here, we examine the validity of the Weisskopf-Ewing approximation for determining unknown (n, n') and (n, 2n) cross sections from surrogate data. Methods: Using statistical reaction calculations with realistic parametrizations, we investigate first whether the assumptions underlying the Weisskopf-Ewing approximation are valid for (n, n') and (n, 2n) reactions on representative target nuclei. We then produce simulated surrogate reaction data and assess the impact of applying the Weisskopf-Ewing approximation when extracting (n, n') and (n, 2n) cross sections in situations where the approximation is not strictly justified. Results: We find that peak cross sections can be estimated using the Weisskopf-Ewing approximation, but the shape of the (n, n') and (n, 2n) cross sections, especially for low neutron energies, cannot be reliably determined without accounting for the angular-momentum differences between the neutron-induced and surrogate reaction. Conclusions: To obtain reliable (n, n') and (n, 2n) cross sections from surrogate reaction data, a detailed description of the surrogate reaction mechanisms is required. To do so for the compound-nucleus energies and decay channels relevant to these reactions, it becomes necessary to extend current modeling capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

X-ray-line coincidence photopumping in a potassium-chlorine mixed plasma

Exploiting the multiple long pulse capability and suite of x-ray diagnostics of the Orion laser, we have set out to explore line coincidence photopuming—the enhancement in population of an atomic level brought on by resonant absorption of x rays from a different emitting ion. Unlike previous work, the two ions are in the same plasma and so the experiment is an x-ray analog of the well-known Bowen resonance fluorescence mechanism that operates in astrophysical situations in the optical region. Herein, our measurements have shown enhanced fluorescence in a chlorine plasma, attributable to line coincident photopumping from co-mixed potassium ions. To detect this relatively low signal-to-noise phenomenon, the data from multiple shots are combined, and the statistical method of bootstrapping is used to assign a confidence value to the measured enhancement, resulting in an estimate of the enhancement of 39 ± 18 16 % compared to the null case, where no pumping occurs. The experimental results have been compared to coupled radiation-transport and radiation hydrodynamics simulations using the CRETIN code together with the NYM radiation hydrodynamics model and agreement has been found, with the simulations also predicting modest enhancement.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2012

Non-Equilibrium Properties from Equilibrium Free Energy Calculations

Calculating free energy in computer simulations is of central importance in statistical mechanics of condensed media and its applications to chemistry and biology not only because it is the most comprehensive and informative quantity that characterizes the eqUilibrium state, but also because it often provides an efficient route to access dynamic and kinetic properties of a system. Most of applications of equilibrium free energy calculations to non-equilibrium processes rely on a description in which a molecule or an ion diffuses in the potential of mean force. In general case this description is a simplification, but it might be satisfactorily accurate in many instances of practical interest. This hypothesis has been tested in the example of the electrodiffusion equation . Conductance of model ion channels has been calculated directly through counting the number of ion crossing events observed during long molecular dynamics simulations and has been compared with the conductance obtained from solving the generalized Nernst-Plank equation. It has been shown that under relatively modest conditions the agreement between these two approaches is excellent, thus demonstrating the assumptions underlying the diffusion equation are fulfilled. Under these conditions the electrodiffusion equation provides an efficient approach to calculating the full voltage-current dependence routinely measured in electrophysiological experiments.

Pohorille, Andrew↗
NASA NTRS2024

Correlations of Additive Manufacturing Model-Based Process Metrics With Spatter-Induced Porosity in the Powder Bed Fusion-Laser Beam/Metallic Process

Components fabricated using the powder bed fusion laser beam metallic (PBF-LB/M) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Qualifying materials for components in critical applications requires a thorough understanding of pertinent processing-microstructure-property relationships. Porosity defects have a strong adverse effect on the mechanical properties of a material. The morphology of porosity defects created by lack of fusion in PBF-LB/M often exhibit high aspect ratios that can act as stress concentrators and become crack initiation sites. The occurrence of spatter induced porosity is studied using synchronized AM model-based process metrics and porosity measured by high-resolution X-ray computed tomography. A novel AM process metric is introduced to quantify the relative exposure to spatter ejecta impacts and incorporation throughout the sequence of the PBF-LB/M process. Further characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated statistically with the spatter ejecta impact field of the PBF-LB/M process. Both keyhole and lack of fusion porosity can be predicted by the spatter exposure process metric, which can be exploited to control the fusion sequence.

Additive Manufacturing↗
DOE OSTI2022

The quenching of galaxies, bulges, and disks since cosmic noon

Here, we present an analysis of the quenching of star formation in galaxies, bulges, and disks throughout the bulk of cosmic history, from z = 2 – 0. We utilise observations from the Sloan Digital Sky Survey and the Mapping Nearby Galaxies at Apache Point Observatory survey at low redshifts. We complement these data with observations from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey at high redshifts. Additionally, we compare the observations to detailed predictions from the LGalaxies semi-analytic model. To analyse the data, we developed a machine learning approach utilising a Random Forest classifier. We first demonstrate that this technique is extremely effective at extracting causal insight from highly complex and inter-correlated model data, before applying it to various observational surveys. Our primary observational results are as follows: at all redshifts studied in this work, we find bulge mass to be the most predictive parameter of quenching, out of the photometric parameter set (incorporating bulge mass, disk mass, total stellar mass, and B/T structure). Moreover, we also find bulge mass to be the most predictive parameter of quenching in both bulge and disk structures, treated separately. Hence, intrinsic galaxy quenching must be due to a stable mechanism operating over cosmic time, and the same quenching mechanism must be effective in both bulge and disk regions. Despite the success of bulge mass in predicting quenching, we find that central velocity dispersion is even more predictive (when available in spectroscopic data sets). In comparison to the LGalaxies model, we find that all of these observational results may be consistently explained through quenching via preventative ‘radio-mode’ active galactic nucleus feedback. Furthermore, many alternative quenching mechanisms (including virial shocks, supernova feedback, and morphological stabilisation) are found to be inconsistent with our observational results and those from the literature.

79 ASTRONOMY AND ASTROPHYSICS↗