Library Of Simulations Research Articles

Simulated fire video collection for advancing understanding of human behavior in building fires.

The goal of the present research was to develop a video collection of simulated fires to investigate how people perceive growing building fires. In fire safety science, a critical factor to occupant responses to building fires is the pre-movement period, determined by how long it takes an individual to initiate taking protective action with an incipient fire. Key to studying the psychological processes that contribute to the duration of the pre-movement period is presenting human subjects with building fires. One approach used in previous research is to present videos of building fires to individuals via scenarios. The numerical simulations used to model fire dynamics can be used to render videos for these scenarios. However, such simulations have predominantly been used in fire protection engineering to design buildings and are relatively inaccessible to social scientists. The present study documents a collection of videos, based on numerical simulations, which can be used by researchers to study human behavior in fire. These videos display developing fires in different types of rooms, growing at different rates, different smoke thickness, among other characteristics. As part of a validation study, participants were presented with subsets of the video clips and were asked to rate the perceived risk posed by the simulated fire. We observed that ratings varied by the intensity and growth rate of the fires, smoke opacity, type of room, and where the viewpoint was located from the fire. These effects aligned with those observed in previous fire science research, providing evidence that the videos could elicit perceived risk using fire simulations. The present research indicates that future studies can utilize the video library of fire simulations to study human perceptions of developing building fires as situational factors are systematically manipulated.

Model order reduction by convex displacement interpolation

We present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several snapshots. Given a library of high-fidelity simulations, we rely on a scalar testing function and on a point set registration method to identify coherent structures of the solution field in the form of sorted point clouds. Given a new parameter value, we exploit a regression method to predict the new point cloud; then, we resort to a boundary-aware registration technique to define bijective mappings that deform the new point cloud into the point clouds of the neighboring elements of the dataset, while preserving the boundary of the domain; finally, we define the estimate as a weighted combination of modes obtained by composing the neighboring snapshots with the previously-built mappings. We present several numerical examples for compressible and incompressible, viscous and inviscid flows to demonstrate the accuracy of the method. Furthermore, we employ the nonlinear interpolation procedure to augment the dataset of simulations for linear-subspace projection-based model reduction: our data augmentation procedure is designed to reduce offline costs — which are dominated by snapshot generation — of model reduction techniques for nonlinear advection-dominated problems.

Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

Understanding Atmospheric Convection Using Large Eddy Simulation

Cloud formation is based on the fundamental principle of atmospheric convection, which involves the vertical transport of heat and moisture into an unstable environment. Convective transfer of moisture and heat in the form of turbulent fluxes over the Bay of Bengal (BoB) has not been explored much and is not resolved in global and regional climate models (GCMs and RCMs) due to the coarser grid resolutions used. Therefore, the present study is an attempt to understand the convection phenomenon over the BoB using a high-resolution cloud-resolving large eddy simulation. Due to the lack of observational data over the BoB, initial and boundary conditions were generated using reanalysis data. We found that the LES successfully captured the cloud formation and convection phenomenon. The turbulence in the convection was analyzed by using Reynolds averaging to obtain variances and covariances. The presence of turbulence over the region was observed. The cloud characteristics were verified by conditionally averaging the output fields. The present study paves a pathway to perform various simulations at different atmospheric conditions over the region in order to create a library of high-resolution simulations.

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model.

Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.

A computational modeling framework for pre-clinical evaluation of cardiac mapping systems.

There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant "gold standard". Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system's ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a "silver standard" comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.

Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-permitting Simulations for Data-driven Parameterizations

Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-permitting Simulations for Data-driven Parameterizations

Collaborative online assignments for an introductory course in musical acoustics

Hands-on activities are a valuable component of any introductory course in physical science, especially a course in musical acoustics that attracts students who are not majoring in a physical science. Tasks that require active engagement, both physical and cognitive, have been shown to improve retention of new concepts [Am. J. Phys. 80, 478 (2012)]. Such tasks also provide an opportunity for students to improve collaboration skills and to learn from each other. The development of a well-vetted library of interactive online physics simulations, such as PhET (https://phet.colorado.edu), and recent improvements in online collaboration platforms have enabled collaborative active learning to occur in a structured way outside the classroom. This presentation will describe three collaborative tasks for a general education course in musical acoustics at Central Washington University that were adapted for online and hybrid versions of thecourse. Each task makes use of freely available software. Examples of how the collaboration can be structured using Zoom and Gather.town will be provided.

Virtual Simulation for Interactive Visualization of 3D Fracture Fixation Biomechanics.

In the surgical fixation of fractures, proper biomechanical stability is key in preventing clinical complications including poor fracture healing, residual deformity, loss of fixation, or implant failure. Stability is largely influenced by treatment decisions made by the surgeon. The interplay of surgeon-controlled variables and their effect on the three-dimensional (3D) biomechanics of a fracture fixation construct are often not intuitive, and current training methods do not facilitate a deep understanding of these interactions. A simulation software interface, FracSim, was developed. FracSim is built on a large precomputed library of finite element simulations. The software allows a surgeon to make adjustments to a virtual fracture fixation construct/weight-bearing plan and immediately visualize how these changes affect 3D biomechanics, including implant stress and fracture gap strain, important for clinical success. Twenty-one orthopaedic residents completed an instructor-led educational session with FracSim focused on bridge plating. Subjects completed pretests and posttests of knowledge of biomechanical concepts and a questionnaire. Subjects scored a mean of 5.6/10 on the pretest of biomechanical knowledge. Senior residents scored better than junior residents (P = 0.04). After the educational session with FracSim, residents improved their test scores to a mean of 8.0/10, with a significant improvement (P < 0.001). Questionnaire scores indicated that subjects believed that FracSim had realistic implants, constructs, and motions and that training with FracSim was purposeful, desirable, efficient, fun, and useful for enhancing the understanding of fracture fixation biomechanics. This new type of simulation software enables interactive visualization of 3D fracture fixation biomechanics. Limitations of this study include lack of a control group undergoing traditional education and lack of a delayed posttest to assess retention. FracSim may provide an effective and engaging way to promote a deeper understanding of biomechanical concepts in the orthopaedic learner.

A NUMERICAL STUDY ON POLAR RING GALAXY (PRG) FORMATION

The association between galaxies is a phenomenon frequently occurring in the universe. Systems called Polar Ring Galaxies (PRGs) can be built for certain interactions. These structures consist of a galaxy known as a host and a ring of gas and stars surrounding a plane nearly perpendicular to its main axis. This research has contributed to the creation of a first library of N-body simulations, to study the parameters required to construct PRGs. A library of this kind is continuously expanding with new simulations based on these first findings. The inherent secular processes of isolated galaxies cannot give rise to this PRG geometry. Such systems are (i) created by galaxy interactions or (ii) by the introduction of cold gas from interstellar filaments within a galaxy. The most popular mechanisms are thought to be (i), and we are interested in this project, particularly where gas from one galaxy to another is introduced, without the need for a fusion. For formation of interaction, orbits with particular geometries are required in order to attach matter to form a polar ring. We review various mass ratios, inclinations and orbital energies in our society. The database which will be developed with the simulation results will contribute to future research on the interaction between galaxies, especially polar rings.

Synthesis of radio signals from extensive air showers using previously computed microscopic simulations

The detection of extensive air showers (EAS) through their radio signal is becoming one of the most promising techniques for the study of neutrinos and cosmic rays at the highest energies. For the design, optimization and characterization of radio arrays and their associated reconstruction algorithms, tens of thousands of Monte Carlo simulations are needed. Current available simulation codes can take several days to compute the signals produced by a single shower, making it impossible to produce the required simulations in a reasonable amount of time, in a cost-effective and environmental-conscious way. In this article we present a method to synthesize the expected signals (the full time trace, not just the peak amplitude) at any point around the shower core, given a set of signals simulated in a finite number of antennas strategically located in a pattern that exploits the signature features of the radio wavefront. The method can be applied indistinctly to the electric field or to the antenna response to the electric field, in the three polarization directions, as long as the maximum of the shower is above the horizon. The synthesized signal can be used to evaluate trigger conditions, compute the fluence or reconstruct the shower incoming direction, allowing for the production of a single library of simulations covering the incoming particles phase-space that can be used and re-used for the characterization and optimization of radio arrays and their associated reconstruction methods, for a thousandth part of the otherwise required CPU time.

Fate of stellar bars in minor merger of galaxies

ABSTRACT Minor merger of galaxies is common during the evolutionary phase of galaxies. Here, we investigate the dynamical impact of a minor merger (mass ratio 1:10) event on the final fate of a stellar bar in the merger remnant. To achieve that, we choose a set of minor merger models from the publicly available GalMer library of galaxy-merger simulations. The models differ in terms of their orbital energy, orientation of the orbital spin vector, and morphology of the satellite galaxy (discy/spheroidal). We demonstrate that the central stellar bar, initially present in the host galaxy, undergoes a transient bar amplification phase after each pericentre passage of the satellite; in concordance with past studies of bar excitation due to tidal encounter. However, once the merger happens, the central stellar bar weakens substantially in the post-merger remnants. The accumulation of satellite’s stars in the central region of merger remnant plays a key role in the bar-weakening process; causing a net increase in the central mass concentration as well as in the specific angular momentum content. We find that the efficiency of mass accumulation from the satellite in the central parts of merger remnants depends on the orbital parameters as well as on the satellite’s morphology. Consequently, different minor merger models display different degrees of bar-weakening event. This demonstrates that minor merger of galaxies is a plausible avenue for bar weakening in disc galaxies.

Review: Some Animals Are More Equal than Others: Keystone Species and Trophic Cascades and Exploring Earth's Keystone Species

At the beginning of every school year, many educators are asked to prepare lesson plans for each course they teach, to be used in the case of an emergency absence. Teachers struggle to find resources that are not only engaging, but also present relevant information in a way students can understand without a teacher being there to translate for them. The Howard Hughes Medical Institute (HHMI) BioInteractive website hosts a vast library of biology-related media resources, virtual labs, and simulations that can be used under these circumstances. Two lessons of note include the film entitled Some Animals Are More Equal than Others: Keystone Species and Trophic Cascades and a related Google Earth Voyager module called Exploring Earth's Keystone Species.The short film opens by explaining pioneering research by Robert Paine and James Estes into interactions between sea star, sea urchin, and sea otter populations and their effects on the local Washington State kelp forests. The key concepts covered encompass topics that link across the entire biology or environmental science content, including population dynamics, food webs, trophic structure, biodiversity, ecosystem stability, species interactions, and experimental design. Covering all of these topics makes the film appropriate to show at any point in the course curriculum. Moreover, the video is short, only 20 minutes, which leaves plenty of time for students to discuss and analyze the information and apply their understanding to activities. Students could discuss ideas such as causal loop modeling of a kelp forest, completing a claim-evidence-reasoning prompt, or making predictions related to a different ecosystem of study. The HHMI website has a pretty highly cognitively demanding set of prompts that could fill several class periods with deep discussions related to these areas.Moreover, HHMI has partnered with Google Earth to provide a Voyager module entitled Exploring Earth's Keystone Species to accompany the video that students will find especially interesting. In this module, students explore other keystone species from around the world, such as the gray wolf, Eurasian beaver, and African elephant. In each location, students are provided a slideshow with captivating imagery, a short description of the organism, and the ability to explore the animal's habitat in greater detail. Be sure to give students a time limit or they could get sucked in for hours! Adding this Google Earth activity to the video is a high-leverage teacher move, not only because it will prompt more detailed discussions and deeper connections to the topic, but also because it presents the content in a more culturally relevant manner. It provides a greater diversity of examples with which students can develop personal connections.Finally, when combined, these two resources provide a challenging, differentiated, and diverse set of learning tasks that engage students in authentic learning in a personally meaningful way. It challenges students to think beyond their classroom about keystone species and human impacts on these species from a more global perspective. This will hopefully lead the students to the place every biology teacher wants their students to go – having them be better stewards of their dynamic and fragile planet.

Dynamically formed black hole binaries: In-cluster versus ejected mergers

AbstractThe growing number of black hole binary (BHB) mergers detected by the Laser Interferometer Gravitational-Wave Observatory have the potential to enable an unprecedented characterisation of the physical processes and astrophysical conditions that govern the formation of compact binaries. In this paper, we focus on investigating the dynamical formation of BHBs in dense star clusters through a state-of-the-art set of 58 direct N-body simulations with N$\leqslant200\,000$particles which include stellar evolution, gravitational braking, orbital decay through gravitational radiation, and galactic tidal interactions. The simulations encompass a range of initial conditions representing typical young globular clusters, including the presence of primordial binaries. The systems are simulated for$\sim 12$Gyr. The dataset yields 117 BHB gravitational wave (GW) events, with 97 binaries merging within their host cluster and 20 merging after having been ejected. Only 8% of all ejected BHBs merge within the age of the Universe. Systems in this merging subset tend to have smaller separations and larger eccentricities, as this combination of parameters results in greater emission of gravitational radiation. We confirm known trends from Monte Carlo simulations, such as the anti-correlation between the mass of the binary and age of the cluster. In addition, we highlight for the first time a difference at low values of the mass ratio distribution between in-cluster and ejected mergers. However, the results depend on assumptions on the strength of GW recoils, thus in-cluster mergers cannot be ruled out at a significant level of confidence. A more substantial catalogue of BHB mergers and a more extensive library of N-body simulations are needed to constrain the origin of the observed events.

Analyzing collective motion with machine learning and topology.

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.

TMB Library of Nucleosome Simulations.

Nucleosomes are the fundamental building blocks of chromatin, the biomaterial that houses the genome in all higher organisms. A nucleosome consists of 145-147 base pairs of DNA wrapped 1.7 times around eight histones. Given a four-letter code (A, C, G, T), there are approximately 4147 or 1088 oligonucleotides that can form a nucleosome. Comparative, rather than comprehensive, studies are required. Here we introduce the TMB Library of nucleosome simulations and present a meta-analysis of over 20 μs of all atom molecular dynamics simulations representing 518 different realizations of the nucleosome. The TMB Library serves as a reference for future comparative, on-demand simulations of nucleosomes and a demonstration of iBIOMES Lite as a tool for managing a laboratory's simulation library. For every simulation, dewatered trajectories, RMSD, and DNA helical parameter data are provided through iBIOMES Lite in a Web browser and a file browser format. A novel view of nucleosomal DNA emerges from our meta-analysis of the TMB Library. DNA conformation is restricted to a specific left-handed superhelix, but the range of conformations observed for individual bases and base pairs is not more restricted nor more highly deformed than DNA free in solution. With the exception of Roll, mean DNA helical parameter values obtained from simulations of nucleosomes are largely within the range of thermal motion of DNA free in solution. The library provides evidence of DNA kinking in the nucleosome and clearly demonstrates the effects of DNA sequence on the gross structure and dynamics of nucleosomes. These effects and mispositioning of the 601 super strong nucleosome positioning sequence can be detected in short simulations (10 ns). Collectively, the results provide a basis for comparative simulation studies of nucleosomes and extend our understanding of the binding of proteins and drugs to nucleosomal DNA. The TMB Library can be found at http://dna.engr.latech.edu/~tmbshare/ .

Simulations Find Our Accounting of Dust-obscured Star Formation May Be Incomplete

The bulk of the star-formation rate density peak at cosmic noon was obscured by dust. How accurately we can assess the role of dust obscured star-formation is affected by inherent biases in our empirical methods -- both those that rely on direct dust emission and those that rely on the inferred dust attenuation of starlight. We use a library of hydrodynamic simulations with radiative transfer to explore these biases. We find that for IR luminous galaxies that are in rapidly quenching systems (e.g. post-coalescence) standard luminosity-to-SFR relations can strongly overestimate the true SFRs. We propose using the $L_{IR}/L_{1.6}$ color to both help identify such systems and provide more accurate SFRs. Conversely, we find that the diagnostic UVJ plot misidentifies a subset of dusty star-forming galaxies. This is due to variability in the effective attenuation curves including being much grayer in the optical-to-near-IR regime than the Calzetti starburst law. This is in agreement with recent observations of IR-selected galaxies at cosmic noon. Our results support the view that we need a panchromatic approach from the rest-frame UV through the IR and SED modeling that includes realistic SFHs and allows for variable attenuation curves if we want to fully account for dust obscured star-formation across the epochs of greatest galaxy build-up.

Clues on Arp 142: The spiral–elliptical merger

Abstract Nearby merging pairs are unique laboratories in which one can study the gravitational effects on the individual interacting components. In this manuscript, we report the characterization of selected H ii regions along the peculiar galaxy NGC 2936, member of the galaxy pair Arp 142, an E+S interaction, known as ‘The Penguin’. Using Gemini South spectroscopy, we have derived a high enhancement of the global star formation rate (SFR) = 35.9 M⊙ yr−1 probably stimulated by the interaction. Star-forming regions on this galaxy display oxygen abundances that are consistent with solar metallicities. The current data set does not allow us to conclude any clear scenario for NGC 2936. Diagnostic diagrams suggest that the central region of NGC 2936 is ionized by active galactic nucleus (AGN) activity and the eastern tidal plume in NGC 2936 is experiencing a burst of star formation, which may be triggered by the gas compression due to the interaction event with its elliptical companion galaxy: NGC 2937. The ionization mechanism of these sources is consistent with shock models of low velocities of 200–300 km s −1. The isophotal analysis shows tidal features on NGC 2937: at inner radii non-concentric (or off-centring) isophotes, and at large radii, a faint excess of the surface brightness profile with respect to de Vaucouleurs law. By comparing the radial velocity profiles and morphological characteristics of Arp 142 with a library of numerical simulations, we conclude that the current stage of the system would be about 50 ± 25 Myr after the first pericentre passage.

First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole

Abstract When surrounded by a transparent emission region, black holes are expected to reveal a dark shadow caused by gravitational light bending and photon capture at the event horizon. To image and study this phenomenon, we have assembled the Event Horizon Telescope, a global very long baseline interferometry array observing at a wavelength of 1.3 mm. This allows us to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87. We have resolved the central compact radio source as an asymmetric bright emission ring with a diameter of 42 ± 3 μas, which is circular and encompasses a central depression in brightness with a flux ratio ≳10:1. The emission ring is recovered using different calibration and imaging schemes, with its diameter and width remaining stable over four different observations carried out in different days. Overall, the observed image is consistent with expectations for the shadow of a Kerr black hole as predicted by general relativity. The asymmetry in brightness in the ring can be explained in terms of relativistic beaming of the emission from a plasma rotating close to the speed of light around a black hole. We compare our images to an extensive library of ray-traced general-relativistic magnetohydrodynamic simulations of black holes and derive a central mass of M = (6.5 ± 0.7) × 109 M ⊙. Our radio-wave observations thus provide powerful evidence for the presence of supermassive black holes in centers of galaxies and as the central engines of active galactic nuclei. They also present a new tool to explore gravity in its most extreme limit and on a mass scale that was so far not accessible.

Acoustic seafloor classification using machine learning and simulations of Helmholtz equations

We discuss numerical methods for inverse problems in high frequency underwater acoustics, aiming to recover detailed characteristics of the seafloor from measured backscatter data generated from SONAR systems. The key to successful inversion is the use of accurate forward modeling capturing the dependence of backscatter on seafloor properties, such as sediment type, roughness, and thickness. Prediction models are often statistical models formed using years of collected ground-truth data or physical models that require costly simulations or unrealistic environmental assumptions. Sophisticated tools are needed to accommodate complicated scenarios, i.e., uncharted seafloor landscapes. To enable a rapid, remote, and accurate seafloor parameter recovery, we propose a combination of machine learning and high fidelity forward modeling and simulation of the physical wave propagation and scattering process. The idea is to partition environments on the order of kilometers in width into much smaller “template” domains, a few meters in width, where the sediment layer can be described using a few parameters. Machine learning is used to train a classifier using a reference library of simulations of Helmholtz equations on the domains. We investigate the potential of multilayer perceptrons and generalized regression in sediment identification using the reference library. [Work supported by NSF and ONR.]