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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The interplay between chromosome stability and cell cycle control explored through gene–gene interaction and computational simulation

Chromosome stability models are usually qualitative models derived from molecular-genetic mechanisms for DNA repair, DNA synthesis, and cell division. While qualitative models are informative, they are also challenging to reformulate as precise quantitative models. In this report we explore how (A) laboratory experiments, (B) quantitative simulation, and (C) seriation algorithms can inform models of chromosome stability. Laboratory experiments were used to identify 19 genes that when over-expressed cause chromosome instability in the yeast Saccharomyces cerevisiae . To better understand the molecular mechanisms by which these genes act, we explored their genetic interactions with 18 deletion mutations known to cause chromosome instability. Quantitative simulations based on a mathematical model of the cell cycle were used to predict the consequences of several genetic interactions. These simulations lead us to suspect that the chromosome instability genes cause cell-cycle perturbations. Cell-cycle involvement was confirmed using a seriation algorithm, which was used to analyze the genetic interaction matrix to reveal an underlying cyclical pattern. The seriation algorithm searched over 10 14 possible arrangements of rows and columns to find one optimal arrangement, which correctly reflects events during cell cycle phases. To conclude, we illustrate how the molecular mechanisms behind these cell cycle events are consistent with established molecular interaction maps.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms

Here, this study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle behaviour within lattices at heightened temperatures in condensed matter systems. Conventional computational methods encounter challenges, especially in managing chemical potential, prompting exploration into Hamiltonian approaches. Despite the promise of quantum algorithms, their efficacy is hampered by coherence limitations when simulating extended imaginary time evolution sequences. To overcome these constraints, this research focuses on optimizing variational quantum algorithms to probe the thermal properties of the Fermi-Hubbard model. Physics-inspired circuit designs are tailored to alleviate coherence constraints, facilitating a more comprehensive exploration of materials at elevated temperatures. Our study demonstrates the potential of variational algorithms in simulating the thermal properties of the Fermi-Hubbard model while acknowledging limitations stemming from error sources in quantum devices and encountering barren plateaus.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Exploring origins for correlations between flow harmonics and transverse momentum in small collision systems

High statistics data sets from experiments at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) with small and large collision species have enabled a wealth of new flow measurements, including the event-by-event correlation between observables. One exciting such observable $ρ(v^2_n, [p_T])$ gauges the correlation between the mean transverse momentum of particles in an event and the various flow coefficients (v n ) in the same event [P. Bozek, Phys. Rev. C 93, 044908 (2016)]. Recently it has been proposed that very low multiplicity events may be sensitive to initial-state glasma correlations [G. Giacalone, B. Schenke, and C. Shen, Phys. Rev. Lett. 125, 192301 (2020)] rather than flow-related dynamics. We find utilizing the ip-jazma framework that the color domain explanation for the glasma results are incomplete. We then explore predictions from pythia8, and the version for including nuclear collisions called pythia-angantyr, which have only nonflow correlations, and from the ampt model which has both nonflow and flow-type correlations. We find that pythia-angantyr has nonflow contributions to $ρ(v^2_n, [p_T])$ in p + O, p + Pb, and O + O collisions that are positive at low multiplicity and comparable to the glasma correlations. It is striking that in pythia8 in p + p collisions there is actually a sign change from positive to negative $ρ(v^2_n, [p_T])$ as a function of multiplicity. The ampt results match the experimental data general trends in Pb+Pb collisions at the LHC, except at low multiplicity where ampt has the opposite sign. In p + Pb collisions, ampt has the opposite sign from experimental data and we explore this within the context of parton geometry. Predictions for p + O, O + O, and Xe + Xe are also presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Graph reinforcement learning for exploring model spaces beyond the standard model

We present a methodology for performing scans of beyond the standard model (BSM) parameter spaces with reinforcement learning. We identify a novel procedure using graph neural networks that is capable of exploring spaces of models without the user specifying a fixed particle content, allowing broad classes of BSM models to be explored—in theory, the technique is applicable to nearly any model space with a prespecified gauge group. We provide a generic procedure by which a suitable graph grammar can be developed for any BSM model that features user-specified symmetry groups and a finite number of different possible particle species, the use of which is applicable to a variety of machine learning tasks over the actions of BSM theories beyond our particular reinforcement learning use case. As a proof of concept, we construct the graph grammar for theories with vectorlike leptons that may or may not be charged under a dark U ( 1 ) group, inspired by portal matter extensions of the sub-GeV vector portal/kinetic mixing simplified dark matter models. We then use this graph grammar to create a reinforcement learning environment tasked with creating models with these vectorlike leptons that are consistent with a list of a variety of precision observables. The reinforcement learning agent succeeds in developing models that can address the observed muon anomalous magnetic moment discrepancy while remaining consistent with flavor violation and electroweak precision observables, including both constructions that have previously been studied as well as new models that have not, to our knowledge, previously been identified. By inspecting the resulting ensembles of models that the agent produces and experimenting with different configurations for our reinforcement learning environment and graph grammar, we also infer various lessons about the development of these environments that can be transferable to reinforcement learning scans of more complicated model spaces and comment on future directions for the development of this technique into a more mature tool. Published by the American Physical Society 2025

Wojcik, George N.↗
DOE OSTI2024

Crystallographic map: A general lattice and basis formalism enabling efficient and discretized exploration of crystallographic phase space

Three-dimensional lattices are fundamental to solid-state physics. The description of a lattice with an atomic basis constitutes the necessary information to predict solid phase properties and evolution. Here, we present an algorithm for systematically exploring crystallographic phase space. Further, coupled with ab initio techniques, such as density functional theory, this algorithm offers an approach for exploring and tuning materials behavior, with a broad range of potential applications: particularly martensitic phase transformations and materials stability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Blending Machine Learning and Interaction Design in Audio Explorer

The results of machine learning models can often be difficult to interpret, especially for domain experts. Audio Explorer, the winning entry of the 2018 VAST Challenge, is an interactive data exploration tool that effectively communicates machine learning results, using coordinated geospatial, temporal, and auditory visualizations to promote information discovery.

Visual analytics, VAST Challenge↗
DOE OSTI2015

Interactive Streamline Exploration and Manipulation Using Deformation

Occlusion presents a major challenge in visualizing three-dimensional flow fields with streamlines. Displaying too many streamlines at once makes it difficult to locate interesting regions, but displaying too few streamlines risks missing important features. A more ideal streamline exploration model is to allow the viewer to freely move across the field that has been populated with interesting streamlines and pull away the streamlines that cause occlusion so that the viewer can inspect the hidden ones in detail. In this paper, we present a streamline deformation algorithm that supports such user-driven interaction with three-dimensional flow fields. We define a view-dependent focus+context technique that moves the streamlines occluding the focus area using a novel displacement model. To preserve the context surrounding the user-chosen focus area, we propose two shape models to define the transition zone for the surrounding streamlines, and the displacement of the contextual streamlines is solved interactively with a goal of preserving their shapes as much as possible. Based on our deformation model, we design an interactive streamline exploration tool using a lens metaphor. Our system runs interactively so that users can move their focus and examine the flow field freely.

flow visualization, focus+context, streamline, def↗
DOE OSTI2019

Exploring the use of novel programming models in land surface models

A wide range of programming models are currently under rapid development to meet the needs of application developers looking to work on more complex machines. These models fill a variety of roles. Some look to abstract supercomputer architecture, including both processors and memory, to present a strategy for portable performance across a wide range of machines. Others look to expose concurrency by explicitly constructing task-driven dependency graphs that allow a scheduler to find parallelism. Here we explore the implications for application codes of adopting two such programming models, Kokkos and Legion, one from each class of models. We specifically focus on the software design implications on refactoring existing applications, rather than the performance and performance tuning of these models. We identify a strategy for refactoring the Energy Exascale Earth System Model's Land Surface Model, an extremely complex code for climate applications, and prototype a series of mini-apps that explore the adoption of Kokkos and Legion. In doing this, we identify commonalities across the models, leading to a series of conclusions about application software design and refactoring for the adoption of novel programming models.

Coon, Ethan↗
DOE OSTI2024

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. Finally, we evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

PlantFAdb: a resource for exploring hundreds of plant fatty acid structures synthesized by thousands of plants and their phylogenetic relationships

Over 450 structurally distinct fatty acids are synthesized by plants. We have developed PlantFAdb.org, an internet-based database that allows users to search and display fatty acid composition data for over 9000 plants. PlantFAdb includes more than 17 000 data tables from >3000 publications and hundreds of unpublished analyses. This unique feature allows users to easily explore chemotaxonomic relationships between fatty acid structures and plant species by displaying these relationships on dynamic phylogenetic trees. Users can navigate between order, family, genus and species by clicking on nodes in the tree. The weight percentage of a selected fatty acid is indicated on phylogenetic trees and clicking in the graph leads to underlying data tables and publications. The display of chemotaxonomy allows users to quickly explore the diversity of plant species that produce each fatty acid and that can provide insights into the evolution of biosynthetic pathways. Fatty acid compositions and other parameters from each plant species have also been compiled from multiple publications on a single page in graphical form. Links provide simple and intuitive navigation between fatty acid structures, plant species, data tables and the publications that underlie the datasets. In addition to providing an introduction to this resource, this report illustrates examples of insights that can be derived from PlantFAdb. Based on the number of plant families and orders that have not yet been surveyed we estimate that a large number of novel fatty acid structures are still to be discovered in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

General-Surrogate Adaptive Sampling Using Interquartile Range for Design Space Exploration

Abstract A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations and experiments). New samples are often selected based on the estimated uncertainty in the design space. While some surrogates such as kriging have readily available uncertainty models for their predictions, other surrogates do not. Consequently, there have been studies of using the process of leaving-samples-out (LSO) used in cross-validation tools to estimate prediction uncertainty, such as the universal prediction distribution (UPD). In this paper, an adaptive sampling scheme for general surrogates is proposed based on LSO, similar to cross-validation and interquartile range (IQR). Multiple submodels are first developed from LSO. The uncertainty is then estimated from the IQR of these surrogates at a given point. New samples are added iteratively at the point with maximum IQR for design space exploration. The proposed scheme is illustrated using kriging, radial basis function, and neural network surrogates. The proposed scheme is evaluated using four algebraic test functions. Multiple sets of initial samples were produced to account for randomness. For these test functions, the proposed scheme was found to be more accurate and robust than kriging with its own uncertainty model. The proposed scheme was more accurate than the UPD for three out of the four test functions. For a fixed number of samples, the IQR-based adaptive sampling also proved to be more accurate than all-at-once sampling in most cases even when the estimated uncertainty was only mildly correlated with prediction errors.

Engineering↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
DOE OSTI2015

Monte Carlo simulations of the dose from imaging with GE eXplore 120 micro-CT using GATE

Purpose: Small animals are increasingly used as translational models in preclinical imaging studies involving microCT, during which the subjects can be exposed to large amounts of radiation. While the radiation levels are generally sublethal, studies have shown that low-level radiation can change physiological parameters in mice. In order to rule out any influence of radiation on the outcome of such experiments, or resulting deterministic effects in the subjects, the levels of radiation involved need to be addressed. The aim of this study was to investigate the radiation dose delivered by the GE eXplore 120 microCT non-invasively using Monte Carlo simulations in GATE and to compare results to previously obtained experimental values. Methods: Tungsten X-ray spectra were simulated at 70, 80, and 97 kVp using an analytical tool and their half-value layers were simulated for spectra validation against experimentally measured values of the physical X-ray tube. A Monte Carlo model of the microCT system was set up and four protocols that are regularly applied to live animal scanning were implemented. The computed tomography dose index (CTDI) inside a PMMA phantom was derived and multiple field of view acquisitions were simulated using the PMMA phantom, a representative mouse and rat. Results: Simulated half-value layers agreed with experimentally obtained results within a 7% error window. The CTDI ranged from 20 to 56 mGy and closely matched experimental values. Derived organ doses in mice reached 459 mGy in bones and up to 200 mGy in soft tissue organs using the highest energy protocol. Dose levels in rats were lower due to the increased mass of the animal compared to mice. The uncertainty of all dose simulations was below 14%. Conclusions: Monte Carlo simulations proved a valuable tool to investigate the 3D dose distribution in animals from microCT. Small animals, especially mice (due to their small volume), receive large amounts of radiation from the GE eXplore 120 microCT, which might alter physiological parameters in a longitudinal study setup.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-E-10: Student-Driven Exploration of Radiographic Material Properties, Phantom Construction, and Clinical Workflows Or: The Extraordinary Life of CANDY MAN

Purpose: To develop a hands-on learning experience that explores the radiological and structural properties of everyday items and applies this knowledge to design a simple phantom for radiotherapy exercises. Methods: Students were asked to compile a list of readily available materials thought to have radiation attenuation properties similar to tissues within the human torso. Participants scanned samples of suggested materials and regions of interest (ROIs) were used to characterize bulk attenuation properties. Properties of each material were assessed via comparison to a Gammex Tissue characterization phantom and used to construct a list of inexpensive near-tissue-equivalent materials. Critical discussions focusing on samples found to differ from student expectations were used to revise and narrow the comprehensive list. From their newly acquired knowledge, students designed and constructed a simple thoracic phantom for use in a simulated clinical workflow. Students were tasked with setting up the phantom and acquiring planning CT images for use in treatment planning and dose delivery. Results: Under engineer and physicist supervision, students were trained to use a CT simulator and acquired images for approximately 60 different foodstuffs, candies, and household items. Through peer discussion, students gained valuable insights and were made to review preconceptions about radiographic material properties. From a subset of imaged materials, a simple phantom was successfully designed and constructed to represent a human thorax. Students received hands-on experience with clinical treatment workflows by learning how to perform CT simulation, create a treatment plan for an embedded tumor, align the phantom for treatment, and deliver a treatment fraction. Conclusion: In this activity, students demonstrated their ability to reason through the radiographic material selection process, construct a simple phantom to specifications, and exercise their knowledge of clinical workflows. Furthermore, the enjoyable and inexpensive nature of this project proved to attract participant interest and drive creative exploration. Mahon and Riblett have nothing to disclose; Hugo has a research agreement with Phillips Medical systems, a license agreement with Varian Medical Systems, research grants from the National Institute of Health. Authors do not have any potential conflicts of interest to disclose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-201-11: Exploring the Upper Limits of Dose Sculpting Capacity of the Novel Direction Modulated Brachytherapy (DMBT) Tandem Applicator

Purpose: To explore and quantify the upper limits in dose sculpting capacity of the novel direction modulated brachytherapy (DMBT) tandem applicator compared with conventional tandem design for {sup 192}Ir-based HDR planning. Methods: The proposed DMBT tandem applicator is designed for image-guided adaptive brachytherapy (IGABT), especially MRI, of cervical cancer. It has 6 peripheral holes of 1.3-mm width, grooved along a 5.4-mm diameter nonmagnetic tungsten alloy rod of density 18.0 g/cc, capable of generating directional dose profiles – leading to enhanced dose sculpting capacity through inverse planning. The external dimensions are identical to that of conventional tandem design to ensure clinical compatibility. To explore the expansive dose sculpting capacity, we constructed a hypothetical circular target with 20-mm radius and positioned the DMBT and conventional tandems at the center. We then incrementally shifted the positions laterally away from the center of up to 15 mm, at 1-mm steps. The in-house coded gradient projection-based inverse planning system was then used to generate inverse optimized plans ensuring identical V100=100% coverage. Conformity index (CI) was calculated for all plans. Results: Overall, the DMBT tandem generates more conformal dose distributions than conventional tandem for all lateral positional shifts of 0-15 mm (CI=0.91–0.52 and 0.99–0.34, respectively), with an exception at the central position due to the ideal circular dose distribution, generated by the {sup 192}Ir, fitting tightly around the circular target (CI = 0.91 and 0.99, respectively). The DMBT tandem is able to generate dose conformity of CI>0.8 at up to 6-mm positional shift while the conventional tandem violates this past 2-mm shift. Also, the CI ratio (=DMBT/conv.) increases rapidly until about 8 mm and then stabilizes beyond. Conclusion: A substantial enhancement in the dose sculpting capacity has been demonstrated for the novel DMBT tandem applicator. While further studies are warranted, the concept is promising for potential clinical translation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Reflections on Cyanobacterial Chromatic Acclimation: Exploring the Molecular Bases of Organismal Acclimation and Motivation for Rethinking the Promotion of Equity in STEM

Cyanobacteria are photosynthetic organisms that exhibit characteristic acclimation and developmental responses to dynamic changes in the external light environment. Photomorphogenesis is the tuning of cellular physiology, development, morphology, and metabolism in response to external light cues. The tuning of photosynthetic pigmentation, carbon fixation capacity, and cellular and filament morphologies to changes in the prevalent wavelengths and abundance of light have been investigated to understand the regulation and fitness implications of different aspects of cyanobacterial photomorphogenesis. Chromatic acclimation (CA) is the most common form of photomorphogenesis that has been explored in cyanobacteria. Multiple types of CA in cyanobacteria have been reported, and insights gained into the regulatory pathways and networks controlling some of these CA types. I examine the recent expansion of CA types that occur in nature and provide an overview of known regulatory factors involved in distinct aspects of cyanobacterial photomorphogenesis. Additionally, I explore lessons for cultivating success in scientific communities that can be drawn from a reflection on existing knowledge of and approaches to studying CA.

59 BASIC BIOLOGICAL SCIENCES↗