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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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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗
DOE OSTI2026

Exploring the QCD phase diagram through correlations and fluctuations

The exploration of the Quantum Chromodynamics (QCD) phase diagram is a central goal of relativistic heavy-ion collision experiments. This review focuses on the role of fluctuations and correlations as sensitive probes of the phase structure. We discuss theoretical advancements and experimental methodologies employed to map the QCD phase diagram, highlighting constraints derived from both lattice QCD calculations and existing experimental data. Key observables such as cumulants and factorial cumulants of conserved charges (e.g., net-proton, net-charge) are explored as promising signatures of phase transitions and the QCD critical point. We discuss how these quantities are measured experimentally and compared with theoretical predictions, addressing challenges and best practices for meaningful comparisons. Special attention is given to predictions and current experimental results at high baryon density, including recent findings from the STAR collaboration at RHIC. Finally, we identify open issues and future directions for fluctuation and correlation studies at lower collision energies, relevant for future measurements, for example by the CBM experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Exploring power behaviors and trade-offs of in-situ data analytics, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.

Computer Science↗
DOE OSTI2018

Tuyere: Enabling Scalable Memory Workloads for System Exploration

Memory technologies are under active development. Meanwhile, workloads on contemporary computing systems are increasing rapidly in size and diversity. Such dynamics in hardware and software further widen the gap between memory system design and performance evaluation. In this work, we propose a data-centric abstraction of high-performance computing applications for fast exploration of new memory technologies. We also provide a framework that uses a formal modeling language to describe the abstraction, automatically translates abstractions into memory traffic, and directly interfaces with cycle-accurate simulators. We evaluated the framework using 20 workloads and validated the memory traffic profile, the simulation results, and the relative memory changes of four memory technologies. Our results show that the data-centric abstraction can accurately capture application behavior adaptable to different input problems and can expedite system exploration.

Peng, Ivy Bo↗
DOE OSTI2024

Exploring Architectural-Aware Affinity Policies in Modern HPC Runtimes

Modern commodity and High-Performance Computing (HPC) systems are evolving with complex CPU architectures. These architectures now feature higher core and NUMA domain counts and implement features such as hyperthreading. When considering significant differences in hardware configurations, library availability, and hardware-tailored system/software stacks, which could substantially vary from one system to another, performance portability is hard to achieve. Throughout the years, this trend resulted in an increasingly high burden on application developers to fine-tune their workloads for each architecture. This work explores how hardware-dependent aspects such as locality/process/thread affinity affect performance in modern CPU architectures. We focus our study on the Global Memory and Threading (GMT) distributed runtime system as a representative of Partitioned Global Address Space (PGAS) software stacks commonly adopted for productivity. In particular, to appreciate performance implications, we evaluate GMT’s thread affinity policies, and, introduce two new ones which exploit architectural awareness. Finally, we explore alternative NUMA configurations via different process bindings and perform a scalability study on three HPC clusters with varying CPU architectures and NUMA layouts. Our analysis indicates that more complex architectures are more affected by affinity and binding policies and highlights the importance of setting proper runtime configurations to achieve superior performance.

Di Dio Lavore, Ian↗
DOE OSTI2026

BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration

While standardized ontologies like the Brick schema address data heterogeneity in Building Automation Systems (BAS), accessing this semantic data remains a challenge as domain experts often lack the expertise to formulate complex SPARQL queries. To bridge this gap, we present BrickQA, a Large Language Model (LLM)-based framework that translates natural language into executable SPARQL queries through structured query decomposition, dynamic schema exploration, and inline validation. BrickQA utilizes an iterative reasoning agent to actively navigate graph topology through dynamic exploration actions without requiring exhaustive context injection or model fine-tuning. This approach effectively mitigates hallucinations, particularly in large-scale building knowledge graphs. Empirical evaluation on BuildingQA, a standardized benchmark, demonstrates that BrickQA significantly outperforms ReAct baselines, delivering a 0.291–0.355 absolute F1 improvement while achieving 3 × –12.7 × higher token cost-efficiency. Beyond these metrics, the framework maintains structural fidelity across heterogeneous buildings and remains resilient to ambiguous queries without requiring site-specific fine-tuning. Furthermore, a case study on operational analytics validates the framework’s capability to handle temporal and aggregation constraints, effectively transforming abstract semantic models into actionable facility management insights.1

Ko, Yun-Dam↗
DOE OSTI2021

Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration library is intended to aid in the explorability and explainability of transformer classification networks, or transformer language models with sequence classification heads. The basic function of this library is to take a trained transformer and test/train dataset and produce an ipywidget dashboard which can be displayed in a jupyter notebook or in jupyter lab.

Martindale, Nathan [Oak Ridge National Lab. (ORNL)↗
DOE OSTI2021

DXT Explorer v0.1

DTX Explorer is a tool to generate interactive data visualizations of Darshan I/O traces collected from HPC applications. Its goal is to provide an easy and interactive way for researchers and developers to explore their application's I/O behavior and detect possible I/O bottlenecks that are impacting performance.

Bez, Jean Luca↗