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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Singularity: Scientific containers for mobility of compute

Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Safeguards-Informed Hybrid Imagery Dataset [Poster]

Deep Learning computer vision models require many thousands of properly labelled images for training, which is especially challenging for safeguards and nonproliferation, given that safeguards-relevant images are typically rare due to the sensitivity and limited availability of the technologies. Creating relevant images through real-world staging is costly and limiting in scope. Expert-labeling is expensive, time consuming, and error prone. We aim to develop a data set of both realworld and synthetic images that are relevant to the nuclear safeguards domain that can be used to support multiple data science research questions. In the process of developing this data, we aim to develop a novel workflow to validate synthetic images using machine learning explainability methods, testing among multiple computer vision algorithms, and iterative synthetic data rendering. We will deliver one million images – both real-world and synthetically rendered – of two types uranium storage and transportation containers with labelled ground truth and associated adversarial examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Improving the long-term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics-based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data-driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data-driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient-based local search algorithm. Two field-scale examples are presented to investigate the performance of the developed data-driven prediction and optimization framework. Furthermore, the prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Co-scheduling Ensembles of In Situ Workflows

Molecular dynamics (MD) simulations are widely used to study large-scale molecular systems. HPC systems are ideal platforms to run these studies, however, reaching the necessary simulation timescale to detect rare processes is challenging, even with modern supercomputers. To overcome the timescale limitation, the simulation of a long MD trajectory is replaced by multiple short-range simulations that are executed simultaneously in an ensemble of simulations. Analyses are usually co-scheduled with these simulations to efficiently process large volumes of data generated by the simulations at runtime, thanks to in situ techniques. Executing a workflow ensemble of simulations and their in situ analyses requires efficient co- scheduling strategies and sophisticated management of computational resources so that they are not slowing down each other. In this paper, we propose an efficient method to co-schedule simulations and in situ analyses such that the makespan of the workflow ensemble is minimized. We present a novel approach to allocate resources for a workflow ensemble under resource constraints by using a theoretical framework modeling the workflow ensemble’s execution. We evaluate the proposed approach using an accurate simulator based on the WRENCH simulation framework on various workflow ensemble configurations. Results demonstrate the significance of co-scheduling simulations and in situ analyses that couple data together to benefit from data locality, in which inefficient scheduling decisions can lead to slowdown in makespan up to a factor of 30.

Do, Tu Mai Anh↗
DOE OSTI2018

Enforcing End-to-end I/O Policies for Scientific Workflows using Software-Defined Storage Resource Enclaves

Data-intensive knowledge discovery requires scientific applications to run concurrently with analytics and visualization codes executing in situ for timely output inspection and knowledge extraction. Consequently, I/O pipelines of scientific workflows can be long and complex because they comprise many stages of analytics across different layers of the I/O stack of high-performance computing systems. Performance limitations at any I/O layer or stage can cause an I/O bottleneck resulting in greater than expected end-to-end I/O latency. In this paper, we present the design and implementation of a novel data management infrastructure called Software-Defined Storage Resource Enclaves (SIREN) at system level to enforce end-to-end policies that dictate an I/O pipeline's performance. SIREN provides an I/O performance interface for users to specify the desired storage resources in the context of in-situ analytics. If suboptimal performance of analytics is caused by an I/O bottleneck when data are transferred between simulations and analytics, schedulers in different layers of the I/O stack automatically provide the guaranteed lower bounds on I/O throughput. Lastly, our experimental results demonstrate that SIREN provides performance isolation among scientific workflows sharing multiple storage servers across two I/O layers (burst buffer and parallel file systems) while maintaining high system scalability and resource utilization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design

High-performance districts and communities offer opportunities for reducing energy use, emissions, and costs, and can be instrumental in helping cities achieve their climate goals. The design of such communities requires identification of opportunities early on and their re-evaluation throughout the planning process. There is a need for energy modeling tools that connect 3D Computer-Aided Design (CAD) platforms to simulation engines, enabling detailed energy analysis of districts within the workflows and tools used by practitioners. This paper introduces the Dragonfly and URBANoptTM combined toolset that supports the creation of urban models from a range of geometry formats typically used by designers and planners, and provides an integrated pathway to simulate district-scale energy systems. The toolset is piloted by a global architecture and master planning firm to evaluate several key urban-scale technical questions for the design of a district in Chicago. The findings indicate that, while energy savings can be achieved through traditional architectural studies and enhancements to individual building efficiency, the modeling toolset helps identify additional savings and insights that can be achieved when considering district-scale energy systems. Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2022

Quantifying the atomistic free-volume morphology of materials with graph theory

Here, we introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2016

MO-B-BRC-03: CT-Based Prostate HDR

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-B-BRC-04: MRI-Based Prostate HDR

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-B-BRC-00: Prostate HDR Treatment Planning - Considering Different Imaging Modalities

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-B-BRC-01: Introduction [Brachytherapy ]

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-B-BRC-02: Ultrasound Based Prostate HDR

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
DOE OSTI2021

CARDINAL

Cardinal is a code that automates the workflow for iteratively solving neutral particle transport via the OpenMC code and computational fluid dynamics via the NekRS code within the Multiphysics Object Oriented Simulation Environment (MOOSE), a finite element framework for solving general partial differential equations. Cardinal establishes a mapping between the Monte Carlo geometry representation used in OpenMC and the spectral element mesh used in NekRS to other physics applications built upon the MOOSE framework, allowing high-resolution particle transport and fluid dynamics physics feedback to the MOOSE application ecosystem. Heat generation rates as solved by OpenMC are used as heat sources in MOOSE, while the temperature and density fields from NekRS are used as conjugate heat transfer boundary conditions and/or source fields in MOOSE. OpenMC, Nek5000, and MOOSE are open source, community-developed simulation codes.

NOVAK, APRILJEAN RODGERS↗
DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗