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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration of software tools for integrative modeling of biomolecular systems

Integrative modeling computes a model based on varied types of input information, be it from experiments or prior models. Often, a type of input information will be best handled by a specific modeling software package. In such a case, we desire to integrate our integrative modeling software package, Integrative Modeling Platform (IMP), with software specialized to the computational demands of the modeling problem at hand. After several attempts, however, we have concluded that even in collaboration with the software's developers, integration is either impractical or impossible. The reasons for the intractability of integration include software incompatibilities, differing modeling logic, the costs of collaboration, and academic incentives. In the integrative modeling software ecosystem, several large modeling packages exist with often redundant tools. So we reason, therefore, that the other development groups have similarly concluded that the benefit of integration does not justify the cost. As a result, modelers are often restricted to the set of tools within a single software package. The inability to integrate tools from distinct software negatively impacts the quality of the models and the efficiency of the modeling. As the complexity of modeling problems grows, we seek to galvanize developers and modelers to consider the long-term benefit that software interoperability yields. In this article, we formulate a demonstrative set of software standards for implementing a model search using tools from independent software packages and discuss our efforts to integrate IMP and the crystallography suite Phenix within the Bayesian modeling framework.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software

The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.

14 SOLAR ENERGY↗
DOE OSTI2014

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Integrated, Interoperable Software Environment for Fusion Simulation and Data Analysis Tools SBIR Phase II

This SBIR effort was focused on developing a production ready system to address the integration and interoperability challenges with analysis and visualization in fusion simulations. Our overarching technical objective was to minimize the code development simulation scientists incur when coupling their simulation codes with different analysis frameworks. To this end, we developed an open-source software library to make data exchange between application easier and a Web application to manage and display analysis extracts from simulations. We have also augmented existing libraries funded by DOE such as ADIOS and VTK-m. When used together, these make it significantly easier to integrate simulation and analysis capability. We demonstrated the flexibility of our approach using two common simulation codes in the fusion community, XGC1 and GTC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the solution of Bayesian inverse problems governed by complex forward models described by partial differential equations (PDEs) remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We present hIPPYlib-MUQ, an extensible and scalable software framework that contains implementations of state-of-the art algorithms aimed to overcome the challenges of high-dimensional, PDE-constrained Bayesian inverse problems. These algorithms accelerate MCMC sampling by exploiting the geometry and intrinsic low-dimensionality of parameter space via derivative information and low rank approximation. The software integrates two complementary open-source software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse problems using automatically-generated adjoint-based derivatives, but it lacks full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients and Hessians to permit large-scale solution. By combining these two complementary libraries, we created a robust, scalable, and efficient software framework that realizes the benefits of each and allows us to tackle complex large-scale Bayesian inverse problems across a broad spectrum of scientific and engineering disciplines. To illustrate the capabilities of hIPPYlib-MUQ, we present a comparison of a number of MCMC methods available in the integrated software on several high-dimensional Bayesian inverse problems. These include problems characterized by both linear and nonlinear PDEs, various noise models, and different parameter dimensions. The results demonstrate that large (~ 50×) speedups over conventional black box and gradient-based MCMC algorithms can be obtained by exploiting Hessian information (from the log-posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ITreeForeCast: An integrated modeling software to simulate tree level growth and forest carbon storage

Healthy trees in forest act as a natural carbon sink, capturing carbon. As they grow, they store carbon in their trunks, leaves and roots. Not all trees store carbon at the same rate, or in the same quantities, as it depends on a variety of biophysical and climatic factors. Furthermore, although carbon estimation in trees can be complex, the precision of estimates is tightly linked to trees growth, both in diameter and height. However, the simulation of carbon uptake by forest and forest growth has each been modeled separately, and independently at differing levels of detail and spatial resolution. In this paper, we introduce ITreeForeCast, a simulation model combining the two types of modeling on a unified platform, enabling the investigation of impacts of management strategies on carbon sequestration and wood products. ITreeForeCast is a user-extendable framework that offers new opportunities to model, simulate, and visualize the dynamics of individual trees in a forest, simulate management strategies over time, and carbon uptake.

09 - BIOMASS FUELS↗
DOE OSTI2018

Final Scientific/Technical Report: Integrating Advanced Software and Statistical Methods for Spectroscopic Dark Energy Surveys

“Dark energy” is a phenomenon of unknown origin that pervades the vacuum of space and is causing the present-day expansion of the universe to accelerate. Understanding the nature of dark energy is one of the most fundamental challenges to modern physics, having the potential to revolutionize our theories of both gravity and elementary particle physics. The effects of dark energy are currently only accessible to astronomical survey experiments that probe large volumes of the cosmos. One of the most powerful forms of experiment for studying dark energy is the cosmological “redshift survey,” which maps the large-scale structure of the universe as traced by the positions of galaxies, quasars, and intergalactic hydrogen clouds. The signature of dark energy and other cosmological parameters is imprinted on this large-scale structure as it evolves over cosmic time. This project focused on maximizing the accurate extraction of information from redshift surveys by improving the algorithms and statistics applied across the chain of inference from raw pixels to high-level cosmological parameter estimation. At the low level, this project developed new techniques for calibrating and analyzing high-background, low signal-to-noise data from astronomical survey spectrographs to provide reliable three-dimensional maps of galaxies and quasars throughout the universe. At the high level, this project developed new statistical frameworks for understanding the completeness of redshift surveys and the underlying demographics of the galaxies that they observe, thereby allowing for a more accurate connection between survey results and theoretical models. This work contributes to maximizing the scientific reach of existing and planned surveys, and opens new potential feasibility for more ambitious surveys in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

NERSC Slurm Software Configurations and Plugins

This software can be used to tune the HPC operations of a Cray system running Slurm workload manager software. This includes many of the customizations that NERSC has made over the past three years. In particular these customizations allow for complex site-local policies to be implemented and managed in a scalable way, or add unique capabilities to slurm via our plugins, including: * performance software integration and compute node staging * temporary directory/dynamic user content management automation * software defined networking integration

Jacobsen, Douglas↗
DOE OSTI2013

A Full-Featured User Friendly CO 2 -EOR and Sequestration Planning Software

A Full-Featured, User Friendly CO 2 -EOR and Sequestration Planning Software This project addressed the development of an integrated software solution that includes a graphical user interface, numerical simulation, visualization tools and optimization processes for reservoir simulation modeling of CO 2 -EOR. The objective was to assist the industry in the development of domestic energy resources by expanding the application of CO 2 -EOR technologies, and ultimately to maximize the CO 2 } sequestration capacity of the U.S. The software resulted in a field-ready application for the industry to address the current CO 2 -EOR technologies. The software has been made available to the public without restrictions and with user friendly operating documentation and tutorials. The software (executable only) can be downloaded from NITEC’s website at www.nitecllc.com. This integrated solution enables the design, optimization and operation of CO 2 -EOR processes for small and mid-sized operators, who currently cannot afford the expensive, time intensive solutions that the major oil companies enjoy. Based on one estimate, small oil fields comprise 30% of the of total economic resource potential for the application of CO 2 -EOR processes in the U.S. This corresponds to 21.7 billion barrels of incremental, technically recoverable oil using the current “best practices”, and 31.9 billion barrels using “next-generation” CO 2 -EOR techniques. The project included a Case Study of a prospective CO 2 -EOR candidate field in Wyoming by a small independent, Linc Energy Petroleum Wyoming, Inc. NITEC LLC has an established track record of developing innovative and user friendly software. The Principle Investigator is an experienced manager and engineer with expertise in software development, numerical techniques, and GUI applications. Unique, presently-proprietary NITEC technologies have been integrated into this application to further its ease of use and technical functionality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy

Purpose: In this study, we develop an integrated software platform for adaptive radiation therapy (ART) that combines fast and accurate image registration, segmentation, and dose computation/accumulation methods. Methods: The proposed system consists of three key components; 1) deformable image registration (DIR), 2) automatic segmentation, and 3) dose computation/accumulation. The computationally intensive modules including DIR and dose computation have been implemented on a graphics processing unit (GPU). All required patient-specific data including the planning CT (pCT) with contours, daily cone-beam CTs, and treatment plan are automatically queried and retrieved from their own databases. To improve the accuracy of DIR between pCT and CBCTs, we use the double force demons DIR algorithm in combination with iterative CBCT intensity correction by local intensity histogram matching. Segmentation of daily CBCT is then obtained by propagating contours from the pCT. Daily dose delivered to the patient is computed on the registered pCT by a GPU-accelerated superposition/convolution algorithm. Finally, computed daily doses are accumulated to show the total delivered dose to date. Results: Since the accuracy of DIR critically affects the quality of the other processes, we first evaluated our DIR method on eight head-and-neck cancer cases and compared its performance. Normalized mutual-information (NMI) and normalized cross-correlation (NCC) computed as similarity measures, and our method produced overall NMI of 0.663 and NCC of 0.987, outperforming conventional methods by 3.8% and 1.9%, respectively. Experimental results show that our registration method is more consistent and roust than existing algorithms, and also computationally efficient. Computation time at each fraction took around one minute (30–50 seconds for registration and 15–25 seconds for dose computation). Conclusion: We developed an integrated GPU-accelerated software platform that enables accurate and efficient DIR, auto-segmentation, and dose computation, thus supporting an efficient ART workflow. This work was supported by NIH/NCI under grant R42CA137886.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Perspective on Sustainable Computational Chemistry Software Development and Integration

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2023

Verification of Spent Fuel Inside Dry Storage Casks by Cask Top Fast Neutron Mapping (FY2023 Mid-Year Report)

This project is developing a prototype scanner array verification system for detection of missing fuel assemblies in spent-fuel storage casks. The prototype consists of six fast-neutron scintillator detectors mounted to a linear actuator frame that is placed on the top of a spent fuel cask to scan across all fuel assembly positions. The scanner array was assembled and tested at LLNL in FY2022. A field test schedule has been requested at the Idaho National Laboratory (INL) Cask Farm site for FY2023. Note that the Cask Farm contractor determines this scheduling and not INL directly. Further system automation will be designed and implemented with the goal of obtaining a level of system operation that meets IAEA needs. This includes integration of the scanner array and data-acquisition control software into a single interface for operator use. In addition, commercial operators and the IAEA may have special requirements for portability, shipping, lifting, and installation. Prior to the Field Test at INL, the system will be operated at LLNL to exercise lifting procedure and linear actuators, monitor stability of detector energy and pulse-shape discrimination calibration, and test system software integration efforts. Following the Field Test, we will present results and discuss the technology with the IAEA. We will incorporate additional improvements to the system based on lessons learned from the field test and feedback from the IAEA. If successful, the technology can be transferred to the IAEA or other stakeholders for assessment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Smart Building Start (SBS) (CRADA 480)

The idea of Smart Building Start (SBS) is to manage the on/off scheduling of HVAC equipment in buildings in an optimal way in order to achieve comfort conditions at the designated times with the minimal use of energy. This concept is also known as Optimal Start and it has typically been used in buildings to start up equipment based on predictions made for a representative (or average) zone in a building. This means that zones that could have waited longer to receive conditioned air are activated too early leading to unnecessary energy loss. The approach proposed in this project is to develop and implement a distributed version of optimal start that will consider the individual requirements of all treated zones. The developed control strategy will enable smart start of the central plant as well as further localized control over each individual zone by means of setpoint adjustment so that zones are not conditioned too early thereby reducing energy use compared to the current state of the art. This project is a collaboration between PNNL and Verdicity with the goal being to bring the research concept of SBS to commercialization. The SBS algorithm(s) will be implemented and hosted on the PNNL-developed open-platform VOLTTRON which will accelerate bringing the research to practice. It is anticipated that this project will lead to new IP surrounding the algorithm and its decentralized deployment. The project will also further demonstrate the success of VOLTRON as a flexible platform for implementation of innovative algorithms for improving energy efficiency in buildings. Previous studies on smart building start carried out by PNNL have indicated potential energy savings on the order of 10-15% for multi-zone buildings compared to conventional strategies. Verdicity currently has a number of customers who can benefit from the use of this technology. PNNL will work with Verdicity to identify any key gaps in the integrated software and jointly work to enhance and customize it for Verdicity and to integrate it with their existing products for use at their customers’ sites. The proposed work will help previously funded Federal research and development technology investments find their way into a viable commercial market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗