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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Multiple response optimization for higher dimensions in factors and responses

When optimizing a product or process with multiple responses, a two-stage Pareto front approach is a useful strategy to evaluate and balance trade-offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non-contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphical summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. As a result, recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Market-Based and System-Wide Fuel Cycle Optimization

The Dynamic Resource Exchange (DRE) gives agency to consumer facilities to determine the preference of any particular trade that is offered by suppliers to satisfy its requests. This provides a natural balance of power in the relationship between consumers and suppliers. However, in situations in which suppliers have flexibility surrounding the way that they respond to individual requests, they have no mechanism to assess how different bids will be received by the consumer. Theoretically, a supplier can offer multiple bids to respond to a given request in an attempt to “cover their bases”, but this introduces more arcs into the underlying network flow problem, increasing the cost to solve the problem. In the extreme, when a supplier can continuously vary the characteristics of the bid, this can represent a large number of additional arcs and have real performance consequences. To remedy this inefficiency in the implementation of the market-level optimization, the definition of a request has been extended to include a function that can be used by the supplier to query the preference that would be assigned by a consumer for a potential bid. The supplier is then free to implement arbitrarily complex algorithms to revise/optimize its bid based on responses to this function. A supplier can chose to not invoke the function at all, mimicking the original DRE behavior, can use it to select among a small set of discrete choices, or can implement an internal algorithm to seek an optimum bid on a continuous parameter space. This capability was demonstrated with a storage facility that preferred material with a specific decay heat that was as close as possible to the maximum allowable decay heat, while requiring the specific decay heat to fall between a minimum and maximum level. This archetype was used to fill multiple storage roles in a simulation that also included a standard recipe reactor: wet storage with no maximum allowable specific decay heat, dry storage with a modest maximum allowable specific decay heat, and a geologic repository with a low maximum allowable specific decay heat. In such a simulation, the reactor, wet storage and dry storage always offer their material to be taken by one of the other storage facilities. The preference function of the consumer would always ensure that material only flowed when the decay heat was sufficiently low, but in the absence of objective function callbacks, would allow for many superfluous offers that exceeded those limits. If this archetype also uses a callback function to probe the preference of the receiving facility for each possible offer, it can avoid making offers that are not going to be accepted by the receiving facility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Real-Time Optimization Workflow Status Update

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Managing the Risk for Early Onset Osteoporosis in Long-Duration Astronauts Due to Spaceflight

Early Onset Osteoporosis is probably the most recognized but poorly understood long-term health risk due to spaceflight. Osteoporosis management is primarily prophylactic and clinical interventions rely upon the ability to predict fractures which is currently determined by surrogate measures of bone strength. The RMAT for Early Onset Osteoporosis identified some open issues related to the fact that long-duration astronauts compose a unique group of subjects for which clinical approaches for osteoporosis management do not apply. Long-duration astronauts are healthy, young (25 to 55 years of age), predominantly male, and physical fit relative to the typical osteoporosis patient. Moreover, during prolonged space missions (typically 6-month missions) the skeleton not only adapts to weightlessness, but is influenced by numerous risk factors induced by operational constraints, e.g., inability to maintain preflight weight-bearing and aerobic activities, sub-optimal dietary intake (e.g., high sodium content for food stability, lack of fresh fruit and vegetables), suppression of vitamin D metabolism by uv shielding, and remote medicine care. Moreover, adaptation results in novel changes to astronauts bones that cannot be detected by current medically-useful measures. Consequently, a panel of clinicians (recognized leaders and policy-makers in osteoporosis) was convened to review the dataset of bone measures and bone loss risk factors in long-duration astronauts. Driven by the queries in the RMAT, the panel was charged to determine 1) if an intervention is required to prevent this risk, 2) what type and at what time would intervention be optimal, 3) what is the clinical trigger that would require a medical response from flight surgeons and 4) how should research data be used in the clinical care of astronauts. Hence, the RMAT determined that a bone health policy need to be formulated specific for this unique cohort subjected to a novel skeletal condition

Sibonga, Jean D.↗
DOE OSTI2025

PV Degradation Modeling: Applying Geospatial Workflows with "PVDeg"

Accurate degradation modeling is essential for predicting photovoltaic (PV) module performance, estimating longevity and informing design decisions. With degradation rates varying significantly by location, geospatial analysis is critical for PV and broader applications, such as agrivoltaics, weathering and environmental data analysis. This work presents PVDeg, an open-source tool designed for geospatial degradation analysis. PVDeg integrates meteorological data from global sources, including the National Solar Radiation Database (NSRDB) and Photovoltaic Geographical Information System (PVGIS), with degradation models. The toolkit enables users to customize geospatial workflows by integrating weather data, material parameters, and user-defined Python functions. It facilitates accelerated downloads of NSRDB and PVGIS datasets and optimizes geospatial point selection to preserve data density in regions of interest. Additionally, PVDeg provides a local database for storage and spatial queries, supporting large-scale analyses without the need for high-performance computing (HPC) resources. PVDeg provides a foundational workflow that extends its utility beyond PV applications, enabling researchers to analyze geospatial processes across discipline.

14 SOLAR ENERGY↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗
DOE OSTI2018

Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADE

Palomar Transient Factory (PTF) is a comprehensive detection system for the identification and classification of transient astrophysical objects. In this paper, we make two significant contributions to the PTF pipeline. First, we present an experimental study that evaluates a novel implementation of the real-time classifier in GLADE - a parallel data processing system that combines the efficiency of a database with the extensibility of map-reduce. We show how each stage in the classifier maps optimally into GLADE tasks by taking advantage of the unique features of the system - range-based data partitioning, columnar storage, multi-query execution, and in-database support for complex aggregate computation. Second, we introduce a novel parallel similarity join algorithm for advanced transient classification. In conclusion, we implement this algorithm in GLADE and execute it on a massive supercomputer with more than 3,000 threads, achieving more than three orders of magnitude improvement over the PostgreSQL solution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SMART-COM – Scalable Multi-Agent Adaptive Resolution Tools for Collaborative Outage Management

The purpose of this grant was to conduct scientific research and prototype applications to support NPP outage staff in their adaptive decision-making in efficient scheduling and resource allocation while preventing violation of safety technical specifications. The project contributed to scientific knowledge and engineering methods in (1) user interface design, (2) scheduling optimization and risk estimation, and (2) natural language processing that would benefit the nuclear power plants in minimizing schedule overruns and even unexpected shutdowns. The research team conducted site visits at a test reactor facility and an operating nuclear power plant to gather necessary information and inputs for research and development of a software application to support NPP staff in executing their outages. The final software application consisted of three modules. First, the natural language processing module supports interactive processing of technical documentation to build a database for outage staff to query non-permissible actions on system components. This module can alleviate outage staff from reviewing extensive documentation and minimize violation of technical specifications, especially in time-sensitive situations. Second, the schedule optimization module schedules outage activities and compute risk indices that outperform existing software and current practice. This module can reduce completion time of an outage that typically have too many activities for human to optimize based on current practice that does not apply the latest operations research. Finally, the visualization module presents progress and risk information of the overall outage and individual activities, as well as enabling access to the natural language processing and schedule optimization modules. This module can provide outage staff with situation awareness that are necessary to make risk-informed decisions in response to unexpected events during the execution of an outage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Gradient-based constrained optimization using a database of linear reduced-order models

A methodology grounded in model reduction is presented for accelerating the gradient-based solution of a family of linear or nonlinear constrained optimization problems where the constraints include at least one linear Partial Differential Equation (PDE). A key component of this methodology is the construction, during an offline phase, of a database of pointwise, linear, Projection-based Reduced-Order Models (PROM)s associated with a design parameter space and the linear PDE(s). A parameter sampling procedure based on an appropriate saturation assumption is proposed to maximize the efficiency of such a database of PROMs. A real-time method is also presented for interpolating at any queried but unsampled parameter vector in the design parameter space the relevant sensitivities of a PROM. The practical feasibility, computational advantages, and performance of the proposed methodology are demonstrated for several realistic, nonlinear, aerodynamic shape optimization problems governed by linear aeroelastic constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-F-T-96: A New Paradigm of Weekly Chart Checking for Radiation Therapy Clinics

Purpose: To put forth an innovative clinical paradigm for weekly chart checking so that treatment status is periodically checked accurately and efficiently. This study also aims to help optimize the chart checking clinical workflow in a busy radiation therapy clinic. Methods: It is mandated by the Texas Administrative code to check patient charts of radiation therapy once a week or every five fractions, however it varies drastically among institutions in terms of when and how it is done. Some do it every day, but a lot of efforts are wasted on opening ineligible charts; some do it on a fixed day but the distribution of intervals between subsequent checks is not optimal. To establish an optimal chart checking procedure, a new paradigm was developed to achieve 1) charts are checked more accurately and more efficiently; 2) charts are checked on optimal days without any miss; 3) workload is evened out throughout a week when multiple physicists are involved. All active charts will be accessed by querying the R&V system. Priority is assigned to each chart based on the number of days before the next due date followed by sorting and workload distribution steps. New charts are also taken into account when distributing the workload so it is reasonably even throughout the week. Results: Our clinical workflow became more streamlined and smooth. In addition, charts get checked in a more timely fashion so that errors would get caught earlier should they occur. Conclusion: We developed a new weekly chart checking diagram. It helps physicists check charts in a timely manner, saves their time in busy clinics, and consequently reduces possible errors.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

One of biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental database access method, VIEWCACHE, provides such an interface for accessing distributed data sets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image data sets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate distributed database search.

Roussopoulos, N.↗
NASA NTRS1993

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

One of the biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental data base access method, VIEWCACHE, provides such an interface for accessing distributed datasets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image datasets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate database search.

Roussopoulos, N.↗
DOE OSTI2024

Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations

Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter suchgeospatial videoson a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative,build-filter-observeparadigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3×, while maintaining up to 97.1% accuracy compared to unoptimized execution.

Computer Science↗
DOE OSTI2017

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses on a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NCBI’s Virus Discovery Codeathon: Building “FIVE” —The Federated Index of Viral Experiments API Index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings

Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.

Prakash, Anand Krishnan↗