Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “resource modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Blockchain-Enabled Secure Device-to-Device Communication in Software-Defined Networking

The Internet of Things (IoT) continues to increase the demand for seamless communication among IoT devices. The rapid growth of IoT devices has led to an exponential increase in device-to-device (D2D) communication within the Software-Defined Networking (SDN), though it enables a flexible archi-tecture for managing network resources. However, traditional security models face challenges (e.g., Security, privacy, and trust) in addressing the dynamic and decentralized nature of these communications. Despite of these challenges, this paper proposes a novel approach that leverages blockchain technology to enhance the security, privacy, and trustworthiness of D2D communication within an SDN environment. The proposed approach integrates blockchain nodes in sDN components to establish a decentralized ledger for transparent and verifiable records. Smart contracts enforce authentication rules to ensure that only authenticated devices can access the network and engage in transactions securely. It also automates the security policies to ensure temper resistance execution using the cryptographic mechanism for data integrity and authentic communication. The Implementation of the proposed algorithms validates the resilience of the proposed approach against cyberattacks. Overall, the proposed approach enables efficient and secure D2D communication for resilient SDN infrastructure in IoT ecosystems.

Das, Debashis↗
DOE OSTI2024

Simulation-Guided Decision-Making for Enhancing Energy Resilience in a Remote Alaskan Community

The increasing frequency and severity of extreme weather events underscore the need to bolster the resilience of energy infrastructure in remote coastal communities exposed to climate hazards. In recent years, considerable effort has been made to harden the grid infrastructure of remote communities through investment in energy storage, advanced metering, renewable generation and energy-efficient loads. However, simulation-based studies are still needed to identify appropriate locations for investment and determine the adequacy of existing infrastructure in supporting new resources. Preparing the required models may often be challenging due to insufficient metering, disparate data sources and workforce limitations. This paper describes modeling efforts undertaken to represent, within a unified co-simulation platform: (a) the electric distribution network and (b) the thermal behavior of a community medical center, in the remote community of Cordova, Alaska. With the help of case-studies, it is shown how the developed simulation platform can help the local utility in making decisions regarding capital investment aimed at enhancing community resilience.

Microgrid, energy storage, resilience↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2018

Experimental Validation of an Explicit Power-Flow Primary Control in Microgrids

The existing approaches to control electrical grids combine frequency and voltage controls at different time-scales. When applied in microgrids with stochastic distributed generation, grid quality of service problems may occur, such as under- or overvoltages as well as congestion of lines and transformers. The COMMELEC framework proposes to solve this compelling issue by performing explicit control of power flows with two novel strategies: 1) a common abstract model is used by resources to advertise their state in real time to a grid agent; and 2) subsystems can be aggregated into virtual devices that hide their internal complexity in order to ensure scalability. While the framework has already been published in the literature, in this paper, we present the first experimental validation of a practicable explicit power-flow primary control applied in a real-scale test-bed microgrid. We demonstrate how an explicit power-flow control solves the active and reactive power sharing problem in real time, easily allowing the microgrid to be dispatchable in real time (i.e., it is able to participate in energy markets) and capable of providing frequency support, while always maintaining quality of service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Detecting Drawdowns Masked by Environmental Stresses with Water-Level Models

Detecting and quantifying small drawdown at observation wells distant from the pumping well greatly expands the characterized aquifer volume. However, this detection is often obscured by water level fluctuations such as barometric and tidal effects. A reliable analytical approach for distinguishing drawdown from nonpumping water-level fluctuations is presented and tested here. Drawdown is distinguished by analytically simulating all pumping and nonpumping water-level stresses simultaneously during the period of record. Pumping signals are generated with Theis models, where the pumping schedule is translated into water-level change with the Theis solution. This approach closely matched drawdowns simulated with a complex three-dimensional, hypothetical model and reasonably estimated drawdowns from an aquifer test conducted in a complex hydrogeologic system. Pumping-induced changes generated with a numerical model and analytical Theis model agreed (RMS as low as 0.007 m) in cases where pumping signals traveled more than 1 km across confining units and fault structures. Maximum drawdowns of about 0.05 m were analytically estimated from field investigations where environmental fluctuations approached 0.2m during the analysis period.

58 GEOSCIENCES↗
DOE OSTI2021

Security assessment and impact analysis of cyberattacks in integrated T&D power systems

In this paper, we examine the impact of cyberattacks in an integrated transmission and distribution (T&D) power grid model with distributed energy resource (DER) integration. We adopt the OCTAVE Allegro methodology to identify critical system assets, enumerate potential threats, analyze and prioritize risks for threat scenarios. Based on the analysis, attack strategies and exploitation scenarios are identified which could lead to system compromise. Specifically, we investigate the impact of data integrity attacks in inverted-based solar PV controllers, control signal blocking attacks in protective switches and breakers, and coordinated monitoring and switching time-delay attacks. Index Terms—Cyberattacks, security assessment, impact analysis, case studies, integrated power systems.

14 SOLAR ENERGY↗
DOE OSTI2026

Decision Support System

Forest-based value chains involve decisions that begin at the landscape level and extend through processing, product manufacturing, and end-use markets. However, these decisions are often made independently across sectors, with limited visibility into how upstream resource conditions, incentives, and land management choices influence downstream production systems. In forested regions of the United States, wildfire risk, fragmented ownership, and uncertain markets for low-value residues complicate efforts to align extraction, processing, and utilization decisions. Without tools that link these stages, stakeholders may overlook opportunities to improve resource utilization or inadvertently shift impacts elsewhere in the value chain. This repository introduces a decision support system (DSS) that applies a system-impact-analysis approach to forest biomass residues and co-products. The framework integrates forest inventory data, geospatial resource assessments, and economic modeling to evaluate how biomass extraction decisions influence downstream product pathways. By linking regional feedstock avail- ability with market incentives and processing options—such as fuels, wood products, or soil amendments like biochar—the tool allows decision-makers to compare value chain outcomes across multiple utilization strategies.

Davis, Maggie [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

Ultrahigh-resolution mass spectrometry data associated with the manuscript “A functional microbiome catalog crowdsourced from North American rivers"

This data package is associated with the publication “A functional microbiome catalog crowdsourced from North American rivers” submitted to Nature (Borton et al., 2024); (https://www.biorxiv.org/content/10.1101/2023.07.22.550117v1). Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial determinants governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). GROWdb is a publicly available resource that paves the way for watershed predictive modeling and microbiome-based management practices. This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. We identified the most cosmopolitan microbiome members, while also revealing local drivers of strain endemism across ecological dimensions. We provide the first evidence that microbial functional trait expression followed the tenets of the River Continuum Concept, suggesting the structure and function of river microbiomes is predictable. The Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data were one of many different data types used in establishing the ecological dimensions along which different microbes were detected .This data package only contains the processed FTICR-MS data associated with this manuscript; all other data is accessible via Zenodo (https://zenodo.org/records/8173287), GitHub (https://github.com/jmikayla1991/Genome-Resolved-Open-Watersheds-database-GROWdb), KBase (https://doi.org/10.25982/109073.30/1895615), and NCBI via Bioproject PRJNA946291.This dataset consists of (1) a file-level metadata (flmd) file; (2) a data dictionary (dd) file; (3) a readme; (4) three Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) processed data files (a ‘data’ file containing peak-by-sample observations, a ‘mol’ file containing peak metadata, and a transformation profile containing transformation-by-sample observations). All files are .csv or .pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Energy Efficient Streaming Time Series Classification with Attentive Power Iteration

Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Extending the Remote Control Capabilities in the CMS Detector Control System with Remote Procedure Call Services

The CMS Detector Control System (DCS) is implemented as a large distributed and redundant system, with applications interacting and sharing data in multiple ways. The CMS XML-RPC is a software toolkit implementing the standard Remote Procedure Call (RPC) protocol, using the Extensible Mark-up Language (XML) and a custom lightweight variant using the JavaScript Object Notation (JSON) to model, encode and expose resources through the Hypertext Transfer Protocol (HTTP). The CMS XML-RPC toolkit complies with the standard specification of the XML-RPC protocol that allows system developers to build collaborative software architectures with self-contained and reusable logic, and with encapsulation of well-defined processes. The implementation of this protocol introduces not only a powerful communication method to operate and exchange data with web-based applications, but also a new programming paradigm to design service-oriented software architectures within the CMS DCS domain. This paper presents details of the CMS XML-RPC implementation in WinCC Open Architecture (OA) Control Language using an object-oriented approach.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Evaluation of the Effective Moisture Penetration Depth Model for Estimating Moisture Buffering in Buildings

This study examines the effective moisture penetration depth (EMPD) model, and its suitability for building simulations. The EMPD model is a compromise between the simple, inaccurate effective capacitance approach and the complex, yet accurate, finite-difference approach. Two formulations of the EMPD model were examined, including the model used in the EnergyPlus building simulation software. An error in the EMPD model we uncovered was fixed with the release of EnergyPlus version 7.2, and the EMPD model in earlier versions of EnergyPlus should not be used.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Water Impacts of High Solar PV Electricity Penetration

This analysis provides a detailed national and regional description of the water-related impacts and constraints of high solar electricity penetration scenarios in the U.S. in 2030 and 2050. A modified version of the Regional Energy Deployment System (ReEDS) model that incorporates water resource availability and costs as a constraint in each of its 134 Balancing Area (BA) regions was utilized to explore national and regional differences in water use impacts and solar deployment locations under different solar energy cost and water availability scenarios (Macknick et al. 2015). Water resource availability and cost data are from recently completed research at Sandia National Laboratories (Tidwell et al. 2013a). Scenarios analyzed include two business-as-usual solar energy cost cases, one with and one without considering available water resources, and four solar energy cost cases that meet the SunShot cost goals (i.e., $1/watt for utility-scale PV systems), with varying levels of water availability restrictions. This analysis provides insight into the role solar energy technologies have in the broader electricity sector under scenarios of water constraints.

14 SOLAR ENERGY↗
DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗
DOE OSTI2018

Renewable Energy Data Explorer User Guide

This publication provides a user guide for the Renewable Energy Data Explorer and technical potential tool within the Explorer. The Renewable Energy Data Explorer is a dynamic, web-based geospatial analysis tool that facilitates renewable energy decision-making, investment, and deployment. It brings together renewable energy resource data and other modeled or measured geographic information system (GIS) layers, including land use, weather, environmental, population density, administrative, and grid data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Argonne Reactor Code Software Verification and Validation Plan for the Versatile Test Reactor

This Argonne Reactor Code (ARC) software has taken a pivotal role in the design work of the Versatile Test Reactor. The ARC software itself consists of several independent or semiindependent pieces of software that are used in concert to carry out specific analysis and design objectives. In this report, we discuss the validation that is needed for the ARC software overall and discuss available, usable experimental data and how it relates to the VTR project. We also discuss the verification that is required for the ARC software with respect to its usage in the VTR project and give some concrete examples as to what it must accomplish. A list of goals for the verification and validation work are presented as the general procedure that will be implemented for every individual piece of software in ARC. The ARC software has been continuously developed for over 50 years and many of the features used for the VTR project have thus been rigorously tested for accuracy during that timeframe. The ARC software is presently under source code control and undergoes nightly regression testing of not only the individual pieces, but the interconnected aspects that are common to most analysis projects. Its greatest weakness, from a software verification and validation perspective, is both a lack of documentation and outdated documentation. As an example, much of the previous validation work will require contemporary effort to re-evaluate given the updated base data libraries and modifications to the software that have occurred since. This requires new documentation to be produced to support the usage of the software in the VTR project. The bulk of the ARC software is focused on steady state neutronics and TH behavior of a reactor system. In this regard, we have identified the ZPPR, FFTF, and EBR-II programs as the best candidates for sources of validation data for the VTR project. The ZPPR-15 series in the ZPPR program was chosen as it is the best representation of the VTR fuel form and reactor core layout of all of the ZPPR measurements. Additional support for it comes from the fact that most of the data has already been processed into usable ARC models for VTR validation. The FFTF and EBR-II reactors were both functional reactors that provide reactor scale measurements. FFTF is clearly a closer match to VTR and its startup measurements would be a very valuable validation basis. EBR-II has invaluable burnup related measurements that will play a key role in the ARC software validation work. Each piece of ARC software will require a report detailing the software verification. The validation work will be logically split into the results of modeling each identified experimental resource (ZPPR, EBR-II, FFTF). The intended timeline for the VTR project is to have the ARC software verified and validated by the end of fiscal year 2020. This is incumbent on sufficient funding allocated to V&V activities and no change in programmatic priorities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Panorama 360 (Final Report)

This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗