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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to those of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Moreover, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A simplified regression building thermal modelling method for detached two-floor house in U.S.

The residential building sector accounts for approximately 37% of total U.S. electricity consumption. Within the residential building sector, heating and cooling is the main target for peak load shifting/reduction since it is the largest contributor to peak demand. In fact, the flexibility of residential HVAC loads can provide continuous variation of demand to provide grid services by varying their demand over a baseline. The performance of HVAC load control to provide grid services relies heavily on the accuracy of indoor air temperature or cooling/heating demand predictions and therefore the quality of building model. Besides forward models, popular building models are data-driven models which can be broken down into two categories: gray-box model, e.g. Resistance-Capacitance (RC) model and black-box model. RC model, also called lumped capacitance or network model, which is constituted with electrical analogue pattern with resistance (R) and capacitance (C). In general, RC models require considerable computation burden and long periods of data to train limited number of model coefficients. Black-box models have gained increasing interest due to their capability in analyzing large-scale data and flexibility in practical applications. But, the data-mining based (machine learning algorithms/techniques based) models tend to have invisible model structures which poses a problem when trying to use the model for optimal control or model predictive control of the HVAC system. Hence, there is a continuing need for efficient online system identification techniques, which can provide explicit parameters for the model. Traditional regression models fit well for this specific purpose. This paper presents an innovative way to predict average indoor temperature in separate floors of typical detached residential house. A rolling horizon linear regression model, which includes online adaptive correction component, is proposed to predict the temperature difference between downstairs and upstairs. A RC model is used to predict the overall mean indoor air temperature. Since the adaptive algorithm needs to be implemented online, a less computation-demanding polynomial fitting algorithm is adopted. This kind of fitting problem can be cast as linear regression problem with multiple variables, parameters of which can be efficiently obtained by well-known gradient descent method.The validation is conducted by comparing the predicted results with the results from data-mining based models as well as measured data from a real typical detached two-floor house. The results show that the developed method has satisfactory performance in predicting the building indoor temperature in 1st and 2nd floors.

Cui, Borui↗
DOE OSTI2018

Active microphonic noise cancellation in radiation detectors

A new adaptive filtering technique to reduce microphonic noise in radiation detectors is presented. The technique is based on system identification that actively cancels the microphonic noise. A sensor is used to measures mechanical disturbances that cause vibration on the detector assembly, and the digital adaptive filtering estimates the impact of these disturbances on the microphonic noise. The noise then can be subtracted from the actual detector measurement. In this paper the technique is presented and simulations are used to support this approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Identification and assignment of rotational spectra using artificial neural networks

A method of identifying molecular parameters may include receiving observed transition frequencies, generating transition frequency sets and a spectral parameter sets, training one or more artificial neural networks by analyzing the transition frequency sets and the spectral parameter sets, analyzing the observed transition frequencies using the one or more trained artificial neural networks to predict estimated spectral parameters, and identifying molecular parameters by analyzing the estimated spectral parameters. A molecular parameter identification system may include a rotational spectrometer, a user interface, and a spectrum analysis application that may retrieve observed transition frequencies, identify a Hamiltonian type by a neural network analyzing the observed transition frequencies, select a second trained artificial neural network based on the identified Hamiltonian type, analyze observed transition frequencies using the second artificial neural network to identify estimated spectral parameters, and identify molecular parameters.

Prozuments, Kirills↗
DOE OSTI2023

Advances in Integral and Separate Effects Experiments for Water Cooled Small Modular Reactors

This paper focuses on the progress being made in water-cooled small modular reactor (SMR) advanced integral effects and separate effects experiments for reactor licensing. SMRs, considered modular in design, are mostly factory-built and then shipped to the reactor site. Of the several types of SMR designs available, water-cooled SMRs are likely to receive regulatory approval faster than others, as most of the technologies involved (e.g., the fuel and coolant technologies) are matured. However, the unique safety systems of SMRs, which depend on specific SMR design features, require integral and separate effects experiments to achieve reactor licensing. Thus, of the many SMR designs being proposed, only a few have successfully undergone licensing and reached the final development and demonstration stage. Many nuclear vendors and newcomer companies are investing millions of dollars to develop integral and separate effects testing facilities for preparing final safety analysis reports to include in licensing applications. Development and analysis of these experimental facilities is costly and takes about four to five years. The unique challenges involved can be reduced when stakeholders synergistically apply lessons learned, knowing the critical role played by advancements in experiments that support the licensing of SMR safety systems. Identification of knowledge/research gaps with the phenomena identification and ranking table (PIRT) and designing experimental facilities focusing on the phenomena of interest (POI) and figures of merit (FOMs) are pivotal to select the critical path to successful design demonstration and licensing application.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

An Approach to Uncertainty Identification in the Valuation of Transactive Energy Systems

Valuation of transactive energy (TE) systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision making. The proposed approach, a variation of fault tree analysis, is anticipated to support valuation analysts in analyzing conventional and transactive system scenarios. This approach allows for expanding the entire tree up to the level of minute details or collapsing them to a level sufficient enough to get an overview of the problem. Quantification scheme for the described approach lends itself for valuation. The method complements value exchange analysis, simulation, and field demonstration studies. The practicality of the proposed approach is demonstrated through uncertainty assessment of the smart grid interoperability panel peak heat day scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Plug load management system with load identification

The present disclosure relates to a plug load management system having automatic and dynamic load detection, meaning it has the ability to identify devices that are plugged into outlets of a building and determine the location of the plug load down to the specific outlet. When a device is moved, the plug load management system can determine this change and update accordingly.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Distributed Model Identification Algorithm for Multi-Agent Systems: Preprint

In this study, we investigate agent-based approach for system model identification with emphasis on power distribution system applications. Departing from conventional practices of relying on historical data for offline model identification, we adopt online update approach utilizing real-time data by employing the latest data points for gradient computation. This methodology offers advantages including a large reduction in the communication network's bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt in real-time to disturbances. Furthermore, we extend our model identification process from linear frameworks to more complex non-linear convex models. This extension is validated through numerical studies demonstrating improved control performance for a synthetic IEEE test case.

data-driven control↗
DOE OSTI2018

Probabilistic N - k failure-identification for power systems

This work considers a probabilistic generalization of the N - k failure-identification problem in power transmission networks, where the probability of failure of each component in the network is known a priori and the goal of the problem is to find a set of k components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the k components. The resulting problem is formulated as a bilevel mixed-integer nonlinear program. Convex relaxations, linear approximations, and heuristics are developed to obtain feasible solutions that are close to the optimum. A general cutting-plane algorithm is proposed to solve the convex relaxation and linear approximations of the N - k problem. Extensive numerical results corroborate the effectiveness of the proposed algorithms on small-, medium-, and large-scale test instances; the test instances include the IEEE 14-bus system, the IEEE single-area and three-area RTS96 systems, the IEEE 118-bus system, the WECC 240-bus test system, the 1354-bus PEGASE system, and the 2383-bus Polish winter-peak test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Mass Transport in Membrane Systems: Flow Regime Identification by Fourier Analysis

The numerical calculation of local mass distributions in membrane systems by computational fluid dynamics (CFD) offers indispensable benefits. However, the concept to calculate such distributions in response to separate variations of operation conditions (OCs) makes it difficult to address overall, flow-physics-related questions, which require the consideration of the collective interaction of OCs. It is shown that such understanding-related relationships can be obtained by the analytical solution of the advection–diffusion equation considered. A Fourier series model (FSM) is presented, which provides exact solutions of an advection–diffusion equation for a wide range of OCs. On this basis, a new zeroth-order model is developed, which is very simple and as accurate as the complete FSM for all conditions of practical relevance. Advection-dominated blocked and diffusion-dominated unblocked flow regimes are identified (depending on a Péclet number which compares the flow geometry with a length scale imposed by the flow), which implies relevant requirements for the use of lab results for pilot- and full-scale applications. Analyses reveal the equivalence of variations of OCs, which offers a variety of options to accomplish desired flow regime changes.

Heinz, Stefan (ORCID:0000000248712416)↗
DOE OSTI2019

A five-level classification system for proteoform identifications

The term proteoform, introduced in these pages in 2013, has rapidly gained acceptance in the proteomics community. The challenge and importance of comprehensively identifying proteoforms in complex samples has been recognized, and reports have begun to appear of new platforms towards that end. However, one interesting central ambiguity has emerged, namely determining precisely what is meant by a “proteoform identification”. At present, the only practical approaches for establishing the exact primary structure of a proteoform employ mass spectrometry (MS), and there is a wide range of MS results that provide “proteoform identifications”. This seemingly small matter has significant impact, as the ambiguity in knowing what is meant by an “identification” makes it difficult to compare results from different laboratories and approaches. This situation hinders the ability of the community to evaluate technological progress and to efficiently expand biological knowledge. To address this issue, we propose a five-level system for classifying proteoform identifications. Here, the classification scheme stems directly from a consideration of the four types of possible ambiguity possible for a proteoform identification, ranging from the most subtle (i.e., precise localization of a post-translational modification, or PTM) to the most dramatic (i.e., ambiguity in the gene of origin). The five classes then correspond to the level of ambiguity present in the identification, ranging from no ambiguity at all (Level 1), to ambiguity of all four types (Level 5). Details of the scheme are provided in Table 1 and Supplementary Table 1, with specific use cases and examples provided in Supplementary Figure 1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Updated Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Small Modular Reactors

Beginning in late 2008, Oak Ridge National Laboratory (ORNL) responded to ongoing internal and external studies addressing key questions related to our national electrical energy supply. This effort has led to the development and refinement of Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE), a tool to support power plant siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of geographic information systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The basic premise requires the development of exclusionary, avoidance, and suitability criteria for evaluating sites for a given siting application such as siting small modular reactors (SMRs). For specific applications of the tool, it is necessary to develop site selection and evaluation criteria (SSEC) that encompass a number of key benchmarks that essentially form the site environmental characterization for that application. These SSEC might include population density, seismic activity, proximity to water sources, proximity to hazardous facilities, avoidance of protected lands and floodplains, susceptibility to landslide hazards, and others.

GIS↗
DOE OSTI2012

Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources

Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS input parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the contiguous United States. If a cell meets the requirements of each criterion, the cell is deemed a candidate area for siting a specific power generation form relative to a reference plant for that power type. Some SSEC parameters preclude siting a power plant because of an environmental, regulatory, or land-use constraint. Other SSEC assist in identifying less favorable areas, such as proximity to hazardous operations. All of the selected SSEC tend to recommend against sites. The focus of the ORNL electrical generation source siting study is on identifying candidate areas from which potential sites might be selected, stopping short of performing any detailed site evaluations or comparisons. This approach is designed to quickly screen for and characterize candidate areas. Critical assumptions supporting this work include the supply of cooling water to thermoelectric power generation; a methodology to provide an adequate siting footprint for typical power plant applications; a methodology to estimate thermoelectric plant capacity while accounting for available cooling water; and a methodology to account for future ({approx}2035) siting limitations as population increases and demands on freshwater sources change. OR-SAGE algorithms were built to account for these critical assumptions. Stream flow is the primary thermoelectric plant cooling source evaluated in this study. All cooling was assumed to be provided by a closed-cycle cooling (CCC) system requiring makeup water to account for evaporation and blowdown. Limited evaluations of shoreline cooling and the use of municipal processed water (gray) cooling were performed. Using a representative set of SSEC as input to the OR-SAGE tool and employing the accompanying critical assumptions, independent results for the various power generation sources studied were calculated.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Squeeze Film Damper Experimental and Numerical Correlation: Test Setup Description and Parameter Identification of Dry System

The process of characterizing a damper involves both experimental techniques and model simulation. Experiments characterize dynamic behavior of a system through empirical modal analysis using impact and shaker testing. The layout of the test setup is crucial for a low-mass, nonlinear system. Physical arrangement, orientation of the system, and instrumentation affect the results that are discussed in this paper. These results can be used to validate a numerical simulation of the same system. With a validated model, important physical input parameters and modeling techniques can be determined and implemented in future designs. In other words, this modeling can be used with confidence to design alternative dampers that may be difficult, costly, and time-consuming to characterize experimentally.

Cook, Jason↗