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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Policy Innovation and Governance for Irrigation Sustainability in the Arid, Saline San Joaquin River Basin

This paper provides a chronology and overview of events and policy initiatives aimed at addressing irrigation sustainability issues in the San Joaquin River Basin (SJRB) of California. Although the SJRB was selected in this case study, many of the same resource management issues are being played out in arid, agricultural regions around the world. The first part of this paper provides an introduction to some of the early issues impacting the expansion of irrigated agriculture primarily on the west side of the San Joaquin Valley and the policy and capital investments that were used to address salinity impairments to the use of the San Joaquin River (SJR) as an irrigation water supply. Irrigated agriculture requires large quantities of water if it is to be sustained, as well as supply water of adequate quality for the crop being grown. The second part of the paper addresses these supply issues and a period of excessive groundwater pumping that resulted in widespread land subsidence. A joint federal and state policy response that resulted in the facilities to import Delta water provided a remedy that lasted almost 50 years until the Sustainable Groundwater Management Act of 2014 was passed in the legislature to address a recurrence of the same issue. The paper describes the current state of basin-scale simulation modeling that many areas, including California, are using to craft a future sustainable groundwater resource management policy. The third section of the paper deals with unique water quality issues that arose in connection with the selenium crisis at Kesterson Reservoir and the significant threats to irrigation sustainability on the west side of the San Joaquin Valley that followed. The eventual policy response to this crisis was incremental, spanning two decades of University of California-led research programs focused on finding permanent solutions to the salt and selenium contamination problems constraining irrigated agriculture, primarily on the west side. Arid-zone agricultural drainage-induced water quality problems are becoming more ubiquitous worldwide. One policy approach that found traction in California is an innovative variant on the traditional Total Maximum Daily Load (TMDL) approach to salinity regulation, which has features in common with a scheme in Australia’s Hunter River Basin. The paper describes the real-time salinity management (RTSM) concept, which is geared to improving coordination of west side agricultural and wetland exports of salt load with east side tributary reservoir release flows to improve compliance with river salinity objectives. RTSM is a concept that requires access to continuous flow and electrical conductivity data from sensor networks located along the San Joaquin River and its major tributaries and a simulation model-based decision support designed to make salt load assimilative capacity forecasts. Web-based information dissemination and data sharing innovations are described with an emphasis on experience with stakeholder engagement and participation. The last decade has seen wide-scale, global deployment of similar technologies for enhancing irrigation agriculture productivity and protecting environmental resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument

Abstract In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies, luminous red galaxies, and quasars. In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey and the Milky Way Survey. DESI also observes a selection of “secondary” targets for bespoke science goals. This paper gives an overview of the publicly available pipeline ( desitarget ) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID ( TARGETID ) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe “supporting” DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target-selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope

We report the fourth catalog of active galactic nuclei (AGNs) detected by the Fermi Gamma-ray Space Telescope Large Area Telescope (4LAC) between 2008 August 4 and 2016 August 2 contains objects located at high Galactic latitudes ( ). It includes 85% more sources than the previous 3LAC catalog based on 4 yr of data. AGNs represent at least 79% of the high-latitude sources in the fourth Fermi -Large Area Telescope Source Catalog (4FGL), which covers the energy range from 50 MeV to 1 TeV. In addition, gamma-ray AGNs are found at low Galactic latitudes. Most of the 4LAC AGNs are blazars (98%), while the remainder are other types of AGNs. The blazar population consists of 24% Flat Spectrum Radio Quasars (FSRQs), 38% BL Lac-type objects, and 38% blazar candidates of unknown types (BCUs). On average, FSRQs display softer spectra and stronger variability in the gamma-ray band than BL Lacs do, confirming previous findings. All AGNs detected by ground-based atmospheric Cerenkov telescopes are also found in the 4LAC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

EMT Models to Evaluate Traveling Wave-Based Protection in Distribution System

The distribution system is going through significant changes. One of them is the high penetration of inverter-based distributed energy resources in the distribution system. Due to this high penetration, there is a need for a better protection system. Multiple organizations are working on creating novel protection techniques for distribution systems with high penetration of inverter-based DERs. One such innovative method is the use of traveling waves to detect and locate faults. Transmission system protection utilizes this approach, and commercial solutions exist for the transmission system. Using this approach in the distribution system is not straightforward and presents some challenges for implementation. This presentation will discuss the need for using wide-band models to model the traveling wave phenomenon in the distribution system.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Ultrasonic through-wall sensors

Aspects of ultrasonic through-wall data communication are described. In some aspects, a sensor device is located in an isolated environment behind a wall. A modulator generates a modulated sensor signal based on the binary sensor signal and a carrier signal. A coaxially aligned pair of electroacoustic transducers that transmits the modulated sensor signal through the wall of the isolated environment. A demodulator demodulates the modulated sensor signal to generate a demodulated binary sensor signal. A sensor data recovery component converts the demodulated binary sensor signal into an image or another sensor parameter.

Zhang, Haifeng↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗
DOE OSTI2023

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Blockchain for Fault-Tolerant Grid Operations

Distribution system fault/failure has a direct impact on customers. Distribution systems are not designed with the contingency of one or more elements failing as the transmission system, however, distribution systems are more likely to have faults in comparison to transmission and generation systems. Most of the distribution systems have a radial design with the protection system assumption of unidirectional power flow. The radial design results in the disconnection of customers for any given component failure. The rapid deployment of distributed energy resources conflicts with the assumption of unidirectional power flow. To maintain/improve the distribution system fault tolerance for a grid with high penetration of distributed energy resources requires improvement. Blockchain can add value to improve fault-tolerant grid operations. That can be achieved using blockchain’s core features of distributed consensus-based decision-making process and immutability. In the process of preparing for an event or system restoration, there is a need for reliable data and system situational awareness. The system situational awareness enables accurate planning for possible scenarios and system restoration after an event, an area where blockchain’s distributed architecture can help. The value of blockchain for a fault-tolerant grid operation is elaborated with three use cases: 1) fault location, isolation, and service restoration; 2) topology identification; and 3) data configuration. These use cases demonstrate how blockchain-based architecture can facilitate bridging the vulnerabilities of the existing implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) Value-Added Product Based on the lagtraj Framework

The Atmospheric Radiation Measurement (ARM) large-scale forcing data developed based on the constrained variational analysis (VARANAL) value-added product (VAP) (Zhang and Lin 1997, Zhang et al. 2001, Xie et al. 2004, Tang et al. 2019) has been widely used for single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LESs) to understand and improve physical processes in models. Recently, the U.S. Department of Energy (DOE) ARM user facility conducted several major field campaigns using ship-based moving observational platforms. For example, the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign focused on the role of subtropical marine-boundary layer (MBL) clouds, and the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign aimed to improve understanding of the coupled climate systems in the Arctic. Observations from moving platforms are critical to provide a comprehensive characterization of coupled-system processes associated with all stages of the cloud and/or sea-ice life cycle. Traditional ARM large-scale forcing data have been developed at fixed locations. They need to be extended to include these moving platforms to address data needs for ship-based field campaigns or to support LES modeling in a Lagrangian framework. With these considerations in mind, we develop ARM-type Lagrangian large-scale forcing data sets based on the lagtraj framework (Boeing et al. 2020) with notable enhancements in generating forcings that are more suitable for ARM field campaigns. The lagtraj is a novel tool that generates forcings for LES and SCM simulation in both Lagrangian and Eulerian perspective. This technical report focuses on the major changes we performed on the lagtraj algorithm and provides an overview of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) value-added products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast-ion-driven Alfvén eigenmodes during ICRF-heated high β p plasmas on EAST

Fast-ion-driven Alfvén eigenmodes (AEs) are observed during ICRF-heated high-β p plasmas on EAST. Multiple high frequency modes are observed for shot 112670 at f TAE1 ~ 145 kHz, δB/B ~ 4 × 10 –4 and f TAE2 ~ 175 kHz, δB/B ~ 1.2 × 10 –5 measured by high-frequency Mirnov coils, at B 0 ~ 2.45 T, I p ~ 350kA, n e ramp-up from 3.5 × 10 19 to 4 × 10 19 m −3 at P ECRH ~ 2 MW, PLHW ~ 2 MW, and P ICRH ~ 2.1 MW. Here, 37 MHz ICRF minority hydrogen heating scheme, located at plasma core, creates fast ion population which drives TAEs unstable from TORIC + SFFPQL. In the experiment, it is observed that the evolution of mode frequency depends on electron density, following the theoretical TAE frequency characteristic $f_{{{\text{th}}}} = V_{{\text{a}}} /(4\pi qR) \propto \sqrt {n_{{\text{e}}} }$. As the electron density increases, the amplitude of the AE mode progressively diminishes. Higher plasma density shortens the fast-ion slowing-down time, reduces the heating efficiency of minority hydrogen, weakens the high-energy tail, and consequently decreases the fast-ion beta. Based on the diagnostic results from the microwave reflectometer and electron cyclotron emission (ECE) diagnostics, the TAE is located within the plasma core, consistent with the narrower core region of the TAE gap observed in the continuous spectrum.

Pan, C. Y. [Chinese Academy of Sciences, Hefei (Ch↗
DOE OSTI2025

Differentially Private Adaptive Noise Injection (DP-ANI) v1.0

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This software implements a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.

Peisert, Sean [Lawrence Berkeley National Laborato↗
DOE OSTI2021

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Well Stimulation Treatment in California: Evaluation of Disclosure Data, May 2015 – October 2019

In this document, disclosure data from 1,228 well stimulations occurring May 2015 to October 2019 in California are evaluated. This evaluation updates a previous study that was based on 618 well stimulations occurring May 2015 to June 2016 (Stringfellow, W.T., Camarillo, M.K., and Jordan, P. 2017, Status of Well Stimulation in California Since Implementation of SB-4 Regulations, Berkeley National Laboratory, Berkeley, CA). While the goal of the previous study was to provide a summary of information obtained since passage of California Senate Bill No. 4—Oil and Gas, the goal of the current evaluation is to provide a basis for studying chemical indicators that could potentially be used to detect aquifer contamination. We evaluate chemical indicators is a separate document using the data contained herein as well as data from monitoring wells located within oil fields. Compared with the data used by Stringfellow et al. 2017, the current evaluation is based on a more diverse data set. While the previous study contained data for only two oil fields with more than 30 well stimulations, the current data set contains four oil fields with more than 30 well stimulations, making these data more ideal for comparisons using statistical tests. More producers and a new oil field, Buena Vista Nose, are represented in the current data set. Some well stimulation practices remain relatively unchanged since the previous study. Well stimulation is mostly occurring in Kern County with the exception of a single well stimulation in Orange County. Almost all well stimulations are hydraulic fracturing. The current data set contains two acid fracturing treatments and one matrix acidizing treatment. The median number of chemicals added per well stimulation—excluding water and proppant—was previously 21; the number in the expanded data set is 20. Median water added per well stimulation was previously 89,000 gallons and is now 98,000 gallons. While this water use represents a 10% increase, water use for hydraulic fracturing in California is still lower than water use in other oil and gas fields. Chemical formulations appear to have shifted since the previous study. This shift may be related to a change in the predominant service company. We identified 26 chemicals for which the frequency of use has changed by more than 30%. The total number of chemicals used has also expanded. Previously, 178 unique chemicals were identified as being added to well stimulation fluids. The total number of unique chemicals is now 205. Analytical data available for base and recovered fluids has expanded. The number of base fluid samples has increased from 12 to 35 and the number of recovered fluid samples has increased from 1,078 to 2,166. The expanded data set is more ideal for an evaluation of indicator chemicals and comparisons across different formations. The analytical data for recovered fluid samples indicates that many ions, radioactive constituents, and organics are consistently observed, making these chemicals ideal for consideration as potential indicators of aquifer contamination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impact of gas injection location and divertor surface material on ITER fusion power operation phase divertor performance assessed with SOLPS-ITER *

Abstract The ITER divertor design and performance assessment, primarily based on the SOLPS-4.3 burning plasma database (Pitts R. et al 2019 Nucl. Mater. Energy 20 100696), assumes the use of beryllium (Be) as the divertor surface material and the injection of gas from the main chamber top. However, the current ITER baseline favors gas injection from the more toroidally symmetric sub-divertor region. This paper evaluates the implications of these assumptions for divertor performance in the ITER fusion power operation phase. The impact of the divertor surface material and the gas injection location on the main ions mirrors the hydrogen only low power phase scenario shown in Park J.-S. et al (2020 Nucl. Fusion 61 016021). However, during burning plasma operation, extrinsic impurity seeding will be required. In the case of neon (Ne), studied here, impurity retention is influenced by both the divertor surface material and the fueling location. Neon leakage increases due to more energetic reflection from tungsten than beryllium, but equivalent divertor performance can be achieved by adjusting the neon seeding rate. While the impurity seeding location does not affect the distributions of impurity or radiation, the fueling location does. Top fueling provides local ionization sources mainly in the mid-SOL under detached conditions, enhancing divergences of the flux there (source-driven flow), bringing stagnation points close to the fueling location, and equilibrating flows towards both targets. In contrast, the global flow pattern (in the absence of fluid drifts) in the case of sub-divertor fueling is biased towards the inner target. Impurity flows, driven by force balance, largely mirror those of the main ion flow, including the stagnation point. The case with top fueling enhances Ne retention and corresponding radiation in the outer divertor, effectively reducing the total and peak target heat fluxes by 20%–40%, compared to the case with divertor fueling. Meanwhile, the case with outer target fueling also achieves similar reductions by enhancing plasma-neutral interactions. These results suggest the possibility that the selection of the fueling location and throughput can be used as an actuator to control impurity divertor retention and divertor radiation asymmetry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies)

NREL's TEMPEST model combines electrical and thermal modeling for residential-scale battery systems. The model and default parameters are based off of two common commercial residential batteries. Key inputs to the model include house load, temperature of the battery location, and the solar profile. The key output of the TEMPEST model is the battery profile and internal battery temperature. If internal battery temperature exceeds limits, a derating will occur and this will be reflected in the battery profile. In addition to thermal modeling, the TEMPEST model uses realistic operating principals that are common in today's residential batteries. Mainly, the TEMPEST model incorporates "modes" of operation that constrain battery setpoint based on house load, solar output, and state of charge. The TEMPEST model can be used to analyze the effect of battery temperature, deratings, and mode setpoints on utility network loading and customer bills.

Blonsky, Michael↗
DOE OSTI2020

A large-scale, agent-based simulation of metropolitan freight movements with passenger and freight market interactions

This study summarizes the first stage in the implementation of an agent-based freight modeling system that has a global representation of agents and detailed modeling of a large-scale transportation network. The model is used to evaluate the transportation and energy impacts of goods movement across urban and national scales. The framework is implemented within POLARIS, a C++-based Planning and Operations Language for Agent-based Regional Integrated Simulation, which consists of an activity-based modeling (ABM) and dynamic traffic assignment (DTA) system that has robust features for passenger travel. This platform provides a tool to model interactions among consumers, producers, and the transportation system. The main objective of this initial implementation is to implement a freight model within POLARIS following an agent-based paradigm with behavioral and simulation methods. This paper presents the initial framework and illustrates the application of the model. Building upon earlier works, a parcel location assignment algorithm for business establishments in the population is documented, along with a method for estimating establishment production and consumption volumes. In addition to population generation, other features of the model include push-pull supply chains, multimodal path choice, choice of transportation logistics node, and dynamic traffic assignment. A module with e-commerce supply and demand was also developed to analyze the effects of e-commerce delivery on last-mile energy use and congestion.

agent-based freight↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗