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DOE OSTI2025

CSP Plant Optimization Study for the California Power Market (“CalCSP”) (Final Technical Report)

Concentrating Solar Power (CSP) with thermal energy storage offers a unique and strategic opportunity to support California’s clean energy transition. Unlike photovoltaic (PV) systems, CSP with thermal storage can generate electricity after sunset and during periods of high demand, making it a valuable complement to intermittent renewable resources. CSP also provides synchronous, inertia-contributing generation, long-duration storage, and flexible dispatch—capabilities increasingly important as thermal plants retire. This report summarizes the findings of the CSP Plant Optimization Study for the California Power Market or “CalCSP study,” which evaluated the technical, economic, environmental, and policy factors that influence the deployment of CSP technologies in California. The CalCSP study was conducted to assess how CSP can contribute to California’s long-term decarbonization goals while enhancing grid reliability, supporting local economic development, and making efficient use of land and transmission resources. It draws on detailed modeling of CSP performance and costs, site suitability analysis, policy reviews, and stakeholder engagement across utilities, regulators, developers, and community organizations. The analysis focuses on mature molten-salt tower technology and incorporates lessons learned from the global CSP fleet, distinguishing today’s CSP from earlier first-of-a-kind projects in the U.S. The findings support a more prominent role for CSP in California’s evolving clean energy landscape. With strategic planning, targeted policy support, and continued cost improvements, CSP can complement PV and batteries to deliver reliable, around-the-clock clean electricity—especially in areas with high solar resource and constrained grid capacity.

14 SOLAR ENERGY↗

NASA NTRS2019

LEAVES: Lofted Environmental and Atmospheric Venus Sensors

LEAVES (Lofted Environmental Atmospheric Venus Sensors) is a design exercise with the goal of dramatically decreasing the cost of obtaining prioritized chemical and physical data in planetary atmospheres. Through the application of a swarm approach this concept parallelizes atmospheric exploration, with geographic coverage far exceeding what is possible with conventional monolithic platforms or sondes. Each unit in the swarm is exceptionally compact, with a powered payload mass of only a few tens of grams and a high-drag, semi-rigid structure that acts to slow each probe as it descends through the atmosphere. This structural design can collapse into a planar form to allow for efficient stowage prior to arrival at the target body. With a total per-unit mass of only 120 g, a fleet of 100 (or more) units can be very reasonably accommodated on a carrier spacecraft.Science operations, which begin when the LEAVES probes reach an altitude of 100 km, are targeted for the cloud-bearing region of Venus' atmosphere. During the roughly 9 hour, terminal velocity descent through the atmosphere, LEAVES collects data of the state and composition of the atmosphere in parallel across multiple units. These data would represent an unprecedented constraint on the distribution and concentration of targeted chemical species, and the detection of local and regional variations in both chemistry and physical properties.A novel and compelling result of this exercise was that the same optimization that produced a structure with an exceptionally low areal mass density (0.126 kg/m2) also resulted in a probe that can be deployed directly from an aerobraking orbit (~140 km at 5 km/s) without the need for aeroshell protection. This translates to a tremendous mass savings and gives LEAVES the flexibility to be carried as a secondary payload aboard either a descending surface probe or an orbital radar mapper. Because such missions are under active development or have already been proposed (but not flown), we infer that LEAVES is well positioned as a technology

Swarm↗

DOE OSTI2022

Cognitive Aging as a Human Factor: Effects of Age on Human Performance

Nuclear power plant (NPP) control room operators must make ongoing computations and decisions that maximize production and ensure safety, which places a high cognitive burden on the operators. How cognitions such as attention, visuospatial ability, and working memory interact with socio-technical systems to achieve optimal operations is well studied. However, there is an absence of research that examines how cognitive functioning within the NPP control room environment is moderated by developmental aging processes. This is of critical importance because different types of cognitive actions are known to develop and peak at different times across the adult life span, and it is becoming increasingly clear that there is no age at which all cognitive faculties operate at maximum capacity. Thus, given that NPPs are experiencing an aging workforce, it is vital to identify how mission critical cognitions change with age. This paper reviews implications of aging on reactor operators in the current and new fleet. We highlight lessons that can be learned from state-of-the-art human factors research that considers aging, lessons from the large cognitive aging literature, and lessons from aging workers in other industries that use sophisticated socio-technical systems, such as aviation. We also consider the important subject of aging effects versus expertise and present preliminary data that support the premise that age of operator is linked to effective and efficient operations but that this relationship may be moderated by level of operations expertise. In conclusion, we apply these lessons to future considerations for aging research in current nuclear operations and with the advent of advanced modernized control rooms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗

DOE OSTI2026

Analysis of Truck Platooning on Rural Highways

An analysis by the National Laboratory of the Rockies on truck platooning technology used on Ohio highways found that truck platooning operated for 40% of driving distances, showcasing its potential to enhance freight efficiency and safety. While energy savings were evident, further optimization of gap distances and operational consistency is needed to fully evaluate fuel savings benefits across diverse driving conditions. While more study is needed, advanced connected and automated vehicle technologies such as platooning demonstrate significant promise for transforming commercial vehicle efficiency and operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Packetized Energy Management: Coordinating Transmission and Distribution (Final Report)

The project Packetized Energy Management (PEM): Coordinating Transmission and Distribution was part of the ARPA-E NODES program from 2015 to 2023. The high-level goal of the project was to develop and demonstrate novel, scalable, and impactful technologies related to the coordination of networked distributed energy resources (DERs). By demonstrating responsive means by which fleets of DERs could be coordinated to enhance grid operation and reliability, the U.S. could accelerate renewable integration and electrification efforts and meet decarbonization goals.

25 ENERGY STORAGE↗

NASA NTRS2014

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..

Horio, Brant M.↗

NASA NTRS2003

Data-Mining Toolset Developed for Determining Turbine Engine Part Life Consumption

The current practice in aerospace turbine engine maintenance is to remove components defined as life-limited parts after a fixed time, on the basis of a predetermined number of flight cycles. Under this schedule-based maintenance practice, the worst-case usage scenario is used to determine the usable life of the component. As shown, this practice often requires removing a part before its useful life is fully consumed, thus leading to higher maintenance cost. To address this issue, the NASA Glenn Research Center, in a collaborative effort with Pratt & Whitney, has developed a generic modular toolset that uses data-mining technology to parameterize life usage models for maintenance purposes. The toolset enables a "condition-based" maintenance approach, where parts are removed on the basis of the cumulative history of the severity of operation they have experienced. The toolset uses data-mining technology to tune life-consumption models on the basis of operating and maintenance histories. The flight operating conditions, represented by measured variables within the engine, are correlated with repair records for the engines, generating a relationship between the operating condition of the part and its service life. As shown, with the condition-based maintenance approach, the lifelimited part is in service until its usable life is fully consumed. This approach will lower maintenance costs while maintaining the safety of the propulsion system. The toolset is a modular program that is easily customizable by users. First, appropriate parametric damage accumulation models, which will be functions of engine variables, must be defined. The tool then optimizes the models to match the historical data by computing an effective-cycle metric that reduces the unexplained variability in component life due to each damage mode by accounting for the variability in operational severity. The damage increment due to operating conditions experienced during each flight is used to compute the effective cycles and ultimately the replacement time. Utilities to handle data problems, such as gaps in the flight data records, are included in the toolset. The tool was demonstrated using the first stage, high-pressure turbine blade of the PW4077 engine (Pratt & Whitney, East Hartford, CT). The damage modes considered were thermomechanical fatigue and oxidation/erosion. Each PW4077 engine contains 82 first-stage, high-pressure turbine blades, and data from a fleet of engines were used to tune the life-consumption models. The models took into account not only measured variables within the engine, but also unmeasured variables such as engine health parameters that are affected by degradation of the engine due to aging. The tool proved effective at predicting the average number of blades scrapped over time due to each damage mode, per engine, given the operating history of the engine. The customizable tools are available to interested parties within the aerospace community.

Litt, Jonathan S.↗

NASA NTRS1998

Collaborative Arrival Planning: Data Sharing and User Preference Tools

Air traffic growth and air carrier economic pressures have motivated efforts to increase the flexibility of the air traffic management process and change the relationship between the air traffic control service provider and the system user. One of the most visible of these efforts is the U.S. government/industry "free flight" initiative, in which the service provider concentrates on safety and cross-airline fairness, and the user on their business objectives and operating preferences, including selecting their own path and speed in real-time. In the terminal arrival phase of flight, severe restrictions and rigid control are currently placed on system users, typically without regard for individual user operational preferences. Airborne delays applied to arriving aircraft into capacity constrained airports are imposed on a first-come, first-serve basis, and thus do not allow the system user to plan for or prioritize late arrivals, or to economically optimize their arrival sequence. A central tenant of the free-flight operating paradigm is collaboration between service providers and users in reaching air traffic management decisions. Such collaboration would be particularly beneficial to an airline's "hub" operation, where off-schedule arrival aircraft are a consistent problem, as they cause serious air-port ramp difficulties, rippling airline scheduling effects, and result in large economic inefficiencies. Greater collaboration can also lead to increased airport capacity and decrease the severity of over-capacity rush periods. In the NASA Collaborative Arrival Planning (CAP) project, both independent exchange of real-time data between the service provider and system user and collaborative decision support tools are addressed. Data exchange of real-time arrival scheduling, airspace management, and air carrier fleet data between the FAA service provider and an air carrier is being conducted and evaluated. Collaborative arrival decision support tools to allow intra-airline arrival preferences are being developed and simulated. The CAP project is part of and leveraged from the NASA/FAA Center TRACON Automation System (CTAS), a fielded set of decision support tools that provide computer generated advisories for both enroute and terminal area controllers to manage and control arrival traffic more efficiently. In this paper, the NASA Collaborative Arrival Planning project is outlined and recent results detailed, including the real-time use of CTAS arrival scheduling data by a major air carrier and simulations of tactical and strategic user preference decision support tools.

Zelenka, Richard E.↗

DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Demonstrating Load-Shaping Capabilities of Cost Minimizing Heat Pump Water Heater Controls with Varying Price Profiles

Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗

DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2012

Overview of US AID-World Bank-NASA Collaboration to Address Water Management Issues in the MENA Region

The World Bank, USAID and NASA have recently established a joint project to study multiple issues pertaining to water related applications in the Middle East North Africa (MENA) region. The main concentration of the project is on utilization of remote sensing data and hydrological models to address crop irrigation and mapping, flood mapping and forecasting, evapotranspiration and drought problems prevalent in this large geographic area. Additional emphases are placed on understanding the climate impact on these areas as well. Per IPCC 2007 report, by the end of this century MENA region is projected to experience an increase of 3 C to 5 C rise in mean temperatures and a 20% decline in precipitation. This poses a serious problem for this geographic zone especially when majority of the hydrological consumption is for the agriculture sector and the remaining amount is for domestic consumption. The remote sensing data from space is one of the best ways to study such complex issues and further feed into the decision support systems. NASA's fleet of Earth Observing satellites offer a great vantage point from space to look at the globe and provide vital signs necessary to maintain healthy and sustainable ecosystem. These observations generate multiple products such as soil moisture, global precipitation, aerosols, cloud cover, normalized difference vegetation index, land cover/use, ocean altimetry, ocean salinity, sea surface winds, sea surface temperature, ozone and atmospheric gases, ice and snow measurements, and many more. All of the data products, models and research results are distributed-via the Internet freely through out the world. This project will utilize several NASA models such as global Land Data Assimilation System (LDAS) to generate hydrological states and fluxes in near real time. These LDAS products will then be further compared with other NASA satellite observations (MODIS, VIIRS, TRMM, etc.) and other discrete models to compare and optimize evapotranspiration, soil moisture and crop irrigation, droUght assessment and water balance. The floods being a critical disaster in many of the MENA countries, NASA's global flood mapping and modeling framework (CREST) will be customized for country specific needs and delivered to the remote sensing organizations for their future use. Finally, capacity building is a critical part of this project and NASA will assist in this effort as well.

Habib, Shahid↗

DOE OSTI2021

Performance Evaluation of Comparative Vacuum Monitoring and Piezoelectric Sensors for Structural Health Monitoring of Rotorcraft Components

The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

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DOE OSTI2024

Utility and Industry Perceptions of Control Room Modernization Over the Last 10 Years

Within the current United States (U.S.) nuclear power fleet, main control room modernization (CRM) is an important step towards cost savings. In recent decades, plants have been engaged in upgrades to varying degrees. This process requires a nuanced, balanced, and timely approach that ensures continued safety and long-term sustainability. In 2012 a survey was issued to individuals from the nuclear industry to learn their perspectives on a range of CRM issues. The survey targeted the benefits and challenges for utilities undertaking this process, including the main drivers and barriers to technology upgrades, regulatory compliance, and the effects these factors have on concepts of operations, strategic approaches, and staffing. In 2022, the survey was issued again to understand whether CRM perceptions had changed in the last 10 years. Our findings identify changes in industry thinking from a decade ago. Here we reveal perspective shifts that represent increased optimism and, in some instances, increased doubt regarding the opportunities and challenges inherent in CRM and implementation. We also report nuanced differences in CRM perspectives between utility and surrounding nuclear industry respondents.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2010

Establishing a Robotic, LEO-to-GEO Satellite Servicing Infrastructure as an Economic Foundation for Exploration

The strategy for accomplishing civilian exploration goals and objectives is in the process of a fundamental shift towards a potential new approach called Flexible Path. This paper suggests that a government-industry or public-private partnership in the commercial development of low Earth orbit to geostationary orbit (LEO-to-GEO (LTG)) space, following or in parallel with the commercialization of Earth-to-LEO and International Space Station (ISS) operations, could serve as a necessary, logical step that can be incorporated into the flexible path approach. A LTG satellite-servicing infrastructure and architecture concept is discussed within this new strategic context. The concept consists of a space harbor that serves as a transport facility for a fleet of specialized, fully- or semi-autonomous robotic servicing spacecraft. The baseline, conceptual system architecture is composed of a space harbor equipped with specialized servicer spacecraft; a satellite command, communication, and control system; a parts station; a fuel station or depot; and a fuel/parts replenishment transport. The commercial servicer fleet would consist of several types of spacecraft, each designed with specialized robotic manipulation subsystems to provide services such as refueling, upgrade, repair, inspection, relocation, and removal. The space harbor is conceptualized as an ISS-type, octagonal truss structure equipped with radiation tolerant subsystems. This space harbor would be primarily capable of serving as an operational platform for various commercially owned and operated servicer spacecraft positioned and docked symmetrically on four of the eight sides. Several aspects of this concept are discussed, such as: system-level feasibility in terms of ISS-truss-type infrastructure and subsystems emplacement and maintenance between LEO and GEO; infrastructure components assembly in LEO, derived from ISS assembly experience, and transfer to various higher orbital locations; the evolving Earth-to-orbit (ETO) capability to deliver humans and cargo to LEO for assembly purposes; system architectural definition, optimal orbital parameters, mass estimations, delta velocity ( V) estimations, power and propulsion options, and assessments of various critical technologies. Large-scale, robotic, LTG satellite servicing is considered as an essential economic pre-condition and next parallel or sequential step on the road toward exploration beyond LEO. Such a step might produce the necessary pre-requisite economic value that can be used by future decision makers to justify further investment in exploration beyond LEO.

Horsham, Gary A. P.↗

DOE OSTI2020

Leak detection in a subcritical boiler

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

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