Results for “maintenance engineering” — Page 73

DOE OSTI2025

Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach

There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRSDate not supplied

CLINICAL DECISION SUPPORT: PATH TO FUNCTIONAL REQUIREMENTS

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support: Path to Functional Requirements

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

Clinical decision support↗

DOE OSTI2020

Lifetime estimation of a BWR core shroud in terms of IGSCC

The continued operation of aging Boiling Water Reactors (BWRs) worldwide requires gradually increasing cost of inspection, maintenance, and repair. Intergranular Stress Corrosion Cracking (IGSCC) in sensitized austenitic stainless steel piping first became a major issue for BWRs in the 1980s, resulting in recognition of the susceptibility of reactor internals to IGSCC. Shroud cracking identified in 1993–1994 confirmed that IGSCC of internals is a significant issue for BWRs. IGSCC is a time-dependent, material degradation process, which is caused and accelerated by the presence of residual stresses, material sensitization, irradiation, cold work, elevated temperature, and corrosive environments. This paper emphasizes the importance of accounting for corrosive environments, or more exactly, electrochemical phenomena in modelling IGSCC and predicting the service life of BWR in-vessel components, and stresses the necessity of performing such modeling not just for a single state point under full power conditions, but for the whole operating history of the reactor, including startups and shutdowns. Overall, this paper demonstrates that ignoring electrochemical considerations may result in underestimating component lifetime and lead to unnecessary expenses for inspection and repair.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Static Strain Modelling, Calibration, and Measurements for High-Temperature Wireless SAW Resonator Operation

Static strain measurements are relevant for Integrated System Health Monitoring of civil structures, aircrafts, power plants and advanced manufacturing equipment, with implications for safety, process efficiency, and maintenance costs. Wireless strain sensing is highly desirable where the presence of wires poses significant safety concerns, increases maintenance, and thus overall costs, or where they are not feasible, such as in applications which contain moving parts. This paper presents wireless interrogation of surface acoustic wave resonators (SAWRs) fabricated on langasite (LGS) along Euler angles (0°, 138.5°, 32.9°) and aimed at the detection of static strain at hightemperature (HT, above 100°C). Both commercial HT strain gauges and finite element analysis (FEA Abaqus software) were used and compared for SAWR strain calibration from room temperature (RT) to HT. In addition, this work investigated a compromise between high temperature operation vs. sensitivity based on the thickness of ceramic adhesive used. For instance, a reduction in the adhesive thickness from about 150 μm to 135 μm, resulted in a 39% increase in sensitivity at RT. It was also observed that the sensitivity dropped up to 34% from RT to 200°C. Furthermore, wireless operation, calibration, and increase in sensitivity constitute important advances in the use of LGS SAW devices to monitor static stress in high-temperature harsh environments.

42 ENGINEERING↗

DOE OSTI2025

ELM 1016706669-AA B581 GDE01 Generator Summary pg 4-5 only

The table below shows the general load breakdown for 581GDE01. Although the total connected load exceeds the generator’s 150kW nameplate capacity, the normal configured load during standby power mode is less, which was measured at 81kW, or 54%, during preventive maintenance activities on 12/12/24. The generator’s available spare capacity must account for dynamically changing loads that can increase the total power demand at any time. If the two online VFD’s are operated at full speed the actual standby mode load is estimated to increase by 38kW, which would bring the total configured load during standby power mode to 119kW, or 79%, still within 581GDE01’s acceptable capacity. Per the LLNL Site 200 Generator Consolidation Final Study 2022, “Standby Emergency Generator nameplate ratings are based upon operation with varying load averaging 70% of the nameplate for 200 hours per year. Continuous loading between 70% and 100% will reduce a generator’s expected lifetime before a major overhaul. This is never a problem with Laboratory machines because of conservative application of generators and the reliability of the normal power system combines to keeps the load and hours down”. To achieve optimal performance and prolong generator life, the recommended generator loading is between 40% and 70%, optimally at 70%, which 581GDE01 appropriately falls within.

42 ENGINEERING↗

NASA NTRS1998

Performance Improvements to the Lidar Atmospheric Sensing Experiment (LASE)

Lidar Atmospheric Sensing Experiment (LASE) is the first fully-engineered, modular, tunable, autonomous Differential Absorption Lidar (DIAL) system for the remote measurement of water vapor, aerosols and clouds across the troposphere. It was designed, built and environmentally tested at LARC. LASE was designed to fly aboard a NASA/Ames ER-2 aircraft (NASA's high altitude aircraft) and operate at altitudes from 58,000 to 70,000 feet. Since its first flight on May 11, 1994, it has flown 28 total missions on board the ER-2. LASE has been validated with results showing an accuracy better than the initial requirement for vertical profiles of water vapor in the troposphere. LASE can also deploy on several other aircraft including the NASA P-3 and will fly aboard the NASA DC-8 during the Convection And Moisture EXperiment (CAMEX) in July-September 1998. The tunable laser system of LASE was designed to operate in a double-pulse mode at 5Hz, with energy outputs of up to 15OmJ per pulse in the 813 to 819nm wavelength region and with 99% of the output energy within a spectral interval of 1.06 pm. Sixteen wavelengths were selected to cover the various water vapor absorption cross sections needed for the DIAL measurement. The Ti:Sapphire laser was constructed using a frequency-doubled Nd:YAG laser as the pump source and a single mode diode laser as a injection seeder for the Ti:Al2O3 laser. We have improved the LASE instrument in several important ways. Improvements to the seed source have demonstrated that DFB laser diodes can be used as reliable seed sources on airborne DIAL instruments. The DFB diode has enabled LASE to gather more data and significantly reduced the maintenance required to insure that the system performance requirements are met. The multiwavelength sequential seeding technique is the current method of data collection for LASE. It has the advantages of providing an entire atmospheric coverage of H2O(v) from the ground to the aircraft altitude along a single ground track. The development of a flight deployable system to measure spectral purity will insure that the LASE instrument or any future flight based DIAL system can meet the performance specifications to insure data accuracy.

Edwards, W. C.↗

DOE OSTI2020

The Always/Never Safety Framework for Satellite Rendezvous and Proximity Operations and On-Orbit Servicing

Space rendezvous and proximity operations are increasing in numbers, enabling inspections, diagnostics, and maintenance of on-orbit systems. Because collision, loss of control, and unintended damage can impact the system under examination -- and at the extreme, cause system break-up and space debris -- the safety practices for rendezvous and proximity operations can have significant implications for national security. This study examines the applicability of the Always/Never surety framework, which was developed for United States nuclear weapons, as a model safety basis for unmanned space proximity operations. This unclassified framework has understandable safety approaches and principles and focuses on a system being always safenever unsafe. The authors consider that the adapting the framework might present a means for standardization across government and commerce, encouraging a consistent approach and a set of clarifying safety principles and applications for rendezvous and proximity operations. The framework also offers a consistent taxonomy, presents safety and reliability requirements organized by four environment categories, defines accident or abnormal conditions, contributes a strategy for identifying hostile and tactical environments, and enables decision-making for determining if conditions are safe for proximity space operations.

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DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗

DOE OSTI2024

Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements

Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.

42 ENGINEERING↗

DOE OSTI2023

Four-Stage Multi-Physics Simulations to Assist Temperature Sensor Design for Industrial-Scale Coal-Fired Boiler

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

42 ENGINEERING↗

DOE OSTI2025

Low-cost buffer storage for solar industrial steam applications

Motivation and importance: Many industrial process heat consumers operate throughout the day, and deep decarbonization with solar energy requires thermal energy storage. Thermal energy storage provides an additional operational benefit of firm delivery of steam – essential to reducing the risk of costly production interruptions. The group of process heat consumers that use steam for biofuels production for the California transportation fuels market is incentivized through the Low Carbon Fuel Standard (LCFS) to reduce their carbon intensity through the use of solar steam. Examples include ethanol and dimethyl ether production. The production of these and other fuels involves distillation driven by steam reboilers with steam pressures in the range 100-280 psig. These pressures can be delivered from the type of thermal energy storage that has been the focus of this project – a steam accumulator Sunvapor, Inc. (SVI) calls a “Bullet Steam Accumulator” or “BSA TM ”. Our project adds to the understanding of the design and integration of steam accumulators charged by solar thermal energy in a biofuels production facility, and similar facilities that consume process steam. The fuel application requires a demanding level of Process Safety Management not found in other steam applications. The type of steam accumulator SVI engineered and built integrates steam generation and storage in one vessel to reduce cost. Achieving a first of a kind permit to operate such a first device has also added to the understanding of the industrial application of such systems. Goals: The overall goal of the project has been to demonstrate the advantages that a “Bullet Steam Accumulator” (BSA™) can provide to solar steam integration with a manufacturing process. The industrial host has already been convinced of the potential of the available solar steam to lower than carbon intensity score. They have been interviewed for a number of publications such as that tout the advantages SVI’s system brings to their operations. Accomplishments: At the end of 2023 Sunvapor placed in service the country’s first industrial solar boiler with thermal energy storage. At the same time, this placement in service represents the first pilot commercial system to decarbonize biofuels production through the use of solar thermal energy. While the full technical effectiveness will be shown as soon as the customer’s plant is able to consume the available steam, the economic feasibility was indicated early in the project when the combination of costs and steam pricing required to meet investor expectations was determined. The project represents a reference pilot from which a full-scale commercial system may be expanded. This pilot is a necessary first step in providing the full potential benefits to the public, including decarbonization of transportation fuels. The decarbonization of the separation steps in fuels production may be the most cost-effective and nearest term solar fuel. Co-benefits include a reduction of air pollutants such as NOx and particulate matter, and job creation for the construction, operation and maintenance of industrial solar facilities.

14 SOLAR ENERGY↗

DOE OSTI2025

Damage modeling of power tower receiver tubes using the SRLIFE tool

Concentrating Solar Power (CSP) molten-salt central receivers are subject to high, transient incident flux during daily operation. The resulting creep-fatigue damage impacts the receiver’s reliability and restricts the permissible incident flux distribution for a given receiver. This paper aims to reduce CSP plants’ levelized cost of electricity by developing a methodology to predict lifetime and identifies the primary damage mechanism (creep vs fatigue) for any given fluid temperature and temperature gradient. Results are presented in the form of a damage map that serves as a valuable operation guide and design tool. Damage maps can be used to reduce maintenance costs by improving reliability and reduce receiver capital costs by better utilizing the receiver area. FEA simulation and damage modeling of tubes subject to asymmetrical flux conditions is performed in the open-source receiver design tool srlife. Parametric studies are performed over a range of inner tube temperatures and thermal gradients for A230, 316H, 740H, A282, A617, and 800H high temperature alloys. Damage maps are presented for each alloy. A parametric, FEA-based methodology is presented for comparison of fatigue-creep ratios and prediction of tube lifetime based on the critical thermal operating conditions. Fatigue is found to be negligible compared to creep for almost every case. Here, this finding suggests that fatigue effects associated with cloud events are insignificant compared to creep at these high temperature operating conditions. Additionally, lifetime predictions identify thermal conditions where small changes in operating conditions can result in large changes in predicted lifetime.

14 SOLAR ENERGY↗

DOE OSTI2024

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. Furthermore, the whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m 2 , showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

42 ENGINEERING↗

DOE OSTI2021

Indoor Occupant Counting by RF Backscattering

Building HVAC (heating, ventilation and air conditioning) consumes approximately 13% of all energy consumption in USA. Motion detectors, cameras and user programmable thermostats have been shown to be ineffective for HVAC controls to save energy, mostly due to the user concerns of comfort, reliability and privacy. A new HVAC control system based on real-time occupant counting that is fully automated, highly accurate, economically sensible and preserving privacy and aesthetics can thus bring forth a disruptive impact to this large energy sector. Our indoor occupant monitoring technology is based on the radio-frequency identification system (RFID), deployed in the room, not on the occupants. One reader with four antennas can be deployed on the ceiling or behind the ceiling panels for every thousand square feet in home, office and assisted living, with or without room partitions. The sticker-like passive tag, 10 cents each and maintenance-free, are profusely hidden on the wall or inside the furniture at arbitrary position, preserving privacy and aesthetics. The large number of tags can realize diverse observation points to accommodate arbitrary room layouts, which is impractical by other active units of camera, infrared, radar or lidar. With 20 tags, the system can reliably detect the number of occupants. For 100 tags, occupant posture and location can be known. The technology has been verified in the research labs and test buildings with very high accuracy. When the real-time occupant number can be accurately known without assuming devices on occupants or occupant motion, the building HVAC system can be automated to achieve building energy saving without sacrificing occupant comfort. According to our limited testing in a few types of building models and the simplified cost calculation, the RFID system has low overall cost in production, deployment, operation and maintenance. The signal processing algorithm based on machine learning requires very small number of training cases as most learning is transferrable for various layouts, and very low computational needs during operation, according to our testing in four different room sizes and layouts. In our preliminary estimate from HVAC saving alone, the RFID system can potentially pay for itself within 1.5 years, in addition to the other enhancement in building automation systems (BAS). Our commercialization strategy and business pitch deck focus on venturing this Cornell occupant monitoring technology into BAS and energy management markets. We have identified three broad BAS market segments of senior living, residential buildings, and office buildings. We have put together the minimum viable product characteristics for these identified segments, including analyses on total cost and competing technologies, as well as the fit and technical gaps for these market segments. We intend to bring the technology to market by licensing or partnering with existing BAS vendors. A list of potential collaborators and licensing partner candidates was assembled for different aspects of integrating our technology into a potential product that can be used with BAS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗

NASA NTRS2008

In-Flight Operation of the Dawn Ion Propulsion System - The First Nine Months

The Dawn mission, part of NASA's Discovery Program, has as its goal the scientific exploration of the two most massive main-belt asteroids, Vesta and Ceres. The Dawn spacecraft was launched from Cape Canaveral Air Force Station on September 27, 2007 on a Delta-II 7925H-9.5 (Delta-II Heavy) rocket that placed the 1218 kg spacecraft into an Earth-escape trajectory. On-board the spacecraft is an ion propulsion system (IPS) which will provide most of the delta-V needed for heliocentric transfer to Vesta, orbit capture at Vesta, transfer to Vesta science orbits, departure and escape from Vesta, heliocentric transfer to Ceres, orbit capture at Ceres, and transfer to Ceres science orbits. The Dawn ion engine design is based on the design validated on NASA's Deep Space 1 mission. However, because of the very substantial (11 km/s) delta-V requirements for this mission Dawn requires two engines to complete its mission objectives. The power processor units (PPU), digital control and interface units (DCIU) slice boards and the xenon control assembly (XCA) are also based on the DS1 design. The DCIUs and thrust gimbal assemblies (TGA) were developed at the Jet Propulsion Laboratory. The spacecraft was provided by Orbital Sciences Corporation, Sterling, Virginia, and the mission is managed by and operated from the Jet Propulsion Laboratory. Dawn partnered with Germany, Italy and Los Alamos National Laboratory for the science instruments. The mission is led by the principal investigator, Dr. Christopher Russell, from the University of California, Los Angeles. The first 80 days after launch were dedicated to the initial checkout of the spacecraft prior to the initiation of long-term thrusting for the heliocentric transfer to Vesta. The IPS hardware, consisting of three ion thrusters and TGAs, two PPUs and DCIUs, xenon feed system, and spacecraft control software, was investigated extensively. Thrust measurements, roll torque measurements, pointing capabilities, control characteristics, and thermal behavior of the spacecraft and IPS were carefully evaluated. The Dawn IPS fully met all its initial checkout performance objectives. Deterministic thrusting for cruise began on December 17, 2007. Over the subsequent approximately 330 days the IPS will be operated virtually continuously at full power thrusting (approximately 91 mN) leading to a Mars flyby in February 2009. The encounter with Mars provides a gravity assist for a plane change and is the only source of post-launch delta-V apart from the IPS. Following the Mars gravity assist IPS will be operated for approximately one year at full power and for 1.3 years at throttled power levels leading to rendezvous with Vesta in August of 2011. Following nine months of orbital operations with IPS providing the propulsion needed for orbit capture, science orbit transfer and orbit maintenance and Vesta escape, Dawn will transit to Ceres with an expected arrival date of February 2015. As of June 16, 2008 the ion thrusters on Dawn have operated for close to 3,846 hours and have delivered nearly 1 km/s of delta-V to the spacecraft. Dawn IPS operation has been almost flawless during the initial checkout and six months of cruise. This paper provides an overview of Dawn's mission objectives, mission and system design, and the results of the post-launch Dawn IPS mission operations through June 2008

Garner, Charles E.↗

DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

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