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At least 469 records · Page 26

Human Supervision of Autonomous Vehicle Fleet Operations and Associated Passenger Communications: Preprint

Advances in automated vehicle (AV) technology and expanded operations are rapidly emerging with Automated Mobility District (AMD) deployments in global cities. NLR's AMD research addresses critical elements of human supervision of AV fleet operations and associated passenger communications for vehicles in which no driver or safety attendant is present. Although sufficiently advanced AVs no longer have direct oversight by a driver, fleet management remains staffed with operations personnel at the operations command and control (OCC) facility. This paper examines the functionality of the OCC, drawing comparisons of how automated train control and automated people mover OCCs operate. Within an AMD, the OCC manages various vehicle types, sizes, and operational modes, including on-demand and fixed route service, to facilitate a 'network of networks' for transport within a metropolitan area. The OCC serves as oversight for multiple AV fleets assisting AVs via remote operation of vehicles, communication, and dispatching personnel to resolve problems. The OCC also coordinates system operation, geographically staging vehicles, and managing weather, police, and emergency events. Informed by traffic management center (TMC) strategies using highly integrated software and communications, OCCs facilitate seamless information flows. OCC personnel remotely assist passengers and oversee multi-party operation to ensure safety and security. Although social norms mitigate large-capacity unattended vehicle operations, social interaction in multi-party automated small vehicles has little precedent. This poses a new frontier for society and requires research to effectively understand and manage. Future research will monitor OCC implementations, passenger interfaces, and deployment scaling of initial AMD systems.

33 ADVANCED PROPULSION SYSTEMS↗

Operational control with set-points tuning - application to mobile robots

This paper proposes a novel method for the optimal tuning of set points for multiple-layered control system structure widely seen in robotics and other complex industrial processes composed of a number of subsystems. The terminal sliding mode control (SMC) is used as the low-level control strategy to ensure the stability of subsystems. When uncertainties exist, it can be shown that the deteriorated system performance will be improved by the outer loop with set points tuning. For this purpose, the learning of the new set point is designed to compensate for the effects caused by uncertainties during the system operation. At the same time, the system is proven to stay with the original set point when the compensation is introduced. A practical application to a holonomic mobile robot system is given to illustrate the presented method. Desired results have been obtained.

42 ENGINEERING↗

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hydroclimate-coupled framework for assessing power system resilience under summer drought and climate change

Extreme drought, exacerbated by climate change, increasingly threatens power system resilience, and a systematic assessment of such impacts is challenging due to the unpredictability of drought and their associated modeling complexity. Here, to address the challenge, this research develops a hydroclimate-coupled power system resilience assessment framework that enables systematic modeling of drought and climate change impacts on generation, transmission, and demand sectors. Applying the framework to the 2025 Eastern U.S. power grid — comprising 6,055 at-risk generators — under climate-induced summer drought scenarios (including SSP126, SSP245, SSP370, and SSP585) from 2023 to 2100, the study finds that climate-induced droughts could jeopardize the power system’s reliability to a greater extent than historical events, potentially leading to widespread load shedding. More specifically, the study reveals that under the twenty-one representative drought scenarios, the loss of load expectation (LOLE) of the grid could range from 34.77 to 91.48 days per summer. The simulations indicate that implementing resilience enhancement strategies is crucial to ensure reliable system operation, which encompasses initiatives such as demand response, upgrading open cooling systems, and transmission expansion. In all, these findings underscore the urgent need for proactive planning and investment in resilient U.S. power systems to mitigate the impacts of extreme drought events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimal Litz Wire Design for High-Power Wireless Charging System

In this study, an optimized Litz wire and polyphase coil winding design is proposed for high-power wireless charging systems operating at 85 kHz frequency. Litz wire in a wireless charging system contributes to more than half of the losses of the system, making it a critical design component. Here, the Litz wire is optimized through Finite Element Analysis (FEA) considering electrical and mechanical design aspects and the design was validated through experimental tests on different Litz wires with various strand-diameters, twisting ratio, and number of strands. The proposed optimal Litz wire maximizes the system efficiency considering the tradeoff between coil resistance and mutual inductance. The optimal Litz wire shows that Litz wire constructed with the 63µm strands significantly outperform the traditionally used Litz wire with 100µm strand diameter. Additionally, the coil with optimal Litz wire design shows 60% reduction in the copper volume for the same loss, and at the same time increase the coil-to-coil efficiency by more than 2% by increasing the mutual inductance and reducing coil current. The optimized Litz wire was implemented in a 22-kW wireless charging system achieving 94.7% efficiency.

Aydin, Emrullah [ORNL]↗

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Characterizing Output Bottlenecks of a Production Supercomputer: Analysis and Implications

This article studies the I/O write behaviors of the Titan supercomputer and its Lustre parallel file stores under production load. The results can inform the design, deployment, and configuration of file systems along with the design of I/O software in the application, operating system, and adaptive I/O libraries.We propose a statistical benchmarking methodology to measure write performance across I/O configurations, hardware settings, and system conditions. Moreover, we introduce two relative measures to quantify the write-performance behaviors of hardware components under production load. In addition to designing experiments and benchmarking on Titan, we verify the experimental results on one real application and one real application I/O kernel, XGC and HACC IO, respectively. These two are representative and widely used to address the typical I/O behaviors of applications.In summary, we find that Titan’s I/O system is variable across the machine at fine time scales. This variability has two major implications. First, stragglers lessen the benefit of coupled I/O parallelism (striping). Peak median output bandwidths are obtained with parallel writes to many independent files, with no striping or write sharing of files across clients (compute nodes). I/O parallelism is most effective when the application—or its I/O libraries—distributes the I/O load so that each target stores files for multiple clients and each client writes files on multiple targets in a balanced way with minimal contention. Second, our results suggest that the potential benefit of dynamic adaptation is limited. In particular, it is not fruitful to attempt to identify “good locations” in the machine or in the file system: component performance is driven by transient load conditions and past performance is not a useful predictor of future performance. For example, we do not observe diurnal load patterns that are predictable.

97 MATHEMATICS AND COMPUTING↗

Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation: Preprint

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

61 RADIATION PROTECTION AND DOSIMETRY↗

Substation-Level Grid Topology Optimization Using Bus Splitting

Operations of substation circuit breakers are important for maintenance needs and topology reconfiguration in power systems. Bus splitting is one type of topology change where the two bus bars at a substation can become electrically disconnected under certain actions of circuit breakers. Because these events involve detailed substation modeling, they are typically not considered in routine power system operation and control. In this paper, an improved substation-level topology optimization framework is developed by expanding traditional line switching decisions by breaker-level bus splitting, which can further reduce grid congestion and generation costs. A tight McCormick relaxation is proposed to reformulate the bilinear terms in the resultant optimization problem to linear inequality constraints. Thus, a tractable mixed-integer linear program reformulation is attained that allows for efficient solutions in real-time operations. Numerical studies on the IEEE 14-bus and 118-bus systems demonstrate the computational performance and economic benefits of the proposed topology optimization approach.

bus split↗

Ecobee Donate Your Data 1,000 homes in 2017

This dataset is a subset of the Ecobee Donate Your Data (DYD) dataset. The Ecobee DYD data comprises user-reported metadata (of home and occupant characteristics), and data collected by Ecobee thermostats (reported in 5-minute intervals). Participant data are pulled from the Ecobee servers, and then anonymized to remove any personally identifiable information. This subset selects 1,000 single family homes in four states - California, Texas, New York, and Illinois, and span the entire year of 2017. In addition to the measurements, a metadata JSON file is included to illustrate the high-level contextual information of the dataset. The dataset can be analyzed to understand how a single-family heating, ventilation, and air-conditioning (HVAC) system operates, occupant behavior, and building thermal dynamics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗

A Mixed integer linear programming‐based distributed energy management for networked microgrids considering network operational objectives and constraints

Abstract Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Using Low-Cost “Garage Band” Recording Technology for Acquiring High Resolution High-Speed Data

The Oak Ridge National Laboratory (ORNL) has developed and tested a novel system architecture for acquiring high fidelity high-speed data. The approach uses a consumer grade audio recording device that is normally associated with “garage band” recording of music. ORNL has coupled this low-cost data acquisition hardware with computing technology running open-source software. The main advantage of this approach is per-channel cost; an instrument grade data acquisition system typically costs between $800 to $2000 per channel compared to less than $50 per channel for these consumer grade components. Three systems, each featuring four channels, have been deployed for acquiring data from geophones and the electrical supply system that supports the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at ORNL. Each channel samples at 96 kHz at 24-bit resolution. The deployed systems operate continuously 24/7 and produce about 4 terabytes of data per month per system. This paper provides a technical overview of this approach, its implementation, and some preliminary results from qualification testing. This work was conducted in support of the Multi-Informatics for Nuclear Operations Scenarios (MINOS).Keywords: Data acquisition Measurement Informatics

Wetherington Jr, G↗

Key Considerations for Adoption of Technical Codes and Standards for Battery Energy Storage Systems in Thailand

Battery storage is “technology that enables power system operators and utilities to store energy for later use. A battery energy storage system (BESS) is an electrochemical device that charges (or collects energy) from the grid or a power plant and then discharges that energy at a later time to provide electricity or other grid services when needed”. As countries in Asia consider the inclusion of BESS in their power systems to meet policy objectives, renewable energy goals, increase resilience, and expand energy access, there is an opportunity to learn from the experiences of other regions and jurisdictions that have developed more advanced storage markets and deployment methodologies. This report presents global best practices of codes, standards, and interconnection procedures developed to support the safe and reliable deployment of BESS. Several relevant case studies highlight current efforts to ensure safe operation of BESS and showcase potential pathways for adoption of relevant codes and standards. Specifically, this report is intended to support the Thailand Office of Energy Regulatory Commission (OERC) and other stakeholders in their efforts to develop technical codes and standards to govern the installation and operation of BESS; it may also be utilized as a guide for other countries as interest in the deployment of BESS technologies continues to grow.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A prototype cooling blanket for mitigating occupant overheating risk in a hot indoor environment: Modeling and assessments

Conventional ways of cooling a room or an entire house for occupant thermal comfort during summer consume a significant amount of energy and are vulnerable to overheating risk during power outages that lead to loss of cooling system operations. This study investigates a low-power cooling blanket, as a Personal Cooling System (PCS), that covers the upper human body for direct cooling during a five-day heat wave in a single-family house. A modeling framework is developed for evaluating the thermal and energy performance of the cooling blanket, which builds upon the co-simulation of three models: a house energy model, a personal thermal comfort model, and a cooling blanket model. Simulation results show that under the power outage scenario, the cooling blanket can greatly reduce the occupant heat stress with a reduction of daily hours of exceedance (discomfort hours defined as TSV>2) by up to 17.2 h (a 95.3 % improvement from the baseline power outage without the blanket). The cooling blanket, equipped with an innovative electrocaloric heat pump (COP as high as 10.1) consumes 6.31 W and can be operated by a portable battery for several days. The cooling blanket consumes only 0.28 % of the electricity of a central air-conditioning system running to provide cooling for the whole house during the five-day heatwave period. The findings justify further research of electrocaloric wearable PCS as low-power effective cooling to ensure thermal survivability of occupants during extreme indoor environments.

Electrocaloric heat pump↗

Emissions mitigation technology for advanced water-lean solvent-based CO 2 capture processes

This technical final report submitted to DOE/NETL presents all the research activities performed during the entirety of DE-FE0031660 project-Emissions Mitigation Technology for Advanced Water-Lean Solvent-Based CO 2 Capture Processes which spans from October 2018 through March 2022. RTI International has been conducting studies from fundamental and operational aspects to reduce the overall amine emissions from the advanced Water-Lean Solvent (WLS) systems, specifically RTI’s Non-Aqueous Solvent (NAS). This technical final report will highlight the key findings from project which align closely to the project objectives which are: Identify the contribution of vapor loss, entrainment, and aerosols to the overall emissions of water-lean systems; Determine the significance of CO 2 capture system operating parameters to the amine emissions; Develop an emissions model based on critical operating parameters; Evaluate the effectiveness of emissions mitigation devices to reduce the amine emissions to <1 ppm under flue coal-fired flue gas; and, Determine the contribution of the ECTs to the overall CO 2 capture cost. The following are the key findings based on numerous tests using both lab-scale setups and parametric testing performed at RTI’s Bench-scale Gas Absorption System (BsGAS). During the BP1, the aerosol generation system and monitoring equipment were installed at BsGAS to produce and determine the aerosol characteristics during the NAS CO 2 capture process. The aerosol produced by this setup produced aerosols with the peak diameter and concentration of 50 micron and 1.2E10 7 cm -3 , respectively. These particle sizes and concentrations are matched to those observed in the actual coal-fired power plant flue gases and expected to be found at the absorber inlet of the CO 2 capture system. Over 1,300 hours of parametric testing have been conducted to evaluate the impact of the aerosols and operating conditions during the CO 2 capture with NAS on the overall amine emissions in the treated flue gas. At the worse condition tested, the presence of the aerosols in the flue gas could increase the overall emissions by 10X compared to the baseline emissions from NAS’s vapor pressure. CO 2 capture rate was found to be a main factor impacting the overall emissions as well as aerosol size and concentrations in the absorber off-gas. The higher CO 2 capture rate, the higher amine emissions in the treated gas. The temperature difference between the temperature bulge seen in the absorber and the water wash temperature also impacts the particle growth where the larger the temperature difference, the more amine emissions from aerosols in the treated gas. The majority of the aerosols did not grow substantially in the system, and the particle concentrations remained nearly constant between the absorber inlet and wash outlet. Only a small portion of the particles were found to grow significantly. The high efficiency demister with mesh size of 5-10 micron can be installed to remove a portion of the aerosols from the gas stream leaving the water wash. Overall, these results from parametric testing have established the emission baseline and validate our assumption on the need of emission control technologies (ECT) in order to minimize the emissions from the baseline NAS CO 2 capture process. Over 2,000 of BsGAS operating hours was used to investigate a handful of process improvements which led to a selection of the vital few changes that effectively control the amine emissions. These process improvements are lime-coated-filters for absorber gas inlet, advanced demister at the top of the absorber, a second water wash with amine recovery unit were designed, installed, and tested at BsGAS at the end of BP1. The result showed that the NAS CO 2 capture process with these additional emission control devices could lower the amine emission in the treated gas to about 1 ppm using a simulated coal-fire flue gas stream. The main contributor in lowering the amine emission came from the second water wash with amine recovery unit where the amine concentration in the scrubbing water was kept below 2 wt% through a continuous amine removal via an adsorbent bed, resulting in a low amine vapor pressure. The adsorbent bed was regenerated via a direct steam regeneration and the recover amine was returned to the absorber to minimize wastewater and makeup amine. A flue gas generation system was designed and installed during the first half of BP2 to support the emission testing using a real coal-derived flue gas. The system is capable of generating both coal- and natural gas- derived flue gases with the composition of the gaseous species highly resemble to that of the power plant flue gases. The particulates detected in the coal-derived flue gas showed the mean diameter of 1 micron. The CO 2 capture operating was then proceed using the real coal-derived flue gas where the amine emission was controlled to be about 0-3 ppm for the total run time of about 200 hours. Similar testing was conducted with natural gas-derived flue gas and the result showed a highly amine emission of 30 ppm under the total run time of 200 hours. The Principal Component Analysis (PCA) and the Partial Least Squares Projection to Latent Structures (PLS) techniques were applied to the parametric testing data to derive a multivariate statistical model. The model was validated and trained with half of the data collected, and the predictive ability of the model was evaluated using the remaining half of the data. The resulting empirical model was capable of predicting the overall emissions from the NAS process without the ECTs with ±15% accuracy (average absolute deviation, AAD) in BP1. As more emission data were obtained under the real coal-flue gas in the BP2, the model incorporated these new set of data to reflect the final process configuration, operating parameters, and amine emission. This results in the updated empirical model predicting the amine emission from the NAS CO 2 capture process with 84% goodness-of-fit (R 2 ), 85% predictability (Q 2 ), and 15% AAD. The study evaluates the use of RTI’s Non-Aqueous Solvent technology for 90% CO 2 capture from a net 650 MWe pulverized coal power plant, downstream of the flue-gas desulfurization unit. The captured CO 2 has a purity of > 95% CO 2 , and is dried, compressed to 15.3 MPa (2,215 psia), ready for sequestration. The analysis uses Case B12B from the DOE Baseline study on Bituminous Coal, Revision 4 where the Cansolv CO 2 capture plant is replaced by the RTI CO 2 Capture plant. The CO 2 capture plant has been sized to capture >90% CO 2 from flue gas derived from a net 650 MWe supercritical pulverized coal power plant. The CO 2 capture plant is equipped with emission control technologies that limits the amine emissions to < 1 ppm. Two different cases were evaluated for the technoeconomic study. The key difference between the two cases is the regenerator pressure. In Case 1, the regenerator operates at 0.195 MPa (28.3 psia), whereas in Case 2, the regenerator pressure is 0.44 MPa (64 psia) thus removing the need for the first stage of compression of the eight-stage compression train. Results from the TEA are compared against the DOE reference cases for SCPC plant with and without CO 2 Capture (Case B12A and Case B12B of the DOE Baseline study, respectively). Case 2 with CO 2 regeneration at higher pressure results in the lower cost of CO 2 capture. The total capital cost of the capture process has been estimated using 2018 dollars in Aspen Process Economic Analyzer and was estimated to be $579 MM. The capture plant operation leads to a total parasitic power loss rate of 96 MWe, resulting in a decrease in pulverized coal power plant efficiency of 7.8% points. The resulting cost of electric power increases from 64.4 mills/kWh, for no capture, to 97.5 mills/kWh, with 90% capture, an increase of 51% in the COE. The cost of capturing 90% CO 2 was estimated to be $38.2/tonne-CO 2 , and meets the DOE target of $40/t-CO 2 . Emission control technologies (ECT) investigated in this project includes a second water wash with use of activated carbon beds for removal of amine from the wash water prior to recirculation in the water wash. These ECT allow operation of the CO 2 capture plant with < 1 ppm amine emissions with the treated flue gas and contributes to $2.4/t-CO 2 captured. Amine emissions derived from thermal and oxidative degradations were investigated under this project along with the emissions derived from aerosols for the NAS system. The thermally degraded of the lean NAS showed less than 4% decreased of the original total amine content in the NAS at 150 °C while the result obtained at 120 °C showed no drop in total amine content, suggesting that thermal degradation of the NAS is minimal. These results also suggested that the thermally degraded species are not likely formed and contributed to the emissions due to the low regeneration temperature of the NAS at 90-105 °C. The oxidative degradation, on the other hand, could become problematic as some of these oxidative degraded species were observed during the NAS-5 testing at National Carbon Capture Center (NCCC) and SINTEF in our previous project. The rapid screening of selected inhibitors suggested that oxidative degradation of NAS can be suppressed using thiol containing compounds in amounts of at least 1 mol%. The detailed mechanistic degradation pathway was conceived for a specific amine used in NAS formulation during BP2. he reduction of the nitrosamines caused by the NO x present in the flue gas was also examined. The study suggested that the thermo-chemical treatment of the NAS solvent would be a more effective and economically viable compared to removing NO x at the DCC.

01 COAL, LIGNITE, AND PEAT↗

Fracture toughness evaluations for spent nuclear fuel dry storage canister welds and spent nuclear fuel clad-pellet structures

Integrity of spent nuclear fuel (SNF) interim storage canisters is very important to the safety of the back-end nuclear fuel cycle. Stress corrosion cracking (SCC) potential of interim storage canister has been considered as a high priority. Because no post-weld heat treatment was required for forming these canisters, the high tensile residual stress existed within these canister welds. This can change the fracture resistance capacity significantly as well as increase SCC potential. Due to relative thin shell thickness of a canister weldment, the spiral notch torsion test (SNTT) method was used to estimate the canister weldment fracture toughness. SNTT was developed to measure the intrinsic fracture toughness of structural materials using small specimens. The SNTT method has been applied to a wide variety of structural materials, such as low-alloy steels, stainless steel, aluminum alloy, ceramics, concrete, and composites. The SNTT system operates by applying pure torsion to cylindrical specimens with a notch line that spirals around the specimen at a 45° pitch. In order to carry out pure torsion load mode, biaxial tension/torsion tester was developed accordingly to perform SNTT protocol. Moreover, applying fracture mechanics approach to SNF system reliability investigation is warranted due to the inherent flaws and hydride structures existed in a SNF system after nuclear reactor operation. However, none of the existing fracture toughness data deal with fuel cladding specific geometry or spent fuel material conditions, such as cladding structure with the pellet-inserts and the associated pellet clad mechanical interactions induced mixed-mode damage mechanisms. Thus, the development of an intrinsic fracture mechanics approach that is suitable for SNF materials is needed. Furthermore, due to thin wall and small dimension of clad tubing structure, the SNTT method was used to estimate the clad tubing structure fracture toughness. Fracture testing were performed on the received stainless steel canister weldment, most SNTT weld samples fracture initiation sites are at heat-affected zone (HAZ) regions. The estimated fracture toughness J Q ’ for the baseline SS304 steel is at 283 kJ/m². The estimated JQ’ for the SS304/308 weld and baseline metals are 148 kJ/m 2 and 459 kJ/m 2 , respectively. Out of cell fracture testing for spent fuel structure were carried out on the surrogate rods made of Zr-4 clad and alumina inserts, the estimated fracture toughness values for baseline Zr-4 cladding with alumina-pellet inserts are: (1) For SNTT samples with a short or medium crack length, between 5.4-mm and 8-mm, the estimate J IQ upon fracture initiation for the baseline Zr-4 cladding is at 50 kJ/m 2 with 2-sigma uncertainty of 3.26 kJ/m 2 , and the associated K IQ is at 67.46 MPa$\sqrt{m}$; and (2) For SNTT samples with a long crack length, around 13-mm, the crack initialization is deviated from that of the Mode-I tensile fracture and appears to be a mixed-mode fracture of Mode I - tensile stress and Mode III - out of plane shear stress; the estimated J MQ is at 18.9 kJ/m 2 , the associated K MQ is at 41.4 MPa$\sqrt{m}$.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗