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At least 667 records · Page 37

Exploring Dynamic P-Q Capability and Abnormal Operations of Inverter-Based Resources

Nowadays, inverter-based resources are rapidly growing in electric power systems. On the other side, abnormal inverter operations are also occurring more often. Traditionally, inverter PQ capability charts are employed by electric utility companies to ensure safe and reliable operations of inverter-based resources. However, it is found in this paper that existing inverter PQ capability charts do not address the operating characteristics of an inverter correctly, which can result in irregular and unstable inverter operations. Here in this paper, a novel study is conducted to investigate inverter PQ capability charts, where constraints that are specific to an inverter are considered. It is found in this paper that the inverter PQ capability charts exhibit a unique dynamic nature under uncertain grid conditions. This dynamic nature has not been considered by the industry in managing, designing, and controlling an inverter as well as in developing international standards for inverter-based resources. Via both electromagnetic transient simulation and hardware experimental investigations, it is found that the dynamic PQ capability nature, together with the limitations of the existing control methods, are the underlying causes of many unusual inverter operations that have been reported in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Trust Model Measurements for the Energy Grid of Things

Information security is essential for the reliable operation of an Energy Grid of Things (EGoT). In addition to basic information security protocols as defined by published standards, there is a need for a monitoring function that measures the trustworthiness of the various actors participating in an EGoT. We describe in this paper the implementation and evaluation of a Distributed Trust Model that was developed specifically for monitoring communication within an EGoT. We then show how the model parameters are set using statistical measures for hypothesis testing.

Energy Grid of Things, EGoT, Smart Grid Security, ↗

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗

First Phase Consensus Roadmap for Development of Condition-Based Cable Reliability Assurance

The objective of this work was to develop a first phase consensus roadmap for condition-based qualification (CBQ) of electrical cables. With CBQ, qualification of Class 1E electrical cables moves from a time-based approach to a condition-based approach, which is anticipated to be safer in terms of reliability and conservatism, and more cost effective in the long run. However, due to barriers, the CBQ approach has not yet been adopted by U.S. nuclear power plants (NPPs). Based upon a review of current work evaluating CBQ, the limitation of available condition monitoring technology seems to be the largest barrier. The importance of condition monitoring, or more specifically selecting appropriate condition indicators, during CBQ cannot be understated. However, selecting appropriate condition indicators is challenged by techniques that are destructive and only evaluate cable degradation locally. Further, arguably, no one identified condition indicator fully establishes cable condition. Thus, additional work is necessary to evaluate potential condition indicators towards CBQ. In addition to the requirements of IEC/IEEE Std. 60780-323, ideal condition indicators should include a) both destructive and non-destructive approaches, b) both local and global measurements, c) real-time (i.e., online) monitoring that trends with degradation, d) enable correlation with qualified levels of degradation, and e) be established within a repository of condition indicators with applicable materials and/or components and their acceptance criteria. Additional work is needed in development of technology and methodology prior to adoption of CBQ, especially for extending qualified life of installed components. Education and early experience by the industry and regulators will be required for this change in approach as an alternative to re-analysis. A series of workshops that bring together stakeholders to identify and address gaps will be needed. The longstanding cooperative working group of cable researchers from the U.S. Department of Energy, the Electric Power Research Institute, and the Nuclear Regulatory Commission forms a valuable starting point for development of a consensus roadmap to condition-based qualification approach as a viable options for qualification of cable systems in U.S. light water reactors.

42 ENGINEERING↗

Integrating Atmospheric Specifications into Seismoacoustic Event Localization

This report investigates the integration of infrasound and seismic data to improve event localization accuracy, specifically focusing on a surface explosion at the Utah Training and Testing Range (UTTR). Utilizing the Seismoacoustic Bayesian Event Locator (SABEL) framework, we incorporated atmospheric specifications derived from Ground to Space (G2S) profiles to enhance celerity-range priors. Our analysis revealed that while the combination of infrasound and seismic observations significantly reduced localization uncertainty, challenges remained, particularly with returns at distances less than 200 km from the source and the influence of specific observations on location estimates. The results indicate that broader celerity distributions, such as those from Blom et al. (2020), facilitate better alignment with ground truth locations compared to narrower models. Overall, this work demonstrates the promise of seismoacoustic approaches in refining event localization and highlights the need for further exploration of celerity-range models to ensure reliable outcomes.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Estimating the Value of Worker Training: A System Reliability & LCOE Perspective

This workshop presentation briefly describes the labor standards required for large photovoltaic (PV) systems (>1MWac) to receive the full investment tax credit from the Inflation Reduction Act. The potential for labor standards to affect aspects other than upfront installation costs (such as energy generation or maintenance expenses) is analyzed using levelized cost of energy (LCOE) calculations. This considers benefits which may come from better training for workers, more productive workers, or improved installation quality, using NREL's simplified PV-specific LCOE calculator at pvlcoe.nrel.gov.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

IM3 Data Center Driven Grid Stress Dataset for the U.S. Western Interconnection

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from an open-source grid operations modeling framework - GO), for the Integrated Multisector, Multiscale Modeling (IM3) project, under varying levels of data center demand growth between 2025 and 2035 in the U.S. Western Interconnection. The scenarios and sensitivity experiments are combinations of different data center demand growth rates and energy, weather, population and economic pathways. Data center demand growth projections were sourced from the Electric Power Research Institute (EPRI). The data center demand growth projection names are: Low (3.71% annual data center demand growth) Moderate (5% annual data center demand growth) High (10% annual data center demand growth) Higher (15% annual data center demand growth) Energy, weather, population and economic pathways are informed by two Shared Socioeconomic Pathways (SSP3 and SSP5) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) following the hotter general circulation model (GCM) forcing group from a set of perturbed thermodynamics simulations. The resulting pathway names are: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The main scenarios and sensitivity experiments are detailed below. Reference scenario: The projected grid stress and reliability results for the U.S. Western Interconnection from a previous study. This scenario does not consider data center demand growth explicitly. Data center scenario: Building on the reference scenario, this scenario considers various data center growth rates and how they impact the U.S. Western Interconnection. Data center loads are modeled as flat 8760-hr profiles. This scenario does not consider new generation and transmission capacities specifically designed to meet the new data center demands. The related folder is named "flat". Delayed generator retirements sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different levels of natural gas and nuclear generator retirement delays. The resulting scenario names are: (1) postponing 100% nuclear retirements; (2) postponing 100% nuclear and 25% natural gas retirements; (3) postponing only 50% natural gas retirements; (4) postponing 100% nuclear and 50% natural gas retirements; (5) postponing 100% nuclear and 75% natural gas retirements; and (6) postponing 100% nuclear and 100% natural gas retirements. The related folder names are: no_gen_retire_0_gas, no_gen_retire_25_gas, no_gen_retire_50_gas, no_gen_retire_50_gas_only, no_gen_retire_75_gas, and no_gen_retire_100_gas. Demand response through curtailment sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different participation and compensation levels of data center demand response. The resulting scenario names are: (1) 5% demand available for curtailment with 750 $/MWh compensation; (2) 5% demand available for curtailment with 500 $/MWh compensation; (3) 5% demand available for curtailment with 250 $/MWh compensation; (4) 15% demand available for curtailment with 750 $/MWh compensation; (5) 15% demand available for curtailment with 500 $/MWh compensation; and (6) 15% demand available for curtailment with 250 $/MWh compensation. The related folder names are: dr_cost_250_drup_0_drdown_5, dr_cost_250_drup_0_drdown_15, dr_cost_500_drup_0_drdown_5, dr_cost_500_drup_0_drdown_15, dr_cost_750_drup_0_drdown_5, and dr_cost_750_drup_0_drdown_15. Combination of delayed generator retirements and demand response through curtailment sensitivity experiment: The impact of combining postponing 100% nuclear and 25% natural gas retirements with 5% demand available for curtailment with 750 $/MWh compensation is simulated. The related folder is named "dr_cost_750_drup_0_drdown_5_nuc_100_gas_25". Please refer to the README file for a detailed description of the dataset including individual files and references.

Artificial Intelligence↗

Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants: Model-Predictive Control Library Development (Report)

This document pertains to the reporting requirements of DOE contract FE-0031767. The document covers the development of a model predictive control (MPC) library for implementing MPC for a general dynamic system. The library is implemented in a standardized manner in Matlab/Simulink, where core functions on model prediction, linearization and formulation and solution of a quadratic programming (QP) optimization problem is done in the core library - independent of the specific application. The user can provide the application-specific dynamic model in continuous and discrete time, to rapidly implement and test the MPC performance in a desktop simulation. The MPC optimization objective and constraints are also easily configured via an Excel file to allow iterative refinement as needed. Finally, the MPC library enables a rapid deployment to a target environment through auto C-code generation and containerization. The MPC library works seamlessly with the model based estimation (MBE) library to obtain the overall output feedback control solution. In this program, the reduced order model (ROM) of a coal-fired power plant (CFPP) is used to implement and test the MPC solution.

01 COAL, LIGNITE, AND PEAT↗

A two-dimensional analytical unit cell model for redox flow battery evaluation and optimization

Cell performance optimization is important for improving the overall system efficiency of a redox flow battery. To gain better insights into key controlling factors of system efficiency, this work first proposed a theoretical model for a unit flow battery cell by extending a two-dimensional analytic model to a full battery cell. Such a model is then used for cell performance optimization after validating it with experimental and numerical modeling data. With the model results, the activation, equilibrium, and pump energy losses are identified as the dominant sources of battery energy losses. Further, a guideline for reducing these sources is also proposed. Following the guideline, the mass transport coefficient is shown as a key control factor of the equilibrium energy loss and Coulombic efficiency (CE). Approaches are then proposed to improve CE and the overall system efficiency. The mass transport is also revealed as the mechanism of pump rate optimization where an optimal pump rate significantly reduces the equilibrium energy loss. With both low equilibrium and pump energy losses, an optimal electrode porosity or specific area design can further improve a battery's system efficiency based on an optimal porosity predicted by the present model. The model also demonstrates distinct behaviors and overestimation in the system efficiency when reduced to a zero-dimensional model with neglected mass transport resistance. With the new model, the guideline, and new insights, this work provides a reliable and efficient tool for the evaluation and optimization of redox flow battery design in practical applications.

25 ENERGY STORAGE↗

Valuing wind as a distributed energy resource: A literature review

As more distributed energy resources are deployed on electric grid systems across the world, it is important to identify, characterize, and quantify the value elements of different types of distributed energy resources so that policymakers, developers, and utilities can make informed energy deployment decisions. This paper focuses on the value of wind energy as a distributed energy resource (i.e., “distributed wind”). Because of a lack of distributed wind-specific valuation studies, in this review we document the current state of distributed energy resource valuation, analyze a wide array of distributed energy resource valuation metastudies, and identify several value elements for which we recommend developing more robust and standardized calculation methodologies for their potential inclusion in distributed wind valuation. These value elements are ancillary services and locational, resilience, reliability, and resource diversity benefits. Furthermore, this work lays the foundation for a future comprehensive framework for distributed wind valuation studies.

17 WIND ENERGY↗

Electrolyte and Cutoff Potential Effects on Cycle Life of Li4Ti5O12/LiNi0.9Mn0.1O2 Batteries for Behind-the-Meter Storage Applications

Behind-the-Meter Storage (BTMS) is a stationary battery energy storage system that is connected to the electrical distribution system on the customer's side of the utility's service meter. BTMS systems are used to store electrical energy from the grid as well as inconstant, renewable energy, such as local solar and wind generation. A successful BTMS system will allow the customer to pair their energy generation and storage to optimize electrical consumption from the grid, improving reliability and minimizing cost. For BTMS applications, batteries must be designed and optimized with different set of criteria from other leading segments of the Li-ion battery market, like transportation, due the system being stationary and proximal to the residential or commercial building it's benefitting. BTMS applications prioritize safety, cost (low/no-critical materials), reliability (20-year calendar life), and durability (10,000 cycle life), while having the ability to (minimally) compromise energy density and rate capability. Lithium titanate (Li4Ti5O12-, LTO) is a promising anode candidate for BTMS applications due to its high safety and capacity retention, while maintaining a reasonable 160 mAhg-1 reversable capacity and composition of relatively abundant materials. (1) Specifically, LTO has a high working voltage which helps to prevent Li dendrite formation, improving safety. Furthermore, LTO also has negligible lithiation-based volume change, leading to less mechanical pulverization, or loss of active material, upon cycling. For the cathode, materials with little or no Co are of high interest due to the high cost and low abundance of Co. LiMn2O4 (LMO) has been paired with LTO for BTMS applications in the past due to its safety, low cost (abundancy), and reasonably high operating voltage. (2-4) However, the low capacity of LMO limits energy density and specific energy. While not the highest priority for BTMS applications, increasing energy density will enable deployment in space constrained BTMS applications and decrease total cost. LiNi0.9Mn0.1O2 (LN-MO) is a recently developed material with promise due to its high operating voltage and relatively low price. (5) However, Ni-rich layered oxides, including LNMO, tend to struggle with capacity retention during high-voltage cycling due to mechanical pulverization, irreversible phase transitions, and unstable solid-electrolyte interphase. The study presented here focuses on building an understanding of how electrolyte solvent and varied cutoff potentials will impact the cycle life of LTO/LN-MO cells. Specifically, a comparison is provided between ethylene carbonate (EC), ethyl methyl carbonate (EMC), fluoroethylene carbonate (FEC), and Gen2 electrolyte solvents with 1M Lithium hexafluorophosphate (LiPF6) salt, cycling to two upper termination potentials, 2.6V and 2.7V. Electrochemical testing and diagnostics (e.g., differential capacity analysis, area specific impedance, constant voltage hold, and rate capability) and post-mortem characterization will be used to understand the aging behavior and failure mechanisms of the 8 cell combinations (four electrolytes and two voltage cutoffs). Cells with FEC electrolyte showed a lower initial capacity compared to cells with Gen2, EMC, and EC cycling at both voltages; however, the cells with FEC showed consistent trends in capacity retention with 2.6V and 2.7V termination potentials, while the cells with the other electrolytes showed much higher rates of capacity loss when cycling to the higher voltage. These results indicate that FEC may play a role in improving durability of high-voltage, Ni-rich electrode systems for use in high-cycle applications, such as BTMS.

electrolyte↗

Assessment of BPM options for the EIC Beam Accumulator Ring

The electron injection system for the Electron-Ion Collider (EIC) at BNL is designed to provide a beam of polarized electrons, which is crucial for studying the structure of protons and atomic nuclei. The Beam Accumulator Ring (BAR) is a part of the injection chain between the 750 MeV linear accelerator and the Rapid Cycling Synchrotron (RCS), which accelerates the beam up to full energy (5–18 GeV). The functional role of the BAR is to accumulate the charge injected from the linear accelerator in order to achieve the high intensity of the polarized electron beam required by the specifications for injection into the RCS. This objective will be realized through the sequential injection of bunches with a charge of 1.1 nC at a repetition rate of 30 Hz. Once the charge of a single bunch reaches 28 nC, the beam will be extracted from the BAR and injected into the RCS. The beam instrumentation needs to provide reliable measurements with the required accuracy over the dynamic range from 0.1 nC (one tenth of a typical injected bunch charge) to 32 nC – the maximum expected accumulated current. There are 10 Beam Position Monitors (BPMs) in the ring, 1 extra button-electrode assembly for the RF system, and 8 BPMs in the beam transport lines. The BPM locations are shown in Fig. 1, marked by blue rectangles. The ring BPMs will be capable of both average orbit and turn-by-turn measurement modes. Accurate measurement of the beam position with a large horizontal offset requires polynomial correction of the BPM nonlinearity.

43 PARTICLE ACCELERATORS↗

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

BETO 2021 Peer Review - FCIC Task 5 - Preprocessing

In the study of feedstock variability, this presentation specifically looks at preprocessing processes that will created well-defined and homogenous feedstock from variable biomass resources, in the quest to produce homogeneous intermediates that will be converted into market-ready products. The objective of this stage of the project is to develop science-based design and operation principles informed by TEA/LCA that result in predictable, reliable, and scalable performance of preprocessing unit operations (comminution, fractionation, deconstruction, and real-time imaging).

biomass↗

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

New sets of CMS underlying-event parameters ("tunes") are presented for the PYTHIA8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell-Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from PYTHIA8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Application of an Erosion Wear Model to Predict Wear of Hammer Milling Components

A workable analytical erosion model that relates critical hammermill process parameters (hammermill geometry and rotational speed) to critical material attributes of inorganic mineral species in feedstock (density, size, and aspect ratio) and substrate (hardness, toughness, and fatigue ductility) was applied to model wear (more specifically, change in the shape of hammers) in hammer milling operations. Results of the model were compared to experimental observations of the shape of hammers used in the INL Stage 1 Vermeer hammermill. Results showed good agreement between predicted and measured shape of the hammer, and that a quality-by-design (QbD) approach can be developed to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches.

36 MATERIALS SCIENCE↗

Lower-length-scale modeling of chemical additions, corrosion and fission products in select MSR base salts

Molten salts play a crucial role in advancing Generation IV nuclear energy technology, with chloride-based salts like NaCl-UCl 3 garnering significant attention due to their distinctive properties. However, the corrosive nature of molten salts causes the dissolution of chromium (Cr), leading to the formation of CrCl 2 and CrCl 3 species in molten chlorides. Moreover, the radioactive decay of nuclear fuel gives rise to fission products, including Cs, Sr, and I chlorides. The first part of this report presents a comprehensive study utilizing ab initio molecular dynamics (AIMD) simulations to investigate the properties of eutectic NaCl-UCl 3 molten salt in the presence of corrosion products (CrCl 2 and CrCl 3 ) and fission product (CsCl). The study focuses on essential structural and thermophysical properties such as density, mixing energy, coordination numbers (CN), and Radial Distribution Functions (RDF) of the salts with varying compositions of corrosion products and fission product, covering a range from 0% to 13.5%. The results offer valuable insights into the behavior of corrosion and fission products in uranium-based molten salts, providing essential data that can be used as input to the MTDB-TC and MSTDB-TP property databases being developed by the NEAMS program. Due to their favorable characteristics such as low melting points, high boiling points, and low costs, MgCl 2 +NaCl+KCl (MNK) eutectic salts have recently attracted attention as high-temperature heat transfer fluids. The incorporation of LiCl into MNK salts can further reduce their melting points and increase their specific heat capacities, which is de- sirable for high-temperature heat transfer applications. The second part of this report presents the development and validation of a new shell-model potential for the MgCl 2 +NaCl+KCl+LiCl system, which captures the polarization of Cl anions. The extensive comparison with experimental data and AIMD simulations demonstrates the accuracy and reliability of the potential. Furthermore, using this potential, we elucidate the intricate network structure in MNK eutectic salts that is formed through polyhedron sharing. This research contributes to a fundamental understanding of the atomic structures and thermophysical properties of multi-component molten salts, which is crucial for future development of heat transfer fluids for applications in molten salt reactors.

36 MATERIALS SCIENCE↗