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DOE OSTI2021

Chain and Segmental Dynamics in Polymer–Nanoparticle Composites with High Nanoparticle Loading

Recent experimental and simulation studies have shown that polymer–nanoparticle (NP) composites (PNCs) with ultra-high NP loading (>50%) exhibit remarkable mechanical properties and dramatic increases in polymer glass-transition temperature, viscosity, and thermal stability compared to the bulk polymer. These deviations in macroscopic properties suggest a slowdown in both segmental and chain-scale polymer dynamics due to confinement. In this work, we examine the polymer conformations and dynamics in these PNCs using molecular dynamics simulations of both unentangled and entangled coarse-grained polymers in random-close-packed NP packings with varying polymer fill fractions. We find that the changes in the polymer dynamics depend on the number of NPs in contact with a polymer segment. Using the number of polymer–NP contacts and different polymer chain conformations as criteria for categorization, we further examine the polymer dynamics at multiple length scales to show the high level of dynamic heterogeneity in PNCs with ultra-high NP loading.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2002

Designing Crane Controls with Applied Mechanical and Electrical Safety Features

The use of overhead traveling bridge cranes in many varied applications is common practice. In particular, the use of cranes in the nuclear, military, commercial, aerospace, and other industries can involve safety critical situations. Considerations for Human Injury or Casualty, Loss of Assets, Endangering the Environment, or Economic Reduction must be addressed. Traditionally, in order to achieve additional safety in these applications, mechanical systems have been augmented with a variety of devices. These devices assure that a mechanical component failure shall reduce the risk of a catastrophic loss of the correct and/or safe load carrying capability. ASME NOG-1-1998, (Rules for Construction of Overhead and Gantry Cranes, Top Running Bridge, and Multiple Girder), provides design standards for cranes in safety critical areas. Over and above the minimum safety requirements of todays design standards, users struggle with obtaining a higher degree of reliability through more precise functional specifications while attempting to provide "smart" safety systems. Electrical control systems also may be equipped with protective devices similar to the mechanical design features. Demands for improvement of the cranes "control system" is often recognized, but difficult to quantify for this traditionally "mechanically" oriented market. Finite details for each operation must be examined and understood. As an example, load drift (or small motions) at close tolerances can be unacceptable (and considered critical). To meet these high functional demands encoders and other devices are independently added to control systems to provide motion and velocity feedback to the control drive. This paper will examine the implementation of Programmable Electronic Systems (PES). PES is a term this paper will use to describe any control system utilizing any programmable electronic device such as Programmable Logic Controllers (PLC), or an Adjustable Frequency Drive (AID) 'smart' programmable motion controller. Therefore the use of the term Programmable Electronic Systems (PES) is an encompassing description for a large spectrum of programmable electronic control devices.

Lytle, Bradford P.↗

DOE OSTI2015

VersiCharge-SG - Smart Grid Capable Electric Vehicle Supply Equipment (EVSE) for Residential Applications

In his 2011 State of the Union address, President Obama called for one million electric vehicles on the road by 2015 [1]. With large-scale Electric Vehicle (EV) or Plug-in Electric Vehicle (PEV or EV for short) or Plug-in Hybrid Electric Vehicle (PHEV) penetration into the US market, there will be drastic reduction in fossil fuel consumption, thus significantly reducing our dependency on foreign oil [2-6]. There will also be significant reduction on Green House Gas (GHG) emissions and smog in the major US cities [3, 7, 8]. Similar studies have also been done other industrial counties [9]. For the fuel cost, with the home electricity rate around $0.13 per kWh, it would cost about $0.05 per mile for DC operation and $0.03 cents per mile for AC operation. But, assuming 25 miles per gallon for a typical vehicle and $4 per gallon, fossil fuel will cost $0.16 per mile [10]. The overall lifecycle cost of PEVs will be several folds lower than the existing fossil fueled vehicles. Despite the above advantages of the EVs, the current cost of EVSE is not affordable for the average consumer. Presently, the cost of installing state-of-the-art residential EVSE ranges from $1500 to $2500 [11]. Low priced EVSE technology, which is easy to install, and affordable to operate and maintain by an average consumer, is essential for the large-scale market penetration of EVs. In addition, the long-term success of this technology is contingent on the PEVs having minimal excessive load and shift impact on the grid, especially at peak times. In a report [2] published by the Pacific Northwest National Laboratory (PNNL), the exiting electric power generation infrastructure, if used at its full capacity 24 hours a day, would support up to 84% of the nation’s cars, pickup trucks and SUVs for an average daily drive of 33 miles. This mileage estimate is certainly much below what an average driver would drive his/her vehicle per day. Another report [3] by the National Renewable Energy Laboratory (NREL) shows that an increased PEV penetration would significantly increase pressure on the peak generation, if no controlled charging strategy was put in place. Investigations from Oak Ridge National Laboratory (ORNL) show that in many regions, additional power generation facilities must be put in place and operate in evening times to recharge the EVs [12]. By all accounts, large PEV penetration will bring to the power grid enormous challenges due to the excessive and stochastic demand, and can entirely change the peak time distribution and behavior, perhaps, into a bi-modal distribution capable of exhausting primary, secondary and even reserves (spinning or non-spinning). To minimize the infrastructure upgrade costs and risks to the grid, and to ensure that power quality and reliability remain within the set standards, the demand for EV plug-ins must then be controlled and coordinated locally and at regional levels. Novel control techniques must be devised to allow for close collaboration between neighboring plug-in requestors, between neighboring communities, and between these and more central power authorities. The concept of electric drive vehicle is not new. The development of electric vehicle has been around since 19th century [13]. But due to a number of reasons and practical limitations at the time, including lower cost of gasoline compared to electricity, excessive refueling times, and abundance of gasoline, the automobile industry embraced gasoline-powered vehicles worldwide [13]. With the global warming, ever reducing reservoirs of fossil oil around the world and increasing political pressure to reduce the national dependency on foreign oil, the last decade of the 20th century witnessed major technological breakthroughs in Alternative Fueled Vehicle (AFV) technologies, including electric vehicles. With GHG emissions and carbon footprint in the minds of many more consumers and politicians, the first decade of the 21stCentury witnessed more breakthroughs with some real life experimentation and sporadic deployment of these technologies [14]. By many accounts, the second decade of the 21st Century is expected to be the time when mass volume production and popular usage of these AFV technologies, especially EV, will materialize. The current DOE request for proposals recognizes the need for major technological changes to ensure that the above national goal is realizable. Two major challenges have been identified: (1) major reduction in the cost of ownership of EVSEs, and (2) managing additional EV loads in the power grid while maintaining power quality, reliability, and affordability. We note that the two challenges are closely linked – A holistic approach to true lifecycle cost of EVSE ownership will certainly include any taxes and surcharges that can be put in place for major potential investments in the grid, and higher electricity charges in case of more frequent and longer peak periods. From a societal perspective, this cost could also include the lost GDP (computed on a local basis) and revenue for businesses at local and regional levels when the grid is no longer capable of meeting the demand and unexpected outages occur. A typical end-point electrical distribution system delivers power to a residential EVSE from the neighborhood distribution pole, as shown in Fig.1. This pole has a transformer (neighboring step-down transformer) that steps down the utility medium voltage to dual 120VAC single phase (also called 240VAC split phase). This voltage is fed through a meter into the residential load control center. The load control center consists of branch circuit breakers and distributes the power supply within various areas of the residential unit. One of the branch circuits from the load control center feeds EV charging station for the unit. An electric vehicle charger is plugged into the socket of the EV charging station and other end of this charger is connected to the vehicle during charging. Figure 1 illustrates a typical configuration of the power grid. The left side of the figure shows the power grid from the power generation to the neighboring step-down transformer, while the right side of the figure shows multiple EVs with the respective charging stations. The typical step-down transformer has an upper limit representing the maximum load that can be requested from these neighboring houses altogether (typically 24 kW). In case the total load increases beyond the supported limit, the protection system (e.g. a circuit breaker) attached to the step-down transformer gets activated automatically.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2018

Design Space Exploration of Pericyclic Transmission with Counterbalance and Bearing Load Analysis

The pericyclic transmission provides the opportunity to vastly impact transmission design in rotorcraft due to its ability to provide exceedingly high reduction ratios in a single stage that would normally require multiple gear stages to produce. This could lead to lighter transmissions with fewer components, increased range, reliability, efficiency, speed and decreased cost to maintain. While many previous studies have focused upon the gearing within the pericyclic transmission, this work focused on what influences pericyclic geometry, and how changes in geometry impacts bearing loads. Specifically the loading of bearings that must deliver power from the input shaft to the nutating and rotating gears of the system were of primary concern. A comprehensive look at dynamic loads generated by nutating bodies was performed. Methods to address these dynamic loads via application of counterbalances and deviation from conventional pericyclic transmission designs were utilized to negate the dynamic moment of concern. Finally a static solver was used to determine the bearing loads with updated component geometries and mass moment of inertias that included the required counterbalances.

Pericyclic↗

NASA NTRS2018

Design Space Exploration of Pericyclic Transmission with Counterbalance and Bearing Load Analysis

Pericyclic↗

DOE OSTI2021

Codelivery of Paclitaxel and Parthenolide in Discoidal Bicelles for a Synergistic Anticancer Effect: Structure Matters

Nanotechnology has long been used to improve the tumor targeting of chemotherapeutics. The structure of these nanocarriers greatly affects their efficacy and toxicity. Herein, structurally stable nanodiscoidal phospholipid bicelles, which are capable of encapsulating anticancer drugs, paclitaxel (PTX) and parthenolide (PTL), are designed. This nanodiscoidal bicelle structure is shown previously to exhibit longer blood circulation, deeper tumor penetration, and increased tumor cell uptake compared with spherical particles of similar compositions. Structural characterization and loading capacity of the PTX‐ and PTL‐ loaded nanodiscs are conducted along with an investigation into the cytotoxicity of the bicelles based on the drug‐to‐lipid ratios of both drugs loaded separately and in combination. A significant synergistic effect between both loaded drugs is also observed in A549 lung cancer and drug‐resistant MDA‐MB‐231 cell lines. An in vivo study using mice bearing subcutaneous A549 tumors shows a long circulation time of the intravenously injected DiR‐loaded bicelles and significant antitumor efficacy of the PTX‐ and PTL‐loaded bicelles. This efficacious self‐assembled nanoparticle system displays great potential as a nanoplatform to codeliver multiple therapeutics or diagnostics for improving cancer therapy.

Rad, Armin Tahmasbi↗

DOE OSTI2018

Non-convex Multi-species Hopfield Models

In this work we introduce a multi-species generalization of the Hopfield model for associative memory, where neurons are divided into groups and both inter-groups and intra-groups pair-wise interactions are considered, with different intensities. Thus, this system contains two of the main ingredients of modern deep neural-network architectures: Hebbian interactions to store patterns of information and multiple layers coding different levels of correlations. The model is completely solvable in the low-load regime with a suitable generalization of the Hamilton–Jacobi technique, despite the Hamiltonian can be a non-definite quadratic form of the Mattis magnetizations. The family of multi-species Hopfield model includes, as special cases, the 3-layers Restricted Boltzmann Machine with Gaussian hidden layer and the Bidirectional Associative Memory model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Pillar[5]arene Based Conjugated Microporous Polymers for Propane/Methane Separation through Host–Guest Complexation

We present an efficient strategy for the preparation of conjugated microporous polymers incorporating pillar[5]arenes (P5-CMPs) with surface areas up to 400 m 2 g –1 via Pd-catalyzed Sonogashira–Hagihara cross-coupling reaction of triflate functionalized pillar[5]arene with 1,4-diethynylbenzene and 4,4'-diethynyl-1,1'-biphenyl linkers. In an effort to transfer intrinsic properties of pillar[5]arene, that is its ability to form strong host–guest complexes with linear hydrocarbons in solution, into the solid-state, we investigated the affinity of P5-CMPs toward propane gas. Unlike previously reported porous solids, which showed isosteric heats of adsorption ($Q$ st ) for propane in the range of 32.9–36.9 kJ mol–1 at zero coverage and increasing $Q$ st with rising loading due to intermolecular interactions between propane molecules, we observed very high $Q$ st values up to 53 kJ mol –1 at zero coverage, which gradually decreased to ~35 kJ mol –1 with increasing loadings. This observation indicates strong supramolecular host–guest complexation between propane and pillar[5]arene via multiple C–H/$π$ interactions, i.e., “macrocyclic effect” arising from the ideal size match of kinetic diameter of propane to the cavity of pillar[5]arene. This approach also allowed us to introduce thermodynamic selectivity for the separation of saturated hydrocarbons with low polarizability. High affinity of P5-CMPs for propane facilitated its efficient breakthrough separation from a simulated natural gas mixture (methane:propane, 9:1) at 298 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Dual-Bed Radioiodine Capture from Complex Gas Streams with Zeolites: Regeneration and Reuse of Primary Sorbent Beds for Sustainable Waste Management

Dual-sorbent systems are proposed for radioiodine management with a regenerated primary bed for multiple cycles of use in complex conditions and a secondary bed for disposal with higher waste loadings. Sorbent approaches for the effective capture of gaseous radioiodine (isotopes 129 I and 131 I) produced from a range of nuclear processes have been studied for over half a century. (1−5) Whether or not a sorbent (e.g., molecular sieve) is required to physically screen/trap or chemically bind a radionuclide of interest through chemisorption, the complexity of the gas stream has a large impact on the performance (e.g., loading capacity, selectivity) and active life of a sorbent bed. (3) Silver mordenite (AgZ), the U.S. Department of Energy baseline sorbent for radioiodine capture from nuclear processes, performs well within acidic conditions and at elevated temperatures (6) and can be consolidated into a chemically durable waste form for long-term disposal. (7,8) However, new sorbents are being sought because optimal capture performance of AgZ significantly decreases in dynamic oxidizing environments with competing species, and it is expensive and it contains Ag (a toxic metal). (9) Until a new sorbent is found to replace AgZ, the regeneration and reuse of AgZ is an attractive alternative to a single-use primary sorbent bed. In this regard, a primary sorbent could be designed for enhanced capture in complex gas streams and the ability to be regenerated for reuse. Here, a secondary sorbent could then be tailored for maximum iodine loading in the gas stream and chemical durability within a disposal facility.

chemisorption↗

DOE OSTI2018

In situ recovery of bio-based carboxylic acids

The economics of chemical and biological processes is often dominated by the expense of downstream product separations from dilute product streams. Continuous separation techniques, such as in situ product recovery (ISPR), are attractive in that they can concentrate products from a reactor and minimize solvent loss, thereby increasing purity and sustainability of the process. In bioprocesses, ISPR can have an additional advantage of increasing productivity by alleviating product inhibition on the microorganism. In this work, we developed a liquid–liquid extraction (LLE)-based ISPR system integrated with downstream distillation to selectively purify free carboxylic acids, which were selected as exemplary bioproducts due to their ability to be produced at industrially relevant titers and productivities. Equilibrium constants for the extraction of carboxylic acids into a phosphine-oxide based organic phase were experimentally determined. Complete recovery of acids from the extractant and recyclability of the organic phase were demonstrated through multiple extraction–distillation cycles. Using these data, an equilibrium model was developed to predict the acid loading in the organic phase as a function of the extraction equilibrium constant, initial aqueous acid concentration, pH, organic to aqueous volume ratio, and temperature. A distillation process model was then used to predict the energy input required to distill neat acid from an organic phase as a function of the acid loading in the organic phase feed. The heat integrated distillation train can achieve neat recovery of acetic acid with an energy input of 2.6 MJ kg -1 of acetic acid. This LLE-based ISPR system integrated with downstream distillation has an estimated carbon footprint of less than 0.36 kg CO 2 per kg of acetic acid, and provides a green approach to enable both new industrial bioprocesses, and process intensification of existing industrial operations by (1) increasing the productivity and titer of the bioprocess via decreasing end-product inhibition, (2) minimizing downstream separation energy input to less than 20% of the heating value of the product, and (3) generating no waste products.

09 BIOMASS FUELS↗

DOE OSTI2018

Improving BA Control Performance Through Advanced Regulation Requirements Prediction

The paper presents a comprehensive approach to predict balancing authority (BA) regulation requirements in order to improve BAs control performance. The proposed probabilistic approach takes into account multiple uncertain and time dependent factors affecting the system balance (e.g., wind and solar generation, electrical loads). A set of methods to predict the regulation has been developed: (1) probabilistic analysis of regulation requirements with respect to time of the day, (2) linear or nonlinear regression models linking regulation requirements to different influencing factors, (3) adopting time series forecasting techniques (e.g., autoregressive integrated moving average (ARIMA)) to account for temporal continuity and auto-correlation in the regulation requirements data. Proposed methodology has been tested and validated using actual California Independent System Operator (ISO) data.

Solar Generation, Wind Generation, Area Control Er↗

DOE OSTI2016

A Recursive Hypergraph Bipartitioning Framework for Reducing Bandwidth and Latency Costs Simultaneously

Intelligent partitioning models are commonly used for efficient parallelization of irregular applications on distributed systems. These models usually aim to minimize a single communication cost metric, which is either related to communication volume or message count. However, both volume- and message-related metrics should be taken into account during partitioning for a more efficient parallelization. There are only a few works that consider both of them and they usually address each in separate phases of a two-phase approach. In this work, we propose a recursive hypergraph bipartitioning framework that reduces the total volume and total message count in a single phase. In this framework, the standard hypergraph models, nets of which already capture the bandwidth cost, are augmented with message nets. The message nets encode the message count so that minimizing conventional cutsize captures the minimization of bandwidth and latency costs together. Our model provides a more accurate representation of the overall communication cost by incorporating both the bandwidth and the latency components into the partitioning objective. The use of the widely-adopted successful recursive bipartitioning framework provides the flexibility of using any existing hypergraph partitioner. In conclusion, the experiments on instances from different domains show that our model on the average achieves up to 52 percent reduction in total message count and hence results in 29 percent reduction in parallel running time compared to the model that considers only the total volume.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Comparative Assessment of Tactics to Improve Primary Frequency Response Without Curtailing Solar Output in High Photovoltaic Interconnection Grids

Power grid primary frequency response will be significantly impaired by Photovoltaic (PV) penetration increase because of the decrease in inertia and governor response. PV inertia and governor emulation requires reserving PV output and leads to solar energy waste. This paper exploits current grid resources and explores energy storage for primary frequency response under high PV penetration at the interconnection level. Based on the actual models of the U.S. Eastern Interconnection grid and the Texas grid, effects of multiple factors associated with primary frequency response, including the governor ratio, governor deadband, droop rate, fast load response. are assessed under high PV penetration scenarios. In addition, performance of batteries and supercapacitors using different control strategies is studied in the two interconnections. The paper quantifies the potential of various resources to improve interconnection-level primary frequency response under high PV penetration without curtailing solar output.

14 SOLAR ENERGY↗

DOE OSTI2018

Stasis PCM Test Results

A phase change material product distributed by Stasis Group, designed to reduce the cooling energy requirements of multi-story commercial buildings, was tested in a side-by-side comparison at FLEXLAB® (LBNL, 2018), a calibrated test facility for low energy building technology solutions. The product was installed according to Stasis recommendations in the ceiling plenum of a 600 ft 2 cell representative of an office space, while an identical cell was operated in the same conditions, except for the night-time pre-cooling sequence and without the product, to gather baseline data. The cells were operated to replicate summer conditions of a candidate office building for retrofit. For the tests conducted at FLEXLAB, two Phase Change Material (PCM) compositions were tested, with the second product having an upgraded composition for thermal performance and upgraded packaging that was used to meet with fire safety requirements for a plenum application. Two rounds of tests were performed, which were designed to collect data for both core (designated as Round 1) and perimeter (Round 2) office space conditions. Following the analysis of the data collected during Round 1, the experimental design was modified to better represent the thermal conditions of a typical office space in relation to the PCM performance. A new product was also brought in for testing in Round 2, which had an improved operational temperature range and packaging properties. In Round 1, multiple tests were conducted to evaluate the performance of the product under different conditions of internal loads, temperature setpoints, pre-cooling strategies and temperature control strategies. In Round 2, the internal conditions were kept constant, while the test conditions only differ in the amount of PCM installed in the test cell plenum and the length of the nighttime pre-cooling sequence. The performance benefits of PCM are generally two-fold: the material may reduce peak cooling energy use during daytime hours (a peak demand energy use benefit) and may allow for energy savings with pre-cooling at night through night flush HVAC controls when night time outside air temperatures are lower, and fan energy use for cooling is more efficient than the use of daytime compressor-based cooling. The focus of this research project was mainly on quantifying the energy savings potential of the latter case although some effects on peak reduction were noted. Between the cell with PCM and nighttime precooling and the reference cell without PCM or precooling, a reduction in cooling load during occupied hours was noted between 7.6 kWh (or 22% of occupied hours cooling load) and 12.5 kWh (or 33% of occupied pg. 7 hours cooling load). Future work can further evaluate the peak cooling load reduction benefits of this product in more detail. This report highlights the results of Round 2, which reflect the performance of the modified PCM product when operated in suitable conditions. The results of Round 1, along with an analysis of the experimental data to identify the factors that prevented the product from performing as desired are presented in Appendix B.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Subcritical Multiplication Ex-Core Calculations with Shift

This report details the development and testing of Virtual Environment for Reactor Applications (VERA) as applied to full core subcritical multiplication ex-core calculations with Shift. The limiting factor in being able to run these detector calculations with Monte Carlo is the amount of computational memory used by a fully-loaded full core problem. The main development features added to VERA in this effort were enabling source biasing and separable weight windows and integrating multithreading in Shift. With these capabilities, a subcritical multiplication detector response calculation on a full core Watts Bar Nuclear Plant Unit 1 (WBN1) ex-core model was successfully run on a moderately sized computing cluster.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗

DOE OSTI2017

MPACT Development Activities at University of Michigan

This report documents the efforts of general development tasks in MPACT performed primarily at the University of Michigan during FY17. These tasks included: 1. Unstructured CMFD - The unstructured CMFD supports the RIA and BWR challenge problems. It allows MPACT to simulate mixed lattice cores (e.g. 8x8 next to 9x9). This design feature is common to operating BWRs and also the SPERT experiments being used to validate VERA-CS for RIA. 2. Higher order spatial sources for MOC - The higher order sources are for improving the overall performance of MPACT. Higher order spatial sources in MOC provide accurate solutions with potentially significantly fewer mesh regions, thus speeding up every calculation. This method is also particularly important for BWRs. 3. Improving fine source region visualization - The improvement of the visualization of the flat source region mesh in MPACT is to support users and communication of models and results from MPACT. VERAView provides visualization of pin-wise information, however MPACT resolves the spatial solution to intra pin level. The capability provided here will let users visualize the full model geometry and components of the solution at the highest spatial resolution available from MPACT. 4. Automated spatial decomposition - The automated spatial decomposition removes the burden from the user to know how to spatially decompose the model for MPACT; a task that has been rather tricky in the past. Instead the user may give some number of processors that is a multiple of the axial mesh and MPACT will determine a nearly optimal decomposition that gives better load balance than most decompositions used currently. 5. Optimal relaxation factors for feedback - The development of optimal relaxation factor is to improve the stability and robustness of problems with feedback. Presently the default feedback values are 0.5, which may or may not be optimal. Without relaxation the fixed point iteration that is currently used is known to be unstable for large problems. The optimal relaxation factors are derived by utilizing a previous Fourier analysis. From the original milestone there was a scope change where the work on the Intel MIC was deferred for work on the automated spatial decomposition.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

97 MATHEMATICS AND COMPUTING↗