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At least 19 records

Information-Theoretic Exploration of Multivariate Time-Varying Image Databases

Modern scientific simulations produce very large datasets, making interactive exploration of such data computationally prohibitive. An increasingly common data reduction technique is to store visualizations and other data extracts in a database. The Cinema project is one such approach, storing visualizations in an image database for post hoc exploration and interactive image-based analysis. This work focuses on developing efficient algorithms that can quantify various types of multivariate dependencies existing within multi-variable datasets. It applies specific mutual information measures for the quantification of salient regions from multivariate image data. Here, using such information measures, the opacity of the images is modulated so that the salient regions are automatically highlighted and the domain scientists can interactively explore the most relevant regions for scientific discovery.

97 MATHEMATICS AND COMPUTING↗

A VR-based volumetric medical image segmentation and visualization system with natural human interaction

Volume rendering produces informative two-dimensional (2D) images from a 3-dimensional (3D) volume. It highlights the region of interest and facilitates a good comprehension of the entire data set. However, volume rendering faces a few challenges. First, a high-dimensional transfer function is usually required to differentiate the target from its neighboring objects with subtle variance. Unfortunately, designing such a transfer function is a strenuously trial-and-error process. Second, manipulating/visualizing a 3D volume with a traditional 2D input/output device suffers dimensional limitations. To address all the challenges, we design NUI-VR 2 , a natural user interface-enabled volume rendering system in the virtual reality space. NUI-VR 2 marries volume rendering and interactive image segmentation. It transforms the original volume into a probability map with image segmentation. A simple linear transfer function will highlight the target well in the probability map. More importantly, we set the entire image segmentation and volume rendering pipeline in an immersive virtual reality environment with a natural user interface. NUI-VR 2 eliminates the dimensional limitations in manipulating and perceiving 3D volumes and dramatically improves the user experience.

42 ENGINEERING↗

Exploiting Saturation Regimes and Surface Effects to Tune Composite Design: Single Platelet Nanocomposites of Peptoid Nanosheets and CaCO 3

Mineral-polymer composites found in nature exhibit exceptional structural properties essential to their function, and transferring these attributes to the synthetic design of functional materials holds promise across various sectors. Biomimetic fabrication of nanocomposites introduces new pathways for advanced material design and explores biomineralization strategies. This study presents a novel approach for producing single platelet nanocomposites composed of CaCO 3 and biomimetic peptoid (N-substituted glycines) polymers, akin to the bricks found in the brick-and-mortar structure of nacre, the inner layer of certain mollusc shells. The significant aspect of the proposed strategy is the use of organic peptoid nanosheets as the scaffolds for brick formation, along with their controlled mineralization in solution. Here, we employ the B28 peptoid nanosheet as a scaffold, which readily forms free-floating zwitterionic bilayers in aqueous solution. The peptoid nanosheets were mineralized under consistent initial conditions (σ calcite = 1.2, pH 9.00), with variations in mixing conditions and supersaturation profiles over time aimed at controlling the final product. Nanosheets were mineralized in both feedback control experiments, where supersaturation was continuously replenished by titrant addition and in batch experiments without a feedback loop. Complete coverage of the nanosheet surface by amorphous calcium carbonate was achieved under specific conditions with feedback control mineralization, whereas vaterite was the primary CaCO 3 phase observed after batch experiments. Thermodynamic calculations suggest that time-dependent supersaturation profiles as well as the spatial distribution of supersaturation are effective controls for tuning the mineralization extent and product. We anticipate that the control strategies outlined in this work can serve as a foundation for the advanced and scalable fabrication of nanocomposites as building blocks for nacre-mimetic and functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Circular olefin copolymers made de novo from ethylene and α-olefins

Ethylene/α-olefin copolymers are produced in huge scale and widely used, but their after-use disposal has caused plastic pollution problems. Their chemical inertness made chemical re/upcycling difficult. Ideally, PE materials should be made de novo to have a circular closed-loop lifecycle. However, synthesis of circular ethylene/α-olefin copolymers, including high-volume, linear low-density PE as well as high-value olefin elastomers and block copolymers, presents a particular challenge due to difficulties in introducing branches while simultaneously installing chemical recyclability and directly using industrial ethylene and α-olefin feedstocks. Here we show that coupling of industrial coordination copolymerization of ethylene and α-olefins with a designed functionalized chain-transfer agent, followed by modular assembly of the resulting AB telechelic polyolefin building blocks by polycondensation, affords a series of ester-linked PE-based copolymers. These new materials not only retain thermomechanical properties of PE-based materials but also exhibit full chemical circularity via simple transesterification and markedly enhanced adhesion to polar surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

From noise to information: The transfer function formalism for uncertainty quantification in reconstructing the nuclear density

The neutron distribution of neutron-rich nuclei provides critical information on the structure of finite nuclei and neutron stars. Parity violating experiments—such as PREX and CREX—provide a clean and largely model-independent determination of neutron densities. Such experiments, however, are challenging and expensive, which is why sound statistical arguments are required to maximize the information gained. We introduce a new framework, the “transfer function formalism,” aimed at uncertainty quantification, model selection, and experimental design in the context of neutron densities. The transfer functions (TFs) are built analytically by expressing the linear response of the objective function (e.g., χ2) to small perturbations of the data. Using the TF formalism, we are able to analyze the expected overall uncertainty—quantified in terms of bias and variance—of the mean square radius and interior density of 48 Ca and 208 Pb. Using relativistic mean field models as a proxy for the weak-charge density—and assuming that a total of five measurements could be performed on the weak form factor of 48 Ca and 208 Pb—we identify the optimal models and experimental locations that minimize the uncertainty in the extraction of the radius and interior density. We also explore the use of the TF formalism to understand the influence of prior distributions for the model parameters, as well as the optimization of model hyperparameters not constrained by the data. Here,we establish how the choice of experimental locations and the model that is used can have a significant impact on the final uncertainties of the extracted quantities of interest. For challenging experiments such as CREX and PREX, a proper quantification of such uncertainties is critical. We have demonstrated how the TF formalism provides several advantages for this type of analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Investigating Porous Media for Relief Printing Using Micro-Architected Materials

Advances in printed electronics are predicated on the integration of sophisticated printing technologies with functional materials. Although scalable manufacturing methods, such as letterpress and flexographic printing, have significant history in graphic arts printing, functional applications require sophisticated control and understanding of nanoscale transfer of fluid inks. In this paper, a versatile platform is introduced to study and engineer printing forms, exploiting a microscale additive manufacturing process to design micro-architected materials with controllable porosity and deformation. Building on this technology, controlled ink transfer for submicron functional films is demonstrated. The design freedom and high-resolution 3D control afforded by this method provide a rich framework for studying mechanics of fluid transfer for advanced manufacturing processes.

36 MATERIALS SCIENCE↗

Four-Switch Buck-Boost Converter Based on Model Predictive Control with Smooth Mode Transition Capability

Four-switch buck-boost converter supports both voltage step-up and step-down functionalities, but it suffers from mode transfer challenge that would need reliable mode detection when designed to operate in multi different modes. In this paper, a novel control method based on model predictive current control is proposed for such converter with inherent smooth mode transfer capability without extra design on mode detection and transfer scheme. Modulator and mode detection are replaced by an optimization process through cost function. This largely simplifies the design and makes it easily implemented. Therefore, seamless transfer between buck and boost modes is achieved with many other system level benefits. Simulation and experimental results are provided to verify the effectiveness of proposed method for the four-switch buck-boost converter.

42 ENGINEERING↗

Nonlocal elastic metasurfaces: Enabling broadband wave control via intentional nonlocality

While elastic metasurfaces offer a remarkable and very effective approach to the subwavelength control of stress waves, their use in practical applications is severely hindered by intrinsically narrow band performance. In applications to electromagnetic and photonic metamaterials, some success in extending the operating dynamic range was obtained by using nonlocality. However, while electronic properties in natural materials can show significant nonlocal effects, even at the macroscales, in mechanics, nonlocality is a higher-order effect that becomes appreciable only at the microscales. This study introduces the concept of intentional nonlocality as a fundamental mechanism to design passive elastic metasurfaces capable of an exceptionally broadband operating range. The nonlocal behavior is achieved by exploiting nonlocal forces, conceptually akin to long-range interactions in nonlocal material microstructures, between subsets of resonant unit cells forming the metasurface. These long-range forces are obtained via carefully crafted flexible elements, whose specific geometry and local dynamics are designed to create remarkably complex transfer functions between multiple units. The resulting nonlocal coupling forces enable achieving phase-gradient profiles that are functions of the wavenumber of the incident wave. The identification of relevant design parameters and the assessment of their impact on performance are explored via a combination of semianalytical and numerical models. The nonlocal metasurface concept is tested, both numerically and experimentally, by embedding a total-internal-reflection design in a thin-plate waveguide. Results confirm the feasibility of the intentionally nonlocal design concept and its ability to achieve a fully passive and broadband wave control.

36 MATERIALS SCIENCE↗

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Online Control of Process Variance Using Feedback

For many stochastic control problem formulations, design objectives typically include constraining the covariance of the outputs of the system. Recent engineering challenges have demonstrated an interest in controlling system output covariances to a setpoint. However, open-loop control of system output covariance can be exceptionally challenging when the system has significant plant uncertainties. This paper explores closed-loop control of the system’s output covariance using a windowed variance function. The stochastic nature of the windowed variance feedback means that root locus and other classical control design techniques cannot be immediately used. Expected value analysis is used to generate transfer functions for the average response of such systems, and the results are used to design controllers. Stochastic simulations are presented to verify the analytical results and make other observations concerning closed-loop variance control.

Bieniek, Mitchell↗

Elucidation of Design Criteria for V‐based Redox Mediators: Structure‐Function Relationships that Dictate Rates of Heterogeneous Electron Transfer

Redox mediators are attractive solutions for addressing the stringent kinetic stipulations required for efficient energy conversion processes. In this work, we compare the electrochemical properties of four vanadium complexes, namely [V(acac) 3 ], [V 6 O 7 (OMe) 12 ], [ n Bu 4 N] 3 [V 6 O 13 (TRIS NO2 ) 2 ], and [ n Bu 4 N] 5 [V 18 O 46 (NO 3 )] in non-aqueous solutions on glassy carbon electrodes. The goal of this study is to investigate the electron transfer kinetics and diffusivity of these compounds under identical experimental conditions to develop an understanding of structure-function relationships that dictate the physicochemical properties of vanadium oxide assemblies. Complex selection was dictated by two criteria – (1) nuclearity of the transition metal complexes (2) distribution of electron density in the native electronic configuration. In conclusion, our analyses establish that electronic communication between metal centers significantly impacts charge transfer kinetics of these vanadium-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stability margin analysis of the Holos-quad microreactor design

A simplified point model of the Holos-Quad microreactor is introduced. The model is based on the point kinetics equations coupled to three heat balance equations, representing the mean temperatures of the fuel particles, the graphite moderator and the helium coolant. The differential equations are converted to the frequency domain, enabling the construction of the closed-loop reactor transfer function. Using this function the stability margins of the core design is analyzed for various power levels. It is shown that the gain margins approaches infinity, demonstrating the stability of the reactor for all power levels. The phase margin at nominal power is about 60 degrees, however it shows a non-monotonous dependence on power, with minimal value obtained for about 10% of nominal power. As power level is further increased, the phase margin also increases, demonstrating the reactor becomes more stable. This behavior may be of high importance in load-follow scenarios, where the power level of the reactor changes with time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Dynamic Model of Active Front-End Converters with 2DOF-PI Controllers for DC Bus Voltage Control

In most of the research, a conventional one degree-of-freedom type proportional-integral controller (1DOF-PI) is used for the regulation of the dc bus voltage in an Active Front-End Converter (AFEC). This paper proposes a two degree-of-freedom type proportional-integral controller (2DOF-PI) for the regulation of dc bus voltage in an AFEC. Such a controller eliminates the left-half plane (LHP) zero in the command-to-output transfer function for dc bus voltage regulation that is inherent to the conventional 1DOF-PI for this application. The dynamic model of an AFEC in the synchronous reference frame (SRF) is presented. An inner current control and outer dc voltage control loop is built for controlling the AFEC. Vector-control scheme is employed to achieve decoupled control of direct-axis and quadrature-axis currents. The outer dc voltage control loop is designed with a 2DOF-PI to complete the control system. Transfer function (TF) of the controller is derived based on the presented model and used to calculate the controller gains. Transient and steady state performance of the designed controllers are investigated in MATLAB/Simulink and results presented.

Thapa, Anup↗

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗

ForceFinder

SAND2025-11750O ForceFinder extends the Structural Dynamics Python Libraries (SDynPy) with comprehensive tools for inverse source estimation (ISE) tasks via frequency response function (FRF) matrix inversion. The software is designed for transfer path analysis and multiple-input/multiple-output (MIMO) vibration control problems. It allows users to estimate sources through various algorithms, from the basic Moore-Penrose pseudo-inverse to statistical learning methods such as Tikhonov regularization via an L-curve and elastic net regularization via an information criterion. ForceFinder uses an object-oriented framework, where all components of the ISE problem—such as FRFs, responses, and transformations—are stored in a "SourcePathReceiver" object. This software can be applied to any noise and vibration problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Carter, Steven [Sandia National Lab. (SNL-CA), Liv↗

Cost optimization of high recovery single stage gap membrane distillation

Estimating the cost of membrane distillation (MD) processes at scale is confounded by the vast array of possible process configurations, module configurations, and operating conditions and the extreme sensitivity of MD costs to these parameters. This work develops a comprehensive MD cost optimization framework for minimizing the levelized cost of water (LCOW) as a function of feed salinity and temperature. Our single stage gap MD model fully captures solution properties, heat transfer, and mass transfer relationships in the modules to determine the cost optimal design as a function of multiple decision variables, including process configuration (feed location and connectivity between the hot and cold-side), the gap type (air, permeate, conductive), equipment sizes (membrane, heater, chiller, and heat exchanger units), and operating conditions (flow rates, hot and cold side temperatures, and heating and cooling duties). We estimate that the minimum LCOW of conductive gap MD ranges between $\$$10 and $\$$16 per m 3 for feed salinities of 25 g/L to 200 g/L and water recoveries of 30%–75%. Further, we update several heuristic assumptions for cost optimal system operation, including: increased thermal conductivity of the gap decreases system costs; cost optimal MD operates at the highest allowable temperature; and the LCOW is a strong function of brine salinity. Finally, we find that improving membrane properties and lowering heating and cooling costs will provide only modest reductions in costs (<25%).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗