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At least 343 records · Page 19

Statistics and sensitivity of axion wind detection with the homogeneous precession domain of superfluid helium-3

The homogeneous precession domain (HPD) of superfluid He 3 has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling g a N N competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in order to make realistic projections for a full-fledged experimental program. We include the effects of clock error and measurement error in a concrete readout scheme using superconducting qubits and quantum metrology. This work also provides a more general framework to describe the statistics associated with the axion gradient coupling through the treatment of a transient resonance with a nonstationary background in a time-series analysis. Incorporating an optimal data-taking and analysis strategy, we project a sensitivity approaching g a N N ∼ 10 − 12 GeV − 1 across a decade in axion mass. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments

Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

97 MATHEMATICS AND COMPUTING↗

pyDAKOTA (Dakota Driver) [SWR-13-16]

pyDAKOTA is an interface to Sandia Laboratory's Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) analysis suite. Allows for users to construct DAKOTA input, feed the input to DAKOTA, and for DAKOTA to call a python object with a "dakota_callback" function for function evaluations. This allows for a light-weight custom python interface to DAKOTA. This is intended to be used with the OpenMDAO dakota driver Author: NREL WISDEM Team

Graf, Peter↗

A Tritium Management Tool for Light Water Detritiation - 17320

Kurion{sup R}, Inc. (Kurion) has developed the Modular Detritiation System (MDS{sup R}) based on the working principle of combined electrolysis catalytic exchange (CECE), offering a combination of efficiency, maturity, and cost among alternatives. The technology builds on proven heavy water solutions, and although is focused on light water, it can be adapted for heavy water detritiation. The application of MDS{sup R} is a modular approach and allows for a flexible design that is adaptable to a range of facility sizes and processing needs. This modular approach delivers a factory quality and plug-n-process system that utilizes commercially available equipment and minimizes impact to a facility's day-to-day operations. In addition to focusing on optimization of the MDS{sup R} technology, Kurion has strategies for a complete detritiation solution of which MDS{sup R} is a part of. The strategy, supported by modeling and numerical analysis, is a systematic approach to managing front-end pretreatment (if required), overall volume reduction, and amount and method of tritium retention. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗

SU-F-T-192: Study of Robustness Analysis Method of Multiple Field Optimized IMPT Plans for Head & Neck Patients

Purpose: Proton therapy is more sensitive to uncertainties than photon treatments due to protons’ finite range depending on the tissue density. Worst case scenario (WCS) method originally proposed by Lomax has been adopted in our institute for robustness analysis of IMPT plans. This work demonstrates that WCS method is sufficient enough to take into account of the uncertainties which could be encountered during daily clinical treatment. Methods: A fast and approximate dose calculation method is developed to calculate the dose for the IMPT plan under different setup and range uncertainties. Effects of two factors, inversed square factor and range uncertainty, are explored. WCS robustness analysis method was evaluated using this fast dose calculation method. The worst-case dose distribution was generated by shifting isocenter by 3 mm along x,y and z directions and modifying stopping power ratios by ±3.5%. 1000 randomly perturbed cases in proton range and x, yz directions were created and the corresponding dose distributions were calculated using this approximated method. DVH and dosimetric indexes of all 1000 perturbed cases were calculated and compared with the result using worst case scenario method. Results: The distributions of dosimetric indexes of 1000 perturbed cases were generated and compared with the results using worst case scenario. For D95 of CTVs, at least 97% of 1000 perturbed cases show higher values than the one of worst case scenario. For D5 of CTVs, at least 98% of perturbed cases have lower values than worst case scenario. Conclusion: By extensively calculating the dose distributions under random uncertainties, WCS method was verified to be reliable in evaluating the robustness level of MFO IMPT plans of H&N patients. The extensively sampling approach using fast approximated method could be used in evaluating the effects of different factors on the robustness level of IMPT plans in the future.

60 APPLIED LIFE SCIENCES↗

Data-Driven Network Reduction and Aggregation with DERs for Outage Management

A novel approach exploiting the growing number of Distributed Energy Resources (DERs) to enhance distribution system resiliency is presented. After a power outage, DERs and loads are aggregated into switch-level equivalent models, preserving, at the same time, controllability information to enable real-time outage management analysis. Pre-fault smart-meters measurements are used as inputs for Artificial Neural Networks and Cold Load Pick-Up (CLPU) modeling to obtain short-term load forecast, while an optimal restoration analysis is carried out to determine restored network topology and utility-controlled loads and DERs set-points

Menazzi, Matteo↗

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗

Electromagnetic Design Optimization Integrated with Mechanical Stress Analysis of PM-Assisted Synchronous Reluctance Machine Topologies Enabled with a Blend of Magnets

Permanent Magnet-Assisted Synchronous Reluctance Machines (PMASynRM) provide a low-cost alternative to Surface PM Machines due to the use of relatively lower grades of rare-earth (RE) or RE-free magnets, as the performance degradation due to weaker magnets is compensated by the presence of reluctance torque. However, the weaker magnets suffer from a high risk of demagnetization, leading to unreliable motor operation. Using a blend of RE and RE-free magnets has the potential to overcome this issue. This paper proposes to blend different grades of various rare-earth (RE) and rare-earth-free (RE-free) magnets in six different combinations and utilizes them in two-layer and three-layer U-shaped PMASynRM topologies with both eight-pole and six-pole variations. The rotor of the various designs is then optimized using a differential evolution (DE) based optimization algorithm to obtain low-cost designs with reduced RE magnet volume and minimum demagnetization risk. The optimization of each design is also integrated with the evaluation of mechanical stresses in the rotor laminations so as to maintain the stresses below the material yield strength. Furthermore, the various performance metrics, such as toque–speed/power–speed characteristics, demagnetization, and efficiency maps, are evaluated for each of the optimized and mechanically feasible designs. A quantitative comparison of the various optimized designs is also obtained to highlight the various trade-offs. The results indicate the feasibility of meeting the baseline torque requirement across the entire speed range, even with a 100% reduction in RE magnet volume and less than 5% demagnetization risk, while achieving a cost reduction exceeding 50%. Moreover, the two-layer, eight-pole designs exhibit relatively higher performance, whereas the three-layer, eight-pole designs are found to be the most economical option.

design optimization↗

LogPath: Log data based energy consumption analysis enabling electric vehicle path optimization

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. Here we successfully validated the system using log data collected from an EV and Tesla's Supercharging map in the US and compared it with the commercially available navigation system, Tesla's trip planner, whose capabilities solely include charging time and routing.

EV (Electric vehicles) navigation↗

Thermal desalination via supercritical CO 2 Brayton cycle: Optimal system design and techno-economic analysis without reduction in cycle efficiency

The supercritical carbon dioxide (sCO 2 ) Brayton cycle is an upcoming power cycle potentially providing higher energy efficiency and more compact turbomachinery compared to traditionally used Brayton and Rankine cycles. The heat rejected from the sCO 2 Brayton cycle is quite hot (>70 degrees C) and must be cooled before flowing back to the compressor. However, this heat is ideal for producing fresh water with a thermal desalination system. This paper focuses on integrating multi-effect distillation (MED) with the sCO 2 stream exiting the recuperator. A new methodology for integration of MED with the sCO 2 Brayton cycle is proposed for simultaneous production of power and fresh water. The distillate produced does not affect the sCO 2 cycle efficiency, i.e., distillate is produced without being a parasitic load to the power plant compared to conventional integration of MED with steam Rankine cycle. To maximize the energy recovery from the waste heat stream exiting the sCO 2 cycle, a new methodology is formulated and discussed in the Appendix. Integration of MED with a 115 MW e sCO 2 power plant (49.2% efficient) produces 2813 m3/day of distillate and the cost of distilled water is 1.1 $/m 3 . Further, to enhance the distillate production, an innovative MED configuration is proposed, and for the same cycle operating condition with an electrical output 115 MW e , the net distillate production increases by 59% to 4472 m 3 /day at 1 $/m 3 . The cost of desalinated water for the integrated system is 17.8% lower than that of a standalone reverse-osmosis plant having the same capacity. Parametric analysis is performed to calculate the optimal design parameters for the integrated MED-sCO 2 system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SU-E-T-163: Evaluation of Dose Distributions Recalculated with Per-Field Measurement Data Under the Condition of Respiratory Motion During IMRT for Liver Cancer

Purpose: The dose distributions within the real volumes of tumor targets and critical organs during internal target volume-based intensity-modulated radiation therapy (ITV-IMRT) for liver cancer were recalculated by applying the effects of actual respiratory organ motion, and the dosimetric features were analyzed through comparison with gating IMRT (Gate-IMRT) plan results. Methods: The 4DCT data for 10 patients who had been treated with Gate-IMRT for liver cancer were selected to create ITV-IMRT plans. The ITV was created using MIM software, and a moving phantom was used to simulate respiratory motion. The period and range of respiratory motion were recorded in all patients from 4DCT-generated movie data, and the same period and range were applied when operating the dynamic phantom to realize coincident respiratory conditions in each patient. The doses were recalculated with a 3 dose-volume histogram (3DVH) program based on the per-field data measured with a MapCHECK2 2-dimensional diode detector array and compared with the DVHs calculated for the Gate-IMRT plan. Results: Although a sufficient prescription dose covered the PTV during ITV-IMRT delivery, the dose homogeneity in the PTV was inferior to that with the Gate-IMRT plan. We confirmed that there were higher doses to the organs-at-risk (OARs) with ITV-IMRT, as expected when using an enlarged field, but the increased dose to the spinal cord was not significant and the increased doses to the liver and kidney could be considered as minor when the reinforced constraints were applied during IMRT plan optimization. Conclusion: Because Gate-IMRT cannot always be considered an ideal method with which to correct the respiratory motional effect, given the dosimetric variations in the gating system application and the increased treatment time, a prior analysis for optimal IMRT method selection should be performed while considering the patient's respiratory condition and IMRT plan results.

60 APPLIED LIFE SCIENCES↗

The ReactorSTM: Atomically resolved scanning tunneling microscopy under high-pressure, high-temperature catalytic reaction conditions

To enable atomic-scale observations of model catalysts under conditions approaching those used by the chemical industry, we have developed a second generation, high-pressure, high-temperature scanning tunneling microscope (STM): the ReactorSTM. It consists of a compact STM scanner, of which the tip extends into a 0.5 ml reactor flow-cell, that is housed in a ultra-high vacuum (UHV) system. The STM can be operated from UHV to 6 bars and from room temperature up to 600 K. A gas mixing and analysis system optimized for fast response times allows us to directly correlate the surface structure observed by STM with reactivity measurements from a mass spectrometer. The in situ STM experiments can be combined with ex situ UHV sample preparation and analysis techniques, including ion bombardment, thin film deposition, low-energy electron diffraction and x-ray photoelectron spectroscopy. The performance of the instrument is demonstrated by atomically resolved images of Au(111) and atom-row resolution on Pt(110), both under high-pressure and high-temperature conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Approximate entropy analysis for nonlinear beam dynamics

In this paper, we applied an approximate entropy (ApEn) analysis to nonlinear beam dynamics. Due to the presence of strong nonlinear magnets, the degree of chaos in beam motion gradually increases proportionally with its amplitude. Such chaos can be quantitatively characterized with ApEn of beam turn-by-turn readings. ApEn analysis is a technique used to quantify the amount of regularity and the unpredictability of fluctuations with respect to time-series data. The ApEn, when applied as a chaos indicator, can then be used for nonlinear lattice optimization and analysis. The National Synchrotron Light Source II (NSLS-II) electron storage ring lattice was used as an example of a real-world application of our technique. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Approximate Entropy Analysis for Nonlinear Beam Dynamics

In this paper, we applied an approximate entropy (ApEn) analysis to nonlinear beam dynamics. Due to the presence of strong nonlinear magnets, the degree of chaos in beam motion gradually increases proportionally with its amplitude. Such chaos can be quantitatively characterized with ApEn of beam turn-by-turn readings. ApEn analysis is a technique used to quantify the amount of regularity and the unpredictability of fluctuations with respect to time-series data. The ApEn, when applied as a chaos indicator, can then be used for nonlinear lattice optimization and analysis. The National Synchrotron Light Source II (NSLS-II) electron storage ring lattice was used as an example of a real-world application of our technique.

43 PARTICLE ACCELERATORS↗

Analysis of the SiMPL Method for Density-Based Topology Optimization

We present a rigorous convergence analysis of a new method for density-based topology optimization that provides pointwise bound-preserving design updates and faster convergence than other popular first-order topology optimization methods. Due to its strong bound preservation, the method is exceptionally robust, as demonstrated in numerous examples here and in the companion article [D. Kim et al., Struct. Multidiscip. Optim., 68 (2025), 74]. Furthermore, it is easy to implement with clear structure and analytical expressions for the updates. Our analysis covers two versions of the method, characterized by the employed line search strategies. We consider a modified Armijo backtracking line search and a Bregman backtracking line search. For both line search algorithms, our algorithm delivers a strict monotone decrease in the objective function and further intuitive convergence properties, e.g., strong and pointwise convergence of the density variables on the active sets, norm convergence to zero of the increments, convergence of the Lagrange multipliers, and more. In addition, the numerical experiments demonstrate apparent mesh-independent convergence of the algorithm. Here, we refer to the new algorithm as the SiMPL method (pronounced “simple”), which stands for Sigmoidal Mirror descent with a Projected Latent variable.

97 MATHEMATICS AND COMPUTING↗

Advanced Manufacturing for Nuclear Core Design

Advanced manufacturing technologies are fundamentally altering the way components are designed and manufactured. The potential application space for these technologies within the nuclear industry is broad, with the most benefit realized from the ability to approach the nuclear core design problem from an advanced manufacturing perspective. One of the objectives of the US Department of Energy Office of Nuclear Energy (DOE-NE) Transformational Challenge Reactor (TCR) design and analysis activities is to demonstrate this approach. A mitigation of conventional constraints realized through advanced manufacturing methods opens up the design space, enabling a departure from typical axially uniform pin- and plate-type geometries common to operating nuclear reactors worldwide. The ability to use more heterogenous spatial and material flexibilities increases the demand on the predictability of higher fidelity modeling and simulation tools to be used in the design, analysis, and optimization processes. Leading candidate advanced manufacturing technologies for nuclear applications are additive methods that provide for high dimensional control and incorporation of small design features such as powder bed fusion, laser directed energy deposition, and binder jetting. Potential applications to core design identified during DOE-NE TCR design and analysis activities fall into two categories: those that are uniquely enabled by additive manufacturing technologies and those that are enabled by the characteristics (e.g., repeatability and instrumentation) inherent to these technologies. Identified applications include reducing in-core parasitic materials, improving heat conduction across fuel-cladding gaps, optimizing thermal hydraulic design with flexible coolant flow geometries, reducing uncertainties in manufactured components, optimizing core conditions with design heterogeneities, designing feedback mechanisms using material characteristics, and embedding burnable poisons. These potential applications join a host of nuclear industry advanced manufacturing activities to recapture manufacturing capabilities, reduce manufacturing costs and complexities, reduce deployment time, and enable performance and operational enhancements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this paper, we aim to minimize the cloud computing cost spent on bioinformatics data analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). Finally, this paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.

97 MATHEMATICS AND COMPUTING↗