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At least 1,099 records · Page 61

The Impact of Disruptions on the Economics of a Tokamak Power Plant

Tokamaks are often considered to be a leading candidate for near-term, cost-effective fusion energy, but these devices are susceptible to sudden loss of confinement events called disruptions. The threat of disruptions has garnered serious attention in research for the next generation of burning plasma experiments, such as ITER, but has received little treatment in economic studies of magnetic fusion energy. In this paper, we present a model for quantifying the effect of disruptions on the cost of electricity produced by a tokamak power plant (TPP). We outline the various ways disruptions increase costs and decrease revenues, introduce metrics to quantify these effects, and add them to a levelized cost of electricity (LCOE) model. Additionally, we identify several rate-limiting repair steps and introduce a classification system of disruption types based on the time to return to operations. We demonstrate how the LCOE model can be used to find the cost of electricity and the requirements for disruption handling of a TPP, and we further highlight where future research can have a strong impact in neutralizing the “showstopping” potential of disruptions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Let it rip: The mechanics of self-bisection in asexual planarians determines their population reproductive strategies

Asexual freshwater planarians reproduce by transverse bisection (binary fission) into two pieces. This process produces a head and a tail, which fully regenerate within 1-2 weeks. How planarians split into two offspring - using only their musculature and substrate traction – is a challenging biomechanics problem. We found that three different species, Dugesia japonica, Girardia tigrina and Schmidtea mediterranea, have evolved three different mechanical solutions to self-bisect. Using time lapse imaging of the fission process, we quantitatively characterize the main steps of division in the three species and extract the distinct and shared key features. Across the three species, planarians actively alter their body shape, regulate substrate traction, and use their muscles to generate tensile stresses large enough to overcome the ultimate tensile strength of the tissue. Moreover, we show that how each planarian species divides dictates how resources are split among its offspring. This ultimately determines offspring survival and reproductive success. Furthermore, heterospecific differences in the mechanics of self-bisection of individual worms explain the observed differences in the population reproductive strategies of different planarian species.

59 BASIC BIOLOGICAL SCIENCES↗

Disentangling electronic, lattice, and spin dynamics in the chiral helimagnet Cr 1/3 NbS 2

We investigate the static and ultrafast magneto-optical response of the hexagonal chiral helimagnet Cr 1/3 NbS 2 above and below the helimagnetic ordering temperature. The presence of a magnetic easy plane contained within the crystallographic $ab$-plane is confirmed, while degenerate optical pump-probe experiments reveal significant differences in the dynamic between the parent, NbS 2 , and Cr-intercalated compounds. Time-resolved magneto-optical Kerr effect measurements show a two-step demagnetization process, where an initial, subpicosecond relaxation and subsequent buildup (τ > 50ps) in the demagnetization dynamic scale similarly with increasing pump fluence. Despite theoretical evidence for partial gapping of the minority spin channel, suggestive of possible half-metallicity in Cr 1/3 NbS 2 , such a long demagnetization dynamic likely results from spin-lattice relaxation as opposed to minority state blocking. However, comparison of the two-step demagnetization process in Cr 1/3 NbS 2 with other 3d intercalated transition metal dichalcogenides reveals a behavior that is unexpected from conventional spin-lattice relaxation, and may be attributed to the complicated interaction of local moments with itinerant electrons in this material system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗

Post-growth Recrystallization by Halides for High Throughput CIGS Photovoltaics

The objective of this project in broad terms was to address the capital equipment expense associated with Cu(In,Ga)Se 2 (CIGS) as a material for photovoltaic devices. This project has demonstrated a new method for processing CIGS using metal halides to recrystallize and enhance the quality of the final materials. A wide spectrum of metal halides were examined. Of these Cu and In chlorides and AgBr were found to be most effective; and, of these, AgBr was found to produce exemplary results. Cu and In chlorides were found to produce undesirable changes in film composition. The project demonstrated over 16% efficiency in a CIGS device deposited at 10x the standard rate of ~1 μm/hr and with a >100°C reduction in maximum temperature to 450°C. The entire deposition of the absorber layer including the recrystallization step required only 15 minutes, which is a 4x decrease in processing time.

14 SOLAR ENERGY↗

SWIFT: Single-pass, Weather Independent Fractionation Technology for Improved Property Control of Corn Stover Feedstock

Corn stover is an abundant source of biomass that can be utilized for bioenergy production, representing 70% of the available crop residues in the U.S. However, recent projections estimate that over 60% of corn stover will be collected at moisture levels that exceed 20% moisture (wet basis), resulting in instability in conventional baled logistics systems due to unwanted microbial degradation. This biomass challenge can be solved with a paradigm-shifting technology or SWIFT: Single-pass, Weather Independent Fractionation Technology. SWIFT streamlines collection by eliminating multiple time-consuming, costly, non-value-added, field and weather-dependent steps that comprise the current state of the art in corn stover harvest. The unique approach envisions simultaneous harvest and collection of high, dry matter, whole-plant corn (i.e., stover plus grain) combined with anaerobic storage and fractionation as a means to positively influence the physical and chemical characteristics of corn stover biomass and the resulting feedstock.

09 BIOMASS FUELS↗

Simultaneous Thermal Analysis of Anion and Cation Exchange Resins

The decomposition reactions of one anion exchange resin (Bio-Rad AG MP-1) and one cerium-loaded cation exchange resin (DOWEX 50W-X8), both in nitrate form, were analyzed using simultaneous thermal analysis methods. Thermogravimetric analysis, dynamic scanning calorimetry, and evolved gas analysis were deployed in combination to probe the temperature profiles, reaction enthalpies, and off-gases from these resins under process-relevant temperature ranges. Analysis indicates that both resins undergo three clear phases during heating. The first is a dehydration step with prominent off-gassing of water, the second is a decomposition step with evolution of several gaseous species, and lastly is a combustion step with a large release of CO 2 . The temperature at which combustion ends varies slightly between AG MP-1 and DOWEX 50W: combustion completed at ~690°C and ~810°C, respectively. For the DOWEX cation resin, the combustion reaction showed greater sensitivity to the availability of oxygen under the test conditions. Both resins displayed broad exotherms during the combustion step of the decomposition, but the DOWEX cation resin demonstrated a 4.5 times higher specific enthalpy than that of the AG MP-1.

36 MATERIALS SCIENCE↗

Position Papers for the 2024 ASCR Workshop on Neuromorphic Computing for Science

Engineering novel neuromorphic computing systems with functionalities, capabilities, and energy efficiency similar to biological brains is one of the most exciting and challenging scientific endeavors of our time. This workshop aims to identify key research needs, challenges, and next steps necessary to develop biologically realistic neuromorphic circuits primitives that capture the functionality of neural systems found in nature. Moreover, simulating neuromorphic computing primitives integrated into networks will be key to under standing their behavior at scale, particularly for those computing architectures where full-scale commercial fabrication is not yet readily accessible. Appropriate neuroscience datasets and metrics will have to be established to vet proposed neuromorphic circuits.

97 MATHEMATICS AND COMPUTING↗

Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.

Rendon, Gumaro↗

Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States: Preprint

This work describes the development of a process based and open source balance of system cost model which provides the capability to evaluate both existing and novel offshore wind technologies. Individual design and installation steps are represented with bottom-up engineering models that compute the times and costs associated with the process; furthermore, operational constraints are assigned to each process so that delays due to weather and presence of marine mammals may be accounted for in the overall project timeline. The model structure, assumptions, inputs, and results are vetted with industry partners and compared against actual projects for validation; installation times are shown to agree with real data with relative errors of less than 35\%. Case studies are presented to demonstrate the functionality of the model. First, individual vessel efficiencies are computed for varying numbers of installation vessels and weather time series to show the diminishing returns of more than two feeder barges. Then, array cable capital costs and installation times are determined for a representative project with different turbine sizes. This quantifies the cost benefit tradeoffs of decreasing number of turbines, increased turbine spacing, and fewer turbine terminations and shows a net cost savings. These results demonstrate that the BOS model features the accuracy, functionality, and accessibility to serve as the foundation for a wide range of analyses to identify cost reduction potentials for offshore wind energy in the United States.

17 WIND ENERGY↗

Carbon composition with hierarchical porosity, and methods of preparation

A method for fabricating a porous carbon material possessing a hierarchical porosity, the method comprising subjecting a precursor composition to a curing step followed by a carbonization step, the precursor composition comprising: (i) a templating component comprised of a block copolymer, (ii) a phenolic component, (iii) a dione component in which carbonyl groups are adjacent, and (iv) an acidic component, wherein said carbonization step comprises heating the precursor composition at a carbonizing temperature for sufficient time to convert the precursor composition to a carbon material possessing a hierarchical porosity comprised of mesopores and macropores. Also described are the resulting hierarchical porous carbon material, a capacitive deionization device in which the porous carbon material is incorporated, as well as methods for desalinating water by use of said capacitive deionization device.

36 MATERIALS SCIENCE↗

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗

Nonpigmented PsbR is involved in the integrity of excitation landscape in higher plant photosystem II, a case study in Arabidopsis thaliana and a mutant

PsbR is a nonpigmented 10 kDa protein in Photosystem II (PSII) in algae and plants. A recent structural study clarified its enigmatic structural location in a Photosystem II megacomplex that has baffled the community for more than four decades. Our current study interrogates whether absence of PsbR affects the overall dynamics of excitation energy migration within light harvesting complexes (LHC) and PSII super assemblies using highly-active PSII membrane particles, so-called BBY particles, isolated from a PsbR deletion mutant (ΔPsbR) of Arabidopsis thaliana. A femto-second (fs)-time-resolved transient absorption experimentation recorded at 77 K with selective excitation of Chl b which is exclusively present in LHCs enabled us to resolve the temporal differences in LHC→LHC and LHC→PSII excitation energy transfer steps. By applying specific target spectro-kinetic models to the transient absorption datasets, we demonstrated that the time constants of Chl a LHC → Chl a LHC excitation transfer significantly elongates in the ΔPsbR LHC-PSII particles, suggestive of the decreased aggregation level of photosynthetic proteins in the mutant. These findings highlight excitation energy transfer integrity in LHC-PSII assembly is not only determined by the pigmented light-harvesting complexes, but also synergistically by the nonpigmented PSII components. Furthermore, the disturbed integrity in dynamics of excitation energy transfer pathway within LHC-PSII supercomplex is discussed in the context of the altered LHC-PSII megacomplexes type I and II architectures which result from the absence of the PsbR protein in higher plant PSII.

Chlorophyll↗

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

36 MATERIALS SCIENCE↗

Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography ( MSOT ) imaging

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.

Juhong, Aniwat↗

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗