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At least 1,243 records · Page 69

5G Enabled Transformative Co-design and Co-simulation Framework for Grid Decarbonization and Modernization

5G is a breakthrough technology to enable a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for clean energy economy and grid modernization. The clean energy transformation not only requires innovative manufacturing, algorithm, application, and cross-domain co-simulation, but also a systematic evaluation and comprehensive support on the microelectronic and architectural computing customization to enable and maximize the high renewable penetration in the future power grid. The co-design of decarbonized grid, reliable yet efficient communication, and affordable yet ubiquitous computing will unlock the potential of almost any available tools for the undergoing clean energy transformation. 5G is also an opportunity to rethink the paradigm of infrastructure planning, as computing will be one of the core services for society alongside electricity and communication services.

5G↗

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Interstellar Communication Channel Based on a Biological Universal

Cellular biosynthesis starts with sugar substrates and continues energetically downhill to yield amino acid, rapid, and nucleotide products. To understand the energetics of these processes, we calculated the energy for biosynthesis from sugars of E. cali's amino acids, nucleotides, and lipids. We found that the biosynthesis of amino acids and lipids from sugar substrates proceeds by redox disproportionation. of sugar carbon with a favorable energy of about -11 kcal/mole of carbon. Overall, redox disproportion of sugar carbon accounted for 84% and 96% (ATP only 6% and 1%) of the total biosynthetic energy of amino acids and lipids (the major cellular constituents). Next, we calculated for all 48 possible 3-carbon substrates the energy of maximal disproportionation to carbon dioxide and methane. We found no other carbon substrates than matched sugars in biosynthetic energy, efficiency, and simplicity. From this, we concluded that sugars are the optimal biosynthetic substrate. Since this conclusion is based on universal properties of carbon chemistry, other carbon-based life throughout the Universe would also use optimal sugar substrates. Furthermore, this rather obvious universal role of sugars as the optimal biosubstrate would probably be common knowledge of technological civilizations throughout the Universe. Since the elemental building block of all sugars is formaldehyde, the common knowledge that sugars are the universal optimal biosubstrate could reasonably lead to the selection of a line(s) in the microwave spectrum of formaldehyde as a frequency for interstellar communication.

Weber, Arthur L.↗

Improving the Optical Quality Factor of the WGM Resonator

Resonators usually are characterized with two partially dependent values: finesse (F) and quality factor (Q). The finesse of an empty Fabry-Perot (FP) resonator is defined solely by the quality of its mirrors and is calculated as F=piR(exp 1/2)/(1-R). The maximum up-to-date value of reflectivity R approximately equal to 1 - 1.6 x 10(exp -6) is achieved with dielectric mirrors. An FP resonator made with the mirrors has finesse F=1.9 x 10(exp 6). Further practical increase of the finesse of FP resonators is problematic because of the absorption and the scattering of light in the mirror material through fundamental limit on the reflection losses given by the internal material losses and by thermodynamic density fluctuations on the order of parts in 109. The quality factor of a resonator depends on both its finesse and its geometrical size. A one-dimensional FP resonator has Q=2 F L/lambda, where L is the distance between the mirrors and lambda is the wavelength. It is easy to see that the quality factor of the resonator is unlimited because L is unlimited. F and Q are equally important. In some cases, finesse is technically more valuable than the quality factor. For instance, buildup of the optical power inside the resonator, as well as the Purcell factor, is proportional to finesse. Sometimes, however, the quality factor is more valuable. For example, inverse threshold power of intracavity hyperparametric oscillation is proportional to Q(exp 2) and efficiency of parametric frequency mixing is proportional to Q(exp 3). Therefore, it is important to know both the maximally achievable finesse and quality factor values of a resonator. Whispering gallery mode (WGM) resonators are capable of achieving larger finesse compared to FP resonators. For instance, fused silica resonators with finesse 2.3 x 10(exp 6) and 2.8 x 10(exp 6) have been demonstrated. Crystalline WGM resonators reveal even larger finesse values, F=6.3 x 10(exp 6), because of low attenuation of light in the transparent optical crystals. The larger values of F and Q result in the enhancement of various nonlinear processes. Low-threshold Raman lasing, optomechanical oscillations, frequency doubling, and hyperparametric oscillations based on these resonators have been recently demonstrated. Theory predicts a possibility of nearly 10(exp 14) room-temperature optical Q-factors of optical crystalline WGM resonators, which correspond to finesse levels higher than 10(exp 9). Experiments have shown numbers a thousand times lower than that. The difference occurs due to media imperfections. To substantially reduce the optical losses caused by the imperfections, a specific, multi-step, asymptotic processing of the resonator is implemented. The technique has been initially developed to reduce microwave absorption in dielectric resonators. One step of the process consists of mechanical polishing performed after high temperature annealing. Several steps repeat one after another to lead to significant reduction in optical attenuation and, as a result, to the increase of Q-factor as well as finesse of the resonator which demonstrates a CaF2 WGM resonator with F greater than 10(exp 7) and Q greater than 10(exp 11).

Savchenkov, Anatoliy↗

Systems and Methods for Peak-Seeking Control

A computerized system and method for peak-seeking-control that uses a unique Kalman filter design to optimize a control loop, in real time, to either maximize or minimize a performance function of a physical object ("plant"). The system and method achieves more accurate and efficient peak-seeking-control by using a time-varying Kalman filter to estimate both the performance function gradient (slope) and Hessian (curvature) based on direct position measurements of the plant, and does not rely upon modeling the plant response to persistent excitation. The system and method can be naturally applied in various applications in which plant performance functions have multiple independent parameters, and it does not depend upon frequency separation to distinguish between system dimensions.

Ryan, John J↗

Impacts of Ionizing Radiation on Sulfur Chloride Compounds for Advanced Fuel Cladding Decontamination Processes

Tripling the nation’s nuclear energy capacity is a critical component for significantly increasing energy production and reducing energy costs for American families and businesses. Achieving this vision requires fuel cycle technologies that maximize resource utilization while minimizing radioactive waste generation. Advanced sulfur chloride–based chlorination technologies are being developed to enable efficient recycling of fuel cladding materials, which account for a significant fraction of used nuclear fuel. However, the impacts of ionizing radiation on the longevity and performance of these sulfur chloride compounds are not well established. Here, we will explore the effects of gamma and electron-beam irradiation on the chemical composition of select sulfur chloride reagents, specifically sulfur monochloride (S2Cl2) and thionyl chloride (SOCl2), and the impacts of pre-irradiation on the chlorination yield/chemical dissolution of surrogate aluminum alloy 6061 (AA6061-T6) materials.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

Simultaneous Waste Heat and Water Recovery from Power Plant Flue Gases for Advanced Energy Systems

This final report presents the results of a two-year technology development project carried out by a team of participants sponsored by the Department of Energy (DOE). The objective of this project is to develop a membrane-based technology to recover both water and low grade heat from power plant flue gases. Part of the recovered high-purity water and energy can be used directly to replace plant boiler makeup water as well as improving its efficiency, and the remaining part of the recovered water can be used for Flue Gas Desulfurization (FGD), cooling tower water makeup or other plant uses. This advanced version Transport Membrane Condenser (TMC) with lower capital and operating costs can be applied to existing plants economically and can maximize waste heat and water recovery from future Advanced Energy System flue gases with CO 2 capture in consideration, which will have higher moisture content that favors the TMC to achieve higher efficiency.

20 FOSSIL-FUELED POWER PLANTS↗

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

Uncrewed Lunar Surface Operations and Support Activities

Sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The time for surface crews to perform tasks will be limited. Extravehicular activities by surface personnel are burdensome and time-consuming, even when a continuous human presence on the surface occurs. Identifying and balancing human/automation roles and tasks and infusing automation and autonomy practices early in a system’s lifecycle will be essential to achieve mission objectives. Among these objectives are attaining a sustained human presence, improving performance and mission effectiveness, reducing operations and maintenance (O&M) costs, and ensuring operations that are robust to communication delays. To achieve these objectives, an operational shift toward increased automation and autonomy with less reliance on humans is needed. Uncrewed lunar surface operations and support activities occur when surface crews are not present or are independent of surface crew timeline activities requiring no surface crew oversight or intervention. These uncrewed surface opportunities can also be planned to minimize crew workload that avoids routine maintenance and support tasks, thus maximizing crew exploration time. Uncrewed preparations such as staging and prepositioning equipment and materials before the crew arrives could improve crew task efficiency. Additional opportunities exist to conduct uncrewed science, exploration, and utilization. Uncrewed surface architecture functions can include science and exploration; habitation; launch and landing support; surface communication and navigation; surface power generation and distribution; human surface mobility; lifting, handling, manipulating; excavation, construction, and site preparation; logistics management; maintenance and repair; surface resource utilization; integrated site operations and shared support services (e.g., site scheduling/prioritization, dust mitigation/contamination control, and surface safety). Early robotic lunar surface campaigns will provide information on the availability of resources, such as oxygen and water, and demonstrate surface-based technologies. After the Artemis III human lunar return mission, a series of landers will deliver surface systems, cargo, supplies, science packages, spare parts, and commodities. A balance of crewed and uncrewed surface operations will enable a sustained lunar surface presence at the South Pole of the Moon at a site that will be known as the Artemis Base Camp (ABC). It is envisioned that base camp operations on and around the Moon will then help prepare for the mission durations and activities needed to support the first human mission to Mars. Before long-duration crew missions to the base camp can occur, the necessary surface infrastructure will be pre-deployed and verified operational. Surface assets will be teleoperated and remotely managed from Earth. Additionally, robotic and short-duration crewed missions to the ABC will ensure the site’s merit to achieve long-term science objectives, availability of usable resources, and that terrain, seasonal variations, and illumination conditions are acceptable. ABC will consist of different areas where specific functions and services are rendered, including: • Launch and Landing Area • Habitation Area • Power Production Area • Resource Areas Launch and Landing Area—The launch and landing area will support associated functions for the arrival and departure of vehicles, such as crewed landing and ascent and uncrewed cargo deliveries and offloading. It will evolve from an unimproved site at the beginning of the exploration campaign to a more sustainable landing and launch area that can support repeated arrivals and departures. Initial uncrewed Lunar Terrain Vehicle (LTV) surface operations may include emplacement of navigation beacons and communication equipment, real-time video and photography of landing/liftoff events, and element repositioning, such as portable utility power (PUP) (applicable for other landed assets at other areas). Site preparations, such as surface leveling, soil compaction, and berm/path construction, may be needed for a more sustainable launch and landing area capable of accommodating vehicles that are increasingly more reusable and reduce the effects of plume surface interactions and ejecta impacts on nearby surface assets. During the ABC missions, cargo and logistics will be delivered to the lunar surface via robotic cargo landers before the crew arrives. These shipments, which can arrive in pressurized logistics carriers, will deliver the logistics necessary to support a crewed mission and include items such as food, water, equipment spares, etc. Providing the capability to retrieve, offload, and transport the logistics closer to the ABC site before the arrival of the crew will increase the overall efficiency of crew operations once they arrive. In the sustained phase of exploration, other supporting services may be needed, such as lander propellant servicing, surface power services, commodity refreshes, and additional inspection, maintenance, and repair capabilities, to sustain a cadence of extended personnel stays and cargo arrivals and departures. Habitation Area—Uncrewed support to surface habitation could involve supporting activation and pre-entry operations of the habitat while the crew is in orbit at the Gateway outpost preparing for a surface landing. Surface Habitat (SH) uncrewed operations may include bringing the cabin environment to a habitable temperature and air mix and activating other critical crew support systems. Potential crop production uncrewed tasks in the SH could also include autonomous watering and tending. Additionally, when the crew departs, the SH enters dormancy for the long period of uncrewed operation. A logistical staging area could also be collocated near the SH. If so, staging operations for crew supplies, waste re-location, and recycling operations may be opportunities for uncrewed operations. Power Production Area—The Fission Surface Power (FSP) element and its supporting distribution equipment provide power to surface elements as needed across the ABC to supplement day-to-day operations and survive lunar nights. Uncrewed support of this power system includes any initial LTV-assisted deployments of cables and other distributed equipment, associated electrical connections, and system testing and activation operations. Robotically performing some inspections, maintenance, or repair tasks on the power distribution equipment could reduce the surface crew workload. Resource Area— Uncrewed resource prospecting, mapping, and characterizing possible resource sites is likely to be time-consuming and represents an opportunity for uncrewed operations between crewed missions. Uncrewed mobile equipment operations will be needed in the extreme environments of permanently shadowed locations where resource extractions occur. As In-Situ Resource Utilization (ISRU) pilot plant operations begin, uncrewed surface support activities with available mobile and portable assets (LTV, PUP, etc.) will better support these operations. Any produced commodities can be stored at a centralized storage location for future use. Also associated with these operations is the use of mobile robotic excavators for resource acquisition and robotic/autonomous regolith processing. The waste tailings generated during excavation and regolith processing would also need to be transported and deposited at a dedicated location. Surface assets will continue operating between crew visits to maintain surface capabilities, conduct lunar surface science, technology demonstrations, and public outreach opportunities. Additionally, certain sustaining tasks that would consume valuable crew time could be performed before crew arrival, or after their departure. This capability may offer more affordable options to construct, activate, test, and maintain a broad set of surface assets. Telerobotically operated human surface mobility systems, such as the LTV and Pressurized Rover (PR), can be utilized for various tasks. Surface environmental conditions pose a distinct challenge for all these activities. Surface illumination and localized shadows are one such factor. Night-survival operations could consist of thermal management, battery pre-charging, and load shedding. Some surface systems may hibernate through the night and then awake and continue nominal operations. Uncrewed mobile assets may use a more adaptive approach to optimize their power and operations; one method is to follow the sunlight. Night-survival operations may be initiated remotely by teleoperation, automated, or accomplished by supervised autonomous operation. The ability to pre-deploy and control remote assets in orbit or on Mars before the arrival of the mission crew is a key capability that can be simulated on the moon. The base camp provides a venue where these advanced operational concepts, technologies, and autonomous methods and techniques, including the incorporation of time delays to simulate Earth-Mars latency can be replicated to help buy down future Mars mission risks. This paper will examine the evolution of uncrewed lunar surface operations and support activities. It will also discuss the lunar surface environmental conditions (thermal, lighting, terrain, topography, communications) along with the challenges they pose on uncrewed surface operations, and the performance of these activities with limited to minimal human interaction and/or teleoperation. Since lunar missions include Mars mission analogs, such investigation provides the framework for future uncrewed Mars mission support.

Mark E Lewis↗

Efficient algorithms and implementations of entropy-based moment closures for rarefied gases

We present efficient algorithms and implementations of the 35-moment system equipped with the maximum-entropy closure in the context of rarefied gases. While closures based on the principle of entropy maximization have been shown to yield very promising results for moderately rarefied gas flows, the computational cost of these closures is in general much higher than for closure theories with explicit closed-form expressions of the closing fluxes, such as Grad's classical closure. Following a similar approach as Garrett et al. (2015) , we investigate efficient implementations of the computationally expensive numerical quadrature method used for the moment evaluations of the maximum-entropy distribution by exploiting its inherent fine-grained parallelism with the parallelism offered by multi-core processors and graphics cards. We show that using a single graphics card as an accelerator allows speed-ups of two orders of magnitude when compared to a serial CPU implementation. To accelerate the time-to-solution for steady-state problems, we propose a new semi-implicit time discretization scheme. The resulting nonlinear system of equations is solved with a Newton type method in the Lagrange multipliers of the dual optimization problem in order to reduce the computational cost. Additionally, fully explicit time-stepping schemes of first and second order accuracy are presented. We investigate the accuracy and efficiency of the numerical schemes for several numerical test cases, including a steady-state shock-structure problem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Memory-efficient decoding of LDPC codes

We present a low-complexity quantization scheme for the implementation of regular (3,6) LDPC codes. The quantization parameters are optimized to maximize the mutual information between the source and the quantized messages. Using this non-uniform quantized belief propagation algorithm, we have simulated that an optimized 3-bit quantizer operates with 0.2dB implementation loss relative to a floating point decoder, and an optimized 4-bit quantizer operates less than 0.1dB quantization loss.

Low-Density-Parity-Check (LDPC)↗

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.

36 MATERIALS SCIENCE↗

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗

15%-efficiency (Mg,Zn)CdTe solar cells with 1.7 eV bandgap for tandem applications

The overall objective of this project was to demonstrate a 15%-efficient Cd(Mg,Zn)Te solar cell with a bandgap of 1.7–1.9 eV. Such a cell would be a critical first step towards market-competitive flat-plate modules with efficiencies significantly higher than is achievable with single-junction silicon or thin-film modules. In particular, Cd(Mg,Zn)Te may be the optimum top cell for pairing with silicon in a tandem. It promises low cost and long-term stability not available with other top-cell candidates, and, while 1.5 eV polycrystalline CdTe cells have already achieved 22.1% efficiency, no serious efforts have been undertaken to demonstrate an efficient 1.7–1.9 eV Cd(Mg,Zn)Te cell. This project was the first such effort. To reach 15% efficiency, this project specifically aimed to develop (1) high-quality Cd(Mg,Zn)Te absorbers that minimize non-radiative recombination and thereby maximize the quasi-fermi level splitting, and (2) effective electron and hole (heterojunction) contacts that allow the full quasi-Fermi-level splitting (chemical potential) to be extracted at the metal electrodes (electrical potential). To this end, 1.7–1.9 eV Cd(Mg,Zn)Te absorber layers and heterojunction contacts were developed in parallel using molecular beam epitaxy (MBE) and close-space sublimation (CSS) growth; the former defines the upper bounds of performance and provides insight into material and device limitations, whereas the latter ensures that technology developed within the project is readily transferable to industry. Minority-carrier lifetime served as the primary metric for the first aim; open-circuit voltage (V oc ) and efficiency served as metrics for the second.

Holman, Zachary↗

Selective collection of iron-rich dust particles by natural Trichodesmium colonies

Abstract Dust is an important iron (Fe) source to the ocean, but its utilization by phytoplankton is constrained by rapid sinking and slow dissolution dust-bound iron (dust-Fe). Colonies of the globally important cyanobacterium, Trichodesmium, overcome these constraints by efficient dust capturing and active dust-Fe dissolution. In this study we examined the ability of Trichodesmium colonies to maximize their Fe supply from dust by selectively collecting Fe-rich particles. Testing for selectivity in particle collection, we supplied ~600 individual colonies, collected on multiple days from the Gulf of Aqaba, with natural dust and silica minerals that were either cleaned of or coated with Fe. Using a stereoscope, we counted the number of particles retained by each colony shortly after addition and following 24 h incubation with particles, and documented translocation of particles to the colony core. We observed a strong preference for Fe-rich particles over Fe-free particles in all tested parameters. Moreover, some colonies discarded the Fe-free particles they initially collected. The preferred collection of Fe-rich particles and disposal of Fe-free particles suggest that Trichodesmium can sense Fe and selectively choose Fe-rich dust particles. This ability assists Trichodesmium obtain Fe from dust and facilitate its growth and subsequent contribution to nutrient cycling and productivity in the ocean.

54 ENVIRONMENTAL SCIENCES↗

Adapting In Situ Accelerators for Sparsity With Granular Matrix Reordering

Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this paper proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Finally, GMR achieves an average of 28% and up to 34% reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.

97 MATHEMATICS AND COMPUTING↗

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗