Degradation Pathway Modeling of PV Minimodule Variants with Different Packaging Materials Under Indoor Accelerated Exposures.
Abstract not provided.
Venkat, Sameera Nalin↗
SEARCH · Engineering Papers
Results for “accelerator modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Accelerating Informed Local Climate Action by Improving Access to DOE Predictive Modeling and Science-Based Tools
The threat of climate change is becoming increasingly urgent, and local governments across the United States are working to develop and implement climate action plans to reduce greenhouse gas emissions and mitigate the impacts of climate change. At the same time, the U.S. Department of Energy's Office of Science, Earth and Environmental Systems Sciences Division (EESSD) has been developing a range of tools and technologies to help predict the potential multi-faceted impacts from climate change given varying environmental and socioeconomic scenarios. However, local governments have been slow to engage with these scientific assets due to a lack of understanding and awareness. The DOE researchers interviewed highlighted three primary types of resources that could benefit local governments and climate practitioners: climate models, modeled datasets, and scientific journal articles. Although many of these resources are open source and available to the public, there were several challenges identified in translating these resources and their research insights directly to decision-makers.
54 ENVIRONMENTAL SCIENCES↗
Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets
Packed columns are commonly used in post-combustion processes to capture CO 2 emissions by providing enhanced contact area between a CO 2 -laden gas and CO 2 -absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid–gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO 2 -capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost. In contrast, data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. We build our surrogates using MeshGraphNets (MGN), a graph neural network framework that efficiently learns and produces mesh-based simulations. We apply MGN to a random packed column modeled with over 160K graph nodes and a design space consisting of three key input parameters: solvent surface tension, inlet velocity, and contact angle. Our models can adapt to a wide range of these parameters and accurately predict the complex interactions within the system at rates over 1700 times faster than CFD, affirming its practicality in downstream design optimization tasks. This underscores the robustness and versatility of MGN in modeling complex fluid dynamics for large-scale CCS analyses.
97 MATHEMATICS AND COMPUTING↗
Accelerating phase-field based predictions: A case study with surrogate models trained using machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
A Roadmap for the Future of Systems Biology in Cancer Research
Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.
Modeling & Simulation↗
Progress in relativistic laser–plasma interaction with kilotesla-level applied magnetic fields
We report on progress in the understanding of the effects of kilotesla-level applied magnetic fields on relativistic laser–plasma interactions. Ongoing advances in magnetic-field–generation techniques enable new and highly desirable phenomena, including magnetic-field–amplification platforms with reversible sign, focusing ion acceleration, and bulk-relativistic plasma heating. Building on recent advancements in laser–plasma interactions with applied magnetic fields, we introduce simple models for evaluating the effects of applied magnetic fields in magnetic-field amplification, sheath-based ion acceleration, and direct laser acceleration. These models indicate the feasibility of observing beneficial magnetic-field effects under experimentally relevant conditions and offer a starting point for future experimental design.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.
Hawks, Benjamin [Fermilab] (ORCID:0000000157000288↗
LW3D v1
LW3D is code for modeling beam dynamics in particle accelerators including Lienard-Wiechert self-fields. It is similar to codes like IMPACT-T except that the Poisson solver is replaced by a Lienard-Wiechert solver. Along with space-charge effects, the code can model effects like coherent synchrotron radiation.
Ryne, Robert↗
Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework
The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.
97 MATHEMATICS AND COMPUTING↗
Timing of pulsars in the globular cluster omega centauri
We present the timing of the first five millisecond pulsars discovered in the globular cluster Omega Centauri and the discovery of a pulsar with a spin period of 3.68 ms. With a timing baseline of ~3.5 yr we are able to measure the derivative of the spin frequency ($\dot{\nu }$) for the first five pulsars. Upper limits on the pulsar line-of-sight acceleration are estimated and compared with predictions based on analytical models of the cluster. We find that PSRs J1326-4728B and D show large negative accelerations, which are in tension with the minimum acceleration predicted by analytical models. We searched for pulsed γ-ray signals using 14.3 yr of data from the Fermi Large Area Telescope. Although we found no evidence for γ-ray pulsations, PSRs J1326-4728A, B, C, and E are associated with X-ray sources. This suggests that the observed γ-ray emission from Omega Centauri is likely caused by the emission of the ensemble of MSPs. Finally, the linearly polarized emission from PSR J1326-4728A yields a rotation measure of -18 ± 8 rad m -2 .
79 ASTRONOMY AND ASTROPHYSICS↗
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
There is a growing interest in custom spatial accelerators for machine learning applications. These accelerators employ a spatial array of processing elements (PEs) interacting via custom buffer hierarchies and networks-on-chip. The efficiency of these accelerators comes from employing optimized dataflow (i.e., spatial/temporal partitioning of data across the PEs and fine-grained scheduling) strategies to optimize data reuse. The focus of this work is to evaluate these accelerator architectures using a tiled general matrix-matrix multiplication (GEMM) kernel. To do so, we develop a framework that finds optimized mappings (dataflow and tile sizes) for a tiled GEMM for a given spatial accelerator and workload combination, leveraging an analytical cost model for runtime and energy. Our evaluations over five spatial accelerators demonstrate that the tiled GEMM mappings systematically generated by our framework achieve high performance on various GEMM workloads and accelerators.
43 PARTICLE ACCELERATORS↗
Scalar flux transport models for self-similar turbulent mixing
A common approach to closing turbulent species flux in multicomponent Reynolds-averaged Navier-Stokes models is to use the standard gradient diffusion approximation. While such an approach has been shown to work well when applied to many canonical turbulent mixing configurations, a gradient diffusion approach is fundamentally limited in its ability to capture complex phenomena such as countergradient transport. For this reason, complicated mixing applications may benefit by treating the turbulent diffusivity with a model transport equation in a manner analogous to second-moment momentum closure in Reynolds-stress transport models. Here, the present work explores the development and application of two different scalar flux transport (SFT) models. Self-similarity constraints are derived for these models, and they are evaluated against gradient-diffusion-based models in several one- and two-dimensional problems of turbulent mixing. It is found that the new SFT models out-perform gradient diffusion models in problems involving rapid acceleration reversal and in problems involving anisotropic transport of materials. In addition, it is found that even a hybrid-SFT approach, in which an SFT equation is utilized along with a gradient diffusion closure, provides some measure of improvement over models that transport the mass flux rather than the scalar flux.
Reynolds-averaged Navier Stokes↗
Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference
We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.
54 ENVIRONMENTAL SCIENCES↗
Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management
Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.
Bhuiyan, Tanveer H.↗
A preference for cold dark matter over Superfluid Dark Matter in local Milky Way data
There are many well-known correlations between dark matter and baryons that exist on galactic scales. These correlations can essentially be encompassed by a simple scaling relation between observed and baryonic accelerations, historically known as the Mass Discrepancy Acceleration Relation (MDAR). The existence of such a relation has prompted many theories that attempt to explain the correlations by invoking additional fundamental forces on baryons. The standard lore has been that a theory that reduces to the MDAR on galaxy scales but behaves like cold dark matter (CDM) on larger scales provides an excellent fit to data, since CDM is desirable on scales of clusters and above. However, this statement should be revised in light of recent results showing that a fundamental force that reproduces the MDAR is challenged by local Milky Way dynamics and rotation curve data between 5–18 kpc. In this study, we test this claim on the example of Superfluid Dark Matter. We find that a standard CDM model is preferred over a static superfluid profile assuming a steady-state Galactic disk and discuss the robustness of this conclusion to disequilibrium effects. This preference is due to the fact that the superfluid model over-predicts vertical accelerations, even while reproducing galactic rotation curves. Our results establish an important criterion that any dark matter model must satisfy within the Milky Way.
79 ASTRONOMY AND ASTROPHYSICS↗
Double Aperture HTS Dipole Magnet Model for the Persistent Current Operation
Particle accelerators use many different types of magnets. There is an interest in replacing rather low-field conventional resistive magnets with high-temperature superconducting (HTS) magnets. The main goal is to reduce operational expenses, which is critical during the continuously increasing cost of electricity. At Fermilab, several HTS accelerator magnets were built and successfully tested. The paper described a variant of the double-aperture dipole magnet that could be suitable for future Lepton Colliders. A single short-circuited HTS coil of 3.8 kA generates a homogeneous magnetic field of 57 mT in both magnet gaps of 84 mm. The short model magnet was designed, built, and successfully tested at liquid nitrogen temperature. This magnet works in a persistent current mode, which provides additional cost savings when used in particle storage rings operated at a stable magnetic field. The results of design, fabrication, and tests are presented and discussed. The magnet demonstrated a high magnetic field stability in both apertures, efficiency of correction coils, and primary powering circuit.
Kashikhin, Vladimir [Fermilab] (ORCID:000000017249↗
BLAST aka: BLAST-Py see also BLAST-Lite (Battery Lifetime Analysis and Simulation Tool Suite - Python) [SWR-22-69]
Battery Lifetime Analysis and Simulation Tool Suite (BLAST or BLAST-Py) developed in the Python programming language. BLAST-Py predicts the evolution of lithium-ion battery performance metrics over their lifetime, using models trained on lab-based accelerated aging data to predict battery performance in dynamic, real-world use. BLAST-Py contains existing models for a variety of lithium-ion battery chemistries (NMC/Gr and LFP/Gr). See also the open-source version of this software tool known as "BLAST-Lite" at: https://github.com/NREL/BLAST-Lite
Smith, Kandler↗