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NOODLES Grid [SWR-25-93]
NOODLES Grid is a real-time visualization server for power system simulation data. It loads precomputed datasets, builds optimized instance renderings, and streams live interactive scenes to connected clients. It is built for high scalability, flexible visualization, and fast interaction. NOODLES is a cross-platform/device/tool protocol for collaborative visualization. NOODLES was Developed at the National Renewable Energy Laboratory (NREL) as a capability of the Insight Center https://www.nrel.gov/computational-science/insight-center.html
Berkeley eXtensible Environment (BXE) v3
The Berkeley eXtensible Environment (BXE) provides a cloud environment for hardware designers and computer architects to design, build, and simulate their custom architectures on an on-premises FPGA cluster. Utilizing the Chipyard, MoSAIC, and FireSim frameworks, users are provided an environment where they can assemble SoC designs from an existing library of components or import their own source code. Once their designs are ready, they can utilize the FireSim framework provided by BXE to deploy and simulate their designs on the FPGA. Users aren't limited to a single FPGA; they can deploy multiple instances across multiple FPGAs, acting like a rack of servers, or partition their large design across multiple FPGAs, ganging multiple FPGAs into a single simulated system.
The "Fake" Supernova Neutrinos of SBND: Using Muons Decaying at Rest to Study the Charged Current MeV-Scale $V_e$-Ar Cross Section
In a Type-II core collapse supernova, 99% of the total amount of energy is released in the form of neutrinos. However, the nearest supernova explosion in the last few hundred years was in 1987, over thirty years ago. It was also the first from which particle detectors around the world managed to detect the neutrino flux, yielding important information about their properties and the explosion mechanism. Neutrinos are the most weakly interacting subatomic fundamental particles known to date, and they act as a cooling mechanism for the star. Out of the six neutrino flavours currently known, the majority exiting the explosion are electron neutrinos, ve. Neutrino experiments using liquid argon (LAr) as their detector medium are unique at probing this specific channel. In the meantime, however, until the next explosion, we can still practise for the big event. With the Short Baseline Near Detector (SBND) experiment at Fermilab, we can study how electron neutrinos of the same energy coming from the Booster Neutrino Beam (BNB) interact in LAr, and measure the cross section. As BNB measurements are mostly tailored to higher energy neutrinos, I will present an overview of how SBND is aiming to select the low-energy neutrino candidates from muons Decaying At Rest (DAR) in the absorber and target within the BNB beam pipe. This is not a straightforward feat, encompassing special difficulties that include beam and flux systematic uncertainty simulation, system trigger needs and particular reconstruction efforts, and so I will present how each challenge is being addressed in SBND. This will be a novel measurement in the neutrino particle physics community, and will prove fundamental for the next generation of supernova neutrino detectors.
Quantum Computing Algorithms and Applications for Coherent and Strongly Correlated Chemical Systems
This project advanced quantum algorithms, quantum information theory, strongly correlated electronic structure methods, molecular quantum materials, and exciton transport imaging in coherent condensed phase systems. Across the award period, the team developed new methods for open-quantum-system simulation, Hamiltonian learning, state tomography, reduced-density-matrix and contracted-quantum eigensolver approaches, and quantum diagnostics for device capability and openness.
3D Cloud Aerosol Precipitation Experiment at kennaook Cape Grim (3D-CAPE-k) Field Campaign Report
The objective of this campaign was to complement the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Cloud And Precipitation Experiment at kennaook (CAPE-k) in northwest Tasmania by deploying scanning, fixed pointing angle, and in situ aerosol, cloud, and precipitation remote-sensing instruments during the last six months of the experiment. These instruments provided a three-dimensional (3D) context to the second ARM Mobile Facility (AMF2) vertically pointing observations collected at the kennaook Cape Grim (KCG) site and captured a portion of the life cycle of the clouds before and after the passage over the AMF2 vertical column. These 3D cloud measurements will be used in the near future to 1) investigate how the vertical profiles of aerosol and cloud properties are representative of the broader area and how these properties evolve during the portion of the cloud life cycle captured by the scanning instruments, 2) quantify how accurate the cloud fraction derived with assumptions from vertically pointing observations are compared with direct 3D cloud fraction measurements, 3) evaluate cloud fraction and liquid water path in the Australian Community Climate and Earth-System Simulator–Convective-scale (ACCESS-C) forecast model, and 4) evaluate aerosol and cloud products from the European Space Agency/Japanese Aerospace Exploration Agency (ESA/JAXA) Earth Cloud Aerosol Radiation Explorer (EarthCARE) mission using the scanning measurements, offering more chances of exact collocation, thereby complementing the statistical approach we are planning to employ with AMF2 observations for that same purpose.
Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems
Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data.
Charting the Path to Electrification: Analyzing the Economic and Technological Potential of Advanced Vehicle Powertrains
The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals.
Decoherence Noise on the Superconducting Qubits Training Program
Quantum computing is a growing field with promising applications in a variety of fields such as healthcare, energy consumption, and cryptography. Quantum computing leverages the principles of quantum mechanics - superposition and entanglement. Yet, in the Noisy Intermediate Scale Quantum (NISQ) Era - quantum systems face the major challenge of decoherence due to noise. This era is characterized by low amounts of qubits and high gate error. Decoherence leads to the loss of the quantum information stored in the qubit. Noise occurs with any quantum system that is exposed to the environment. It should also be noted that quantum information can be stored in the cavity - Fermilab specializes in coupling transmons to ultrahigh-Q SRF cavities. The Superconducting Qubits Training Program (SQTP) provides a visualization for beginners in quantum computing. The open quantum system simulated is a superconducting qubit (two-level atom) coupled to a microwave cavity whose excitations are photons. The Rotating Wave Approximation of the Jaynes-Cumming Hamiltonian is used. SQTP utilizes open-source Python-based libraries scQubits, NumPy, and QuTiP alongside the Master Lindblad equation. In this project, we study the different decay behaviors of qubits and cavities with collapse operators.
A comprehensive framework for validating simulation models of power system equipment using terminal measurements
Accurate simulation of power-plants is essential to the planning and operation of modern power grids. The current methods used to periodically check power-plant simulation models have many open questions about their limitations and accuracy. The research in this project explored using Monte-Carlo Experimentation (MCE) as a means for answering these important questions.
Coupled Lindblad Pseudomode Theory for Simulating Open Quantum Systems
Coupled Lindblad pseudomode theory is a promising approach for simulating non-Markovian quantum dynamics on both classical and quantum platforms, with dynamics that can be realized as a quantum channel. We provide theoretical evidence that the number of coupled pseudomodes only needs to scale as polylog(𝑇/𝜖) in the simulation time 𝑇 and precision 𝜖. Inspired by the realization problem in control theory, we also develop a robust numerical algorithm for constructing the coupled modes that avoid the nonconvex optimization required by existing approaches. We demonstrate the effectiveness of our method by computing population dynamics and absorption spectra for the spin-boson model. Furthermore, this Letter provides a significant theoretical and computational improvement to the coupled Lindblad framework, which impacts a broad range of applications from classical simulations of quantum impurity problems to quantum simulations on near-term quantum platforms.
Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications
A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.
CHARMM-GUI Bicelle Builder : An Extension of Membrane Builder for Modeling and Simulation of Bicelle Systems
Membrane mimetics, such as detergent micelles, nanodiscs, and amphipol complexes, which can provide membrane-like environments while retaining small and soluble features, have been utilized to study membrane proteins. A bicelle, composed of varying lipids and detergents, is a useful membrane mimetic because the lipid-to-detergent ratio, the q-value, can be adjusted to alter the properties of the aggregate, including the thickness and size of the bicelle. However, building a bicelle model for modeling and simulation studies requires nontrivial efforts, even for experts. We introduce CHARMM-GUI Bicelle Builder, a web-based platform that can generate various all-atom bicelle systems via a graphical user interface with all available lipids and detergents in Membrane Builder. To illustrate and validate Bicelle Builder with practical systems, we have modeled and simulated pure bicelles consisting of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids with 1,2-dihexanoyl-sn-glycero-3-phosphocholine (C6DHPC) detergents and protein–bicelle complexes, composed of DMPC with C6DHPC, foscholine-10 (FOS10), and lysophosphatidylcholine-12 (LPC12) detergents. Our simulation results indicate that Bicelle Builder can generate reliable and robust bicelle models with and without proteins that retain DMPC bilayer characteristics. Bicelle Builder is expected to help researchers better understand not only bicelles themselves but also atomistic-level structures of protein–bicelle complexes that are often difficult to access through experimental approaches.
Scientific/Technical Report for Framework for Modeling and Simulation of Security Systems in Nuclear Facilities
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Simulating large one-dimensional neutral-atom quantum systems
While abstract models of quantum computation assume a closed system of two-level states, practical quantum devices inevitably couple to the environment in some way, creating sources of noise. Understanding the tolerance to noise of specific quantum algorithms run on specific devices is important for determining the feasibility of quantum computing in the current noisy intermediate-scale quantum era. Of particular interest is understanding the noise sensitivity of these devices as more qubits are added to the system. Classical simulations are a useful tool to understand the effects of this noise, but direct classical simulations of open quantum systems are burdened by an exponentially growing cost in the number of qubits and a large local Hilbert space dimension. For onedimensional, shallow circuits, using tensor networks can replace this exponential cost with a linear one and simulate far wider systems than what would normally be available. In this paper, we describe a tensor network simulation of a neutral atom quantum system under the presence of noise, while introducing a purity-preserving truncation technique that compromises between the simplicity of the matrix product state and the positivity of the matrix product density operator. We apply this simulation to a near-optimized iteration of the quantum approximate optimization algorithm on a transverse field Ising model in order to investigate the influence of large system sizes on the performance of the algorithm. We find that while circuits with a large number of qubits fail more often under noise that depletes the qubit population, their outputs on a successful measurement are just as robust under Rydberg atom dissipation or qubit dephasing as smaller systems. However, such circuits might not perform as well under coherent multiqubit errors such as Rydberg atom crosstalk. We also find that the optimized parameters are especially robust to noise, suggesting that a noisier quantum system can be used to find the optimal parameters before switching to a cleaner system for measurements of observables.
Direct statistical simulation of the Lorenz96 system in model reduction approaches
Direct statistical simulation (DSS) of nonlinear dynamical systems bypasses the traditional route of accumulating statistics by lengthy direct numerical simulations by solving the equations that govern the statistics themselves. DSS suffers, however, from the curse of dimensionality as the statistics (such as correlations) generally have higher dimensions than the underlying dynamical variables. Here we investigate two approaches to reduce the dimensionality of DSS, illustrating each method with numerical experiments with the Lorenz96 dynamical system. The forms of DSS chosen here involve approximate closures at second and third order in the equal-time cumulants. We demonstrate significant reduction in computational effort that can be achieved without sacrificing the accuracy of DSS. The methods developed here can be applied to turbulent fluid and magnetohydrodynamical systems. Published by the American Physical Society 2025
Power generation-cooling water Nexus: Impacts of cooling water shortage on power system operation - a simulation case study in Illinois, U.S
Cooling water shortage, frequently attributed to drought and heat waves, poses a significant threat to the operations of thermoelectric power plants and further poses a challenge for the entire power system and environmental stakeholders. Recognizing the critical nexus between power generation and cooling water availability and the potential ability of power generations to adjust generation schedules during cooling water shortages, this paper introduces a security-constrained unit commitment and economic dispatch model considering water-energy nexus. In specific, the model is augmented with a unit-level cooling water requirement (CWR) model and multi-level cooling water availability (CWA) constraints. The unit-level CWR model quantifies the cooling water withdrawal per MWh of power generation, taking into account factors such as thermoelectric generation technologies, cooling system technologies, and environmental parameters. The multi-level CWA constraints incorporate pump-level, plant-level, watershed-level, and forced minimum power constraints, utilizing data derived from actual-based cooling water shortage scenarios. Using a simulation case study in Illinois, United States, this research examines the reliability, economic, and environmental implications of cooling water shortages on power system operations. The results show that Illinois may experience 10-15% daily load curtailment and severe congestion between certain regions from the east to central during cooling water shortages, while once-through and wet-tower units experience a 52% and 17% reduction in power generation. In conclusion, overall cooling water withdrawal decreases by 24-38% as severity intensifies.
Efficient simulation of open quantum systems coupled to a reservoir through multiple channels
It is challenging to simulate open quantum systems that are connected to a reservoir through multiple channels. For example, vibrations may induce fluctuations in both energy gaps and electronic couplings, which represent two independent channels of system–bath couplings. Systems of this kind are ubiquitous in the processes of excited state radiationless decay. Combined with density matrix renormalization group (DMRG) and matrix product states (MPS) methods, we develop an interaction-picture chain mapping strategy for vibrational reservoirs to simulate the dynamics of these open systems, resulting in time-dependent spatially local system–bath couplings in the chain-mapped Hamiltonian. This transformation causes the entanglement generated by the system–bath interactions to be restricted within a narrow frequency window of vibrational modes, enabling efficient DMRG/MPS dynamical simulations. We demonstrate the utility of this approach by simulating singlet fission dynamics using a generalized spin-boson Hamiltonian with both diagonal and off-diagonal system–bath couplings. This approach generalizes an earlier interaction-picture chain mapping scheme, allowing for efficient and exact simulation of systems with multi-channel system–bath couplings using matrix product states, which may further our understanding of nonlocal exciton–phonon couplings in exciton transport and the non-Condon effect in energy and electron transfer.