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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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SF-26-123 24-hour scaled power-flow analysis of a modified IEEE 123-bus distribution feeder using OpenDSS. The simulation runs 24 snapshot power-flow cases (one per hour) with independent hourly scaling profiles for base loads, added loads, generators, and battery storage, and writes per-hour CSV/Excel reports plus a 24-panel voltage-profile plot.
Distributed energy resources (DER) have become a popular solution to modern-day issues surrounding the efficiency and reliability of power generation, as well as climate change concerns. Energy centers are shifting towards incorporating smart inverters with embedded functionalities such as high voltage ride through (HVRT), low voltage ride through (LVRT), active and reactive power compensation. However, the integration of smart inverters leave DER systems highly vulnerable to cybersecurity threats. The distributed network protocol 3 (DNP3) is a common method of communication between grid-tied hardware. Despite its popularity, the level of security leaves all hardware connected to the grid at risk of severe cyber-attacks. Thus, it is important to study any potential cybersecurity threats towards grid-tied smart inverters to mitigate cybersecurity vulnerabilities and refine existing cyber-security protections. This report describes the proposed testbed design to study cybersecurity threats to smart inverters. The testbed utilizes a real-time simulation case in RSCAD that includes a grid-tied wind turbine (WT) topology featuring two back-to-back two-level voltage source converters (BTB,2L-VSCs) and a permanent magnet synchronous machine (PMSM). The simulated case runs within the NovaCor real time digital simulator (RTDS). This report focuses on the design and implementation of a single module of the GTNETx2 card as a distributed network protocol and the configuration of an IEEE 1518 DNP database file that includes input and output variables mapped to different connection points in the grid that transmit and receive discrete, analog, and binary signals on command. This allows realistic emulation of the communication between the smart inverter and the grid for cybersecurity studies.
Diatom silaffins direct silica biomineralization through heavily post-translationally modified repeat domains, yet how these modifications reshape the multirepeat conformational ensemble remains unknown. We report all-atom MD simulations of a 195-residue construct spanning repeats R1–R7 of Sil1p from Cylindrotheca fusiformis, carrying the full complement of native PTMs: phosphoserine (pSer), long-chain polyamines (LCPA), dimethyllysine (MLY), and trimethylhydroxylysine phosphate (TPL). We simulated three variants (Native, P1/singly deprotonated phosphate, and P2/doubly deprotonated phosphate) at two NaCl concentrations in triplicate for 500 ns each. All systems disorder from the AlphaFold 3 starting structure. Phosphate charge state, not ionic strength, is the dominant control of ensemble compaction and ion organization. Doubly deprotonated phosphate organizes an extensive Na + condensation shell (∼100 ions, 20% of box Na + ) and a heterogeneous bridging network that integrates both pSer and TPL phosphate groups. The resulting ensemble is compact with LCPA side chains exhibiting above-median solvent accessibility in 84% of simulation frames in P2 at 300 mM. This is higher than any other condition we simulated and indicates that polyamine groups are preferentially surface-presented in the most compact, ion-organized state. A charge-neutralization control confirms that this compact state is a structured intermediate maintained by the bridging network, not a simple collapsed globule. Here, this repeat-scale spatial organization is not captured by single-repeat peptide studies. Understanding the dynamics, mechanism, and spatial organization of PTM-rich silaffin at the repeat scale is a step closer to hierarchical biomimetic materials beyond simple silica morphologies.
Infrasonic signatures of meteoroid fragmentation are frequently ambiguous: do multiple arrivals signify a complex breakup or merely the distorting effects of a layered atmosphere? Resolving this ambiguity is critical for accurate energy estimates and source reconstruction. In this study, we address this challenge by analyzing a unique regional dataset of well-constrained meteoroid events observed by the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. We employ pseudo-differential parabolic equation (PPE) simulations to quantify how fine-scale gravity-wave structures in the stratosphere and lower thermosphere modify acoustic waveforms at ranges <300 km. Our modeling reveals that while fine-scale layering can stretch signals and generate diffuse oscillatory tails, it does not produce discrete, high-amplitude pulse splitting at ranges below ∼140 km. By applying these results to the rare multi-arrival event 20060305, we demonstrate that its distinct double arrival at 100 km range is inconsistent with atmospheric multipathing and provides definitive evidence of separate fragmentation episodes. These findings establish new diagnostic criteria for separating source physics from propagation artifacts, improving the reliability of infrasound as a monitoring tool for natural bolides, space debris re-entries, and catastrophic launch failures.
Physically gelled soft materials, driven by the self-assembly of low-molecular-mass gelators (LMGs), have emerged as a platform for designing advanced gels that exhibit reversible gelation and property tunability. Liquid crystal (LC) gels are of great interest due to their supramolecular orderings as gel hosts and their enhanced electro-optical properties. In this study, we demonstrate the physical gelation of a nematic LC driven by nanoplate self-assembly, expanding the concept of gelators from small molecules to nanoparticles. These nanoplates are functionalized with promesogenic ligands and form a fibrillar network in LCs with face-to-face interplate stacking, resembling LMGs. The critical gelation volume fraction in the tilt test is only 0.14 v %, comparable to values reported for LMGs. Rheological analyses confirm viscoelastic properties characteristic of gelation. In situ small-angle X-ray scattering (SAXS) characterizes the formation of nanoplate networks in the LC with decreasing temperature, wherein LC mesogens become trapped in pores. Molecular dynamics (MD) simulations reveal that the interaction between ligand-coated nanoplates and LC-forming mesogens induces a multidomain LC structure, increasing friction between LC domains and stabilizing the gel. This study establishes direct relationships among molecular interactions, nanostructures, and mechanical properties in physically gelled LCs. In conclusion, the findings inspire the future gelator design of both LMGs and nanoplates, with potential applicability in bioscaffold engineering and liquid crystalline nanocomposites.
The balance of hydrophobic and hydrophilic interactions underlies emergent phenomena in complex multicomponent chemical systems. Here, we show that a supposedly ‘non–interacting’ nonpolar phase can be used to competitively solvate amphiphilic molecules at an oil/aqueous interface. This solvation, as probed by surface specific nonlinear spectroscopy and simulations, results in a molecularly thin corrugated phase boundary featuring metastable assemblies that alter the hydrogen bonding networks of water and the apparent ‘hard/soft’ descriptors used to describe ionic interactions. We show that competitive solvation enhances amphiphile mobility, opening up otherwise energetically inaccessible complexes that transiently interact with aqueous phase ions. These transient species impact ensemble binding affinities and may represent the molecular agents responsible for aspects of ionic transport and function. In conclusion, the result of this work highlights how seemingly unrelated nonpolar interactions feedback onto aqueous phase chemical phenomena, providing a pathway to tune phase separation and self-assembly to access new reaction pathways using interfaces for a range of chemical and biological systems.
Architectural thermo-responsive dynamic windows offer an autonomous solution for solar heat regulation, thereby reducing building energy consumption. Previous work has emphasized the significance of thermo-responsive windows in hot climates due to their role in solar heat control and subsequent energy conservation; conversely, our study provides a different perspective. Through a global-scale analysis, we explore over 100 material samples and execute more than 2.8 million simulations across over two thousand global locations. World heatmap results, derived from well-trained artificial neural network models, reveal that thermo-responsive windows are especially useful in climates where buildings demand both heating and cooling energy, whereas thermo-responsive windows with optimal transition temperatures show no dynamic features in most of low-latitude tropical regions. Additionally, this study provides a practical guideline and an open-source mapping tool to optimize the intrinsic properties of thermo-responsive materials and evaluate their energy performance for sustainable buildings at various geographical scales.
Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.
We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.
Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local quantum field theories (QFTs) [1]. In the infinite neuron number limit, single-layer NNs can exactly reproduce QFT results. This paper examines the viability of this architecture for perturbative calculations of local QFTs for finite neuron number N using scalar ϕ 4 theory in d Euclidean dimensions as an example. We find that the renormalized O(1/N ) corrections to two-and four-point correlators yield perturbative series which are sensitive to the UV cut-off and therefore have a weak convergence. We propose a modification to the architecture to improve this convergence and discuss constraints on the parameters of the theory and the scaling of N which allow us to extract accurate field theory results.
Solid-state switches already replaced thyratrons in the SNS extraction kicker power supplies, offering improved efficiency and reliability. However, recent supply chain disruptions, combined with a self-firing issue, led us to explore alternative solutions. A vendor-developed MOS-Gated Thyristor switch was introduced but ultimately failed during testing. To investigate the failure and assess possible improvements, a benchtop test stand was constructed to evaluate the performance of a single stage board. This test stand features a Pulse Forming Network (PFN) that operates at 6 kV and 6 kA, with a repetition rate exceeding 60 Hz, simulating real operational conditions. By utilizing this setup, we seek to gain a better understanding of the failure mechanisms, refine the switch designs, and ultimately develop a more reliable alternative for the extraction kicker power supplies.
The increasing adoption of distributed energy resources (DERs) over the last decade warrants a reconsideration of control of generation resources. This paper proposes a distributed Automatic Generation Control (AGC) using transmission-and-distribution (T&D) dynamic co-simulation framework for the efficient DPV frequency regulation services. The co-simulation framework allows AGC units to exchange the information for distributed AGC, based on their adopted communication network topology. As a result, a cost-effective automatic generation control is achieved with DPV and conventional generators. The proposed distributed AGC is based on the gossip algorithm in which the neighboring AGC units share the relevant local information with each other and updates their share of AGC regulation signal. Distributed photovoltaics (DPV) unit contribute to AGC response based on their headroom capacity via DER aggregators. The algorithm is tested on IEEE-14 bus transmission system under conditions of generation failure and random load variation to observe effective frequency regulations service offered by DPVs and other AGC units. The study shows that DPV can effectively participate in AGC with the proposed distributed control framework.
This poster details the evaluation of a Michel electron selection algorithm centered on the neural network-based software SPINE (Scalable Particle Imageing with Neural Embeddings). The algorithm was developed and calibrated using simulated data from the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated data from DUNE (Deep Underground Neutrino Experiment).
The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.
Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.
Deployment of wireless technologies is a salient need for modernization, automation and improved operation of nuclear power plants (NPPs). As a single technology cannot support the ever-changing needs, it is required to have a heterogeneous wireless network architecture to address the different technical and economic challenges. However, the coexistence of these multiband heterogeneous wireless networks brings numerous challenges due to the factors including dissimilarity in their channel access mechanism, distance between nodes, transmit power level and many more. This paper develops real-world experiments and simulations of wireless coexistence for Wi-Fi, Fifth generation cellular (5G) and Zigbee in the unlicensed band to understand the challenges and opportunities. The experiments were conducted over the Platform for Open Wireless Data-driven Experimental Research (POWDER) testbed at the university of Utah. In addition, this paper is the first to propose a novel packet rate control technique at the network layer to create temporary opportunities for 5G or Zigbee signal transmissions focusing its application in a nuclear facility while using the shared band. The performance of the proposed coexistence solution is validated with experimental results and simulation.
The quasilinear diffusion coefficient (D QL ) derived from our machine learning framework shows comparable trends with the ground truth D QL obtained from GENRAY-CQL3D simulations. Additionally, for the strong absorption cases, the radial current drive profiles generated using the D QL from our model exhibit consistent behavior with those obtained from the original simulation. These findings indicate the potential of our surrogate modeling approach with physical restrictions to replicate key wave–plasma interaction characteristics while reducing computational costs. Traditionally, calculating D QL for wave–particle interactions relies on computationally intensive wave simulations coupled with Fokker–Planck solvers. To address this challenge, we developed a machine learning-based surrogate model with physical restrictions derived from cold plasma theory and bounce-averaged damping effects. First, we establish the propagation domain of Lower Hybrid Waves in the (N∥, ρ) space by identifying the accessibility limit and determining the upper and lower bounds of N∥ using the Potential Power Deposition (PPD) method. Subsequently, leveraging a database constructed using Latin hypercube sampling alongside the underlying physical restrictions (e.g. PPD), machine learning methods including U-Net and Recurrent Neural Networks are employed to design a physics-restricted machine learning framework capable of reconstructing D QL .
5G advancements like Massive Multiple Input Multiple Output (MIMO) bring high capacity and low latency, but also intensify interference challenges. Static and dynamic coordination techniques address this, often at the cost of increased power draw. We introduce Cookie-Jar (CJ), an interference coordination (IC) framework using reinforcement learning for multi-goal optimization. By dynamically adjusting network, power, and topology parameters based on real-time conditions, CJ improves Signal to Noise and Interference Ratio (SINR) while minimizing power consumption. Simulated 5G experiments showcase CJ's potential, achieving a 15% SINR improvement with near-identical power draw compared to existing methods.