Data-Efficient Scientific Design Optimization with Neural Network Surrogates
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Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis: Preprint
This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty Electric Vehicles (EVs) based on both geospatial and electrical grid analysis. The geo-spatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node level analysis of potential impacts on voltages and power losses in distribution system. A case study using realistic three-phase unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast charging station for heavy duty EVs.
47 OTHER INSTRUMENTATION↗
OCHRE
OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NLR's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis
This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.
47 OTHER INSTRUMENTATION↗
Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network
This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.
36 MATERIALS SCIENCE↗
FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks
Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.
Purohit, Shaurya [Iowa State University Ames Iowa ↗
Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater
Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus , as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway.
59 BASIC BIOLOGICAL SCIENCES↗
Neural-network based collision operators for the Boltzmann equation
Kinetic gas dynamics in rarefied and moderate-density regimes have complex behavior associated with collisional processes. These processes are generally defined by convolution integrals over a high-dimensional space (as in the Boltzmann operator), or require evaluating complex auxiliary variables (as in Rosenbluth potentials in Fokker-Planck operators) that are challenging to implement and computationally expensive to evaluate. In this work, we develop a data-driven neural network model that augments a simple and inexpensive BGK collision operator with a machine-learned correction term, which improves the fidelity of the simple operator with a small overhead to overall runtime. The composite collision operator has a tunable fidelity and, in this work, is trained using and tested against a direct-simulation Monte-Carlo (DSMC) collision operator.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)
This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.
54 ENVIRONMENTAL SCIENCES↗
The Synchronic Web
The Synchronic Web is a distributed network for securing data provenance on the World Wide Web. By enabling clients around the world to freely commit digital information into a single shared view of history, it provides a foundational basis of truth on which to build decentralized and scalable trust across the Internet. Its core cryptographical capability allows mutually distrusting parties to create and verify statements of the following form: “I commit to this information—and only this information—at this moment in time.” The backbone of the Synchronic Web infrastructure is a simple, small, and semantic-free blockchain that is accessible to any Internet-enabled entity. The infrastructure is maintained by a permissioned network of well-known servers, called notaries, and accessed by a permissionless group of clients, called journals. Through an evolving stack of flexible and composable semantic specifications, the parties cooperate to generate synchronic commitments over arbitrary data. When integrated with existing infrastructures, adapted to diverse domains, and scaled across the breadth of cyberspace, the Synchronic Web provides a ubiquitous mechanism to lock the world’s data into unique points in discrete time and digital space. This document provides a technical description of the core Synchronic Web system. The distinguishing innovation in our design—and the enabling mechanism behind the model—is the novel use of verifiable maps to place authenticated content into canonically defined locations off-chain. While concrete specifications and software implementations of the Synchronic Web continue to evolve, the information covered in the body of this document should remain stable. We aim to present this information clearly and concisely for technical non-experts to understand the essential functionality and value proposition of the network. In the interest of promoting discourse, we take some liberty in projecting the potential implications of the new model.
97 MATHEMATICS AND COMPUTING↗
HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network
Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.
Kim, Joon-Seok↗
Data-Driven Template Discovery Using Graph Convolutional Neural Networks
Modeling adversarial activities is a critical component of developing high-con?dence indicators of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). Current approaches to generating representative patterns of interest (a.k.a templates) from the real-world domains involve a Subject Matter Expert (SME)-guided manual process. The goal of Data-Driven Template Discovery (DDTD) is to use a (potentially small) set of SME generated templates to discover other previously unknown and interesting templates in an attributed graph. A template is an activity pattern describing a set of interactions among a group of nodes in the graph. The motivation behind DDTD is to expand the original set of templates, without having SMEs craft all the templates by hand. DDTD also provides seed templates to SMEs, to help them construct larger, high-?delity, and scenario-oriented templates. In these cases, obtaining a larger set of templates that are related (contain similar signals) to the original set is of great value. In this work, we propose to use Graph Convolutional Neural Networks (GCNs) to discover new templates that are heavily related to the original set. GCNs are a family of Neural Network (NN) architectures especially designed to work directly on graphs. In contrast to the traditional NNs, that require considerable amounts of labeled data, GCNs do not require a big labeled training set because they can directly leverage the graph structure instead. This property makes GCNs the perfect tool for creating activity templates.
Joaristi, Mikel↗
Analyzing Infrastructure Interdependencies Using Network-Of-Networks Modeling
Infrastructure networks play a crucial role in our day-to-day lives, and modeling these infrastructure networks can help decisionmakers prepare for and respond to disruptions such as natural disasters or cyberattacks. Because these infrastructure networks depend on each other, it is not sufficient to model a single network in isolation. We build on previous single-network-modeling techniques to develop a methodology for modeling infrastructure interdependencies as a Network-of-Networks. Using distributionlevel data from a real U.S. city on the power grid, road geometry, and hospital locations, we show how to apply this methodology to modeling three of the U.S. Department of Homeland Security's Critical Infrastructure Sectors: Healthcare, Transportation, and Energy. We also analyze three primary metrics before and after a simulated disaster: 1) impact on hospital access; 2) road network impact with the change in betweenness centrality; 3) electric customer outage. We simulate three different disruptions: 1) road flooding from nearby rivers; 2) a malicious actor targeting the road networks; 3) a malicious actor targeting the electric grid. Finally, we discuss how our methodology can be applied to additional infrastructure networks and types of disruption, and how Artificial Intelligence (AI) techniques may be incorporated into this methodology for further research.
Scherrer, Evan [Drake University, Iowa]↗
Fluid – Fluid Interfacial Area and Its Impact on Relative Permeability - A Pore Network Modeling Study
Abstract Relative permeability (kr) is commonly modeled as an empirical function of phase saturation. Although current empirical models can provide a good match of one or two measured relative permeabilities using saturation alone, they are unable to predict relative permeabilities well when there is hysteresis or when physical properties such as wettability change. Further, current models often result in relative permeability discontinuities that can cause convergence and accuracy problems in simulation. To overcome these problems, recent research has modeled relative permeability as a state function of both saturation (S) and phase connectivity (X). Pore network modeling (PNM) data, however, shows small differences in relative permeability for the same S-X value when approached from a different flow direction. This paper examines the impact of one additional Minkowski parameter (Mecke and Arns, 2005), the fluid-fluid interfacial area, on relative permeability to identify if that satisfactorily explains this discrepancy. We calculate the total fluid-fluid interfacial areas (IA) during two-phase (oil/water) flow in porous media using pore network modeling. The area is calculated from PNM simulations using the areas associated with corners and throats in pore elements of different shapes. The pore network is modeled after a Bentheimer sandstone, using square, triangular, and circular pore shapes. Simulations were conducted for numerous primary drainage and imbibition cycles at a constant contact angle of 0° for the wetting phase. Simultaneous measurements of capillary pressure, relative permeability, saturation, and phase connectivity are made for each displacement. Fluid-fluid interfacial area is calculated from the PNM capillary pressure, the fluid location in the pore elements, and the pore element dimensional data. The results show that differences in the relative permeability at the same (S,X) point is explained well by differences in the fluid-fluid interfacial area (IA). That is, for a larger change in IA at these intersection points, the permeability difference is greater. That difference in relative permeability approaches zero as the difference in IA approaches zero. This confirms that relative permeability can be modeled better as a unique function of S, X, and IA. The results also show that an increase in IA restricts flow decreasing the nonwetting (oil) phase permeability. This decrease is caused by an increase in the throat area fraction compared to the corner area as the total area IA increases. The wetting phase relative permeability, however, shows the inverse trend, in that its relative permeability is greater when IA becomes larger owing to a greater fraction of the total area associated with the corners. The area IA, however, impacts the nonwetting phase relative permeability more than the wetting phase relative permeability. Corner flow improves the wetting phase relative permeability because the wetting phase is continuous there. Finally, a sensitivity analysis shows that relative permeability a is more sensitive to change in S than they are for IA for the case studied implying that if only two parameters are used to model relative permeability it is better to choose S and X.
Engineering↗
A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data
Not Available
97 MATHEMATICS AND COMPUTING↗
Overview of RFID Applications Utilizing Neural Networks
As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.
42 ENGINEERING↗
Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)
Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.
14 SOLAR ENERGY↗
Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows
This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.
97 MATHEMATICS AND COMPUTING↗