Data-driven Identification of Bandgaps in Flexural Metastructures using Component Mode Synthesis and FRF Based Substructuring

Metastructures, characterized by their periodic unit cells, are known for their ability to block the propagation of elastic waves within specific frequency ranges, known as “bandgaps”. To estimate the wave propagation characteristics of these systems, two primary approaches are employed: physics-based methods and data-driven techniques. Physics-based methods depend on the material properties and geometry of the unit cells, while data-driven approaches utilize experimental data, such as steady-state dynamic response data. This study assesses the effectiveness of data-driven techniques, particularly Component Mode Synthesis (CMS) and Frequency Response Function-Based Substructuring (FBS), in identifying bandgaps in metastructures composed of multiple unit cells. The focus is on metastructures consisting of 1D beams that exhibit flexural wave behavior. Within these structures, two significant challenges arise when using frequency response functions based on out-of-plane response data: the absence of rotational degrees of freedom (dofs) and the presence of rigid-body modes. Both factors critically impact the dispersion relationship and, by extension, the bandgap estimation. Traditionally, capturing rotational dynamics has been difficult due to limitations in direct experimental measurement, necessitating the inference of rotational dofs from translational measurements. Furthermore, rigid-body modes are estimated from experimental data. To overcome these challenges, we propose the estimation of rotational dofs by curve-fitting of translational dofs. In addition, this study explores a novel approach to the estimation of rigid body modes from the modal parameters acquired using the well-known Polymax algorithm. The discussed methodologies are also applied to derive dispersion relations for infinite metastructures

On-Road Investigation of Energy Saving Opportunity for Autonomous Light-Duty Vehicles through Automated Vehicle-Following in Safe Distance Scenarios

Reducing aerodynamic drag through Vehicle-Following is one of the energy reduction methods for connected and automated vehicles with advanced perception systems. This paper presents the results of an investigation aimed at assessing energy reduction in light-duty vehicles through on-road tests of reducing the aerodynamic drag by Vehicle-Following. This study provides insights into the effects of lateral positioning in addition to intervehicle distance and vehicle speed, and the profile of the lead vehicle. A series of tests were conducted to analyze the impact of these factors, conducted under realistic driving conditions. The research encompasses various light-duty vehicle models and configurations, with advanced instrumentation and data collection techniques employed to quantify energy-saving potential. The study featured two sets of L4 capable light duty vehicles, including the Stellantis Pacifica PHEV minivan and Stellantis RAM Truck, examined in various lead and following vehicle configurations at different speeds with cruise control enabled. Energy savings per km in the range of 9-17% were observed in Pacifica and savings up to 25% were obtained in RAM within 1-2 seconds following gap for speeds of 55-75 mph. It was also observed that the lateral positioning has a significant impact on energy saving overall. The results are also compared to the previous studies on drag reduction in two-vehicle platoons. This investigation contributes valuable knowledge to the vehicle-following to reduce the aerodynamic drag and thus to reduce the overall energy consumption in the highway driving scenarios. This can also be used in advanced vehicle positioning controls in autonomous vehicles where advanced sensing of the relative positions can be estimated accurately. The results give insights into optimizing energy efficiency, with a focus on the role of Vehicle-Following, aerodynamic drag reduction, and lateral positioning strategies for sustainable and environmentally conscious road transportation

Leveraging Product and Process Characteristics Across the Concrete Pavement Life Cycle to Integrate Global Warming Potential into Project Procurement Processes

The objective of this research is to support sustainable procurement of concrete pavements by linking materials-level global warming potential (GWP) to the project-level. Infrastructure owners require reliable environmental product declarations (EPDs) and methodologies for integrating GWP into the procurement process to ensure equitable decision-making. This work provides insights into current EPD reliability by assessing the sensitivity of concrete GWP to materials-level contributors to recommend a level of supply chain specificity needed to effectively communicate GWP. A benchmarking methodology was developed and implemented to establish reference values for procuring sustainable products. Having provided evidence towards EPD reliability, this work presents a framework that integrates GWP with pay items in project specifications. Linking incentives within infrastructure owner specifications to desired performance characteristics encourages all involved stakeholders to prioritize achievement of those performance characteristics. This same concept can be applied using GWP as the desired performance characteristics. A data collection protocol and life cycle information model (LCIM) for concrete pavement construction were developed to facilitate GWP integration into current project procurement practices. The LCIM methodology was developed and implemented to estimate the production and construction environmental impacts of six real-world concrete pavement construction projects. Applying the LCIM methodology allowed this work to map GWP to pay items and incentives in specifications and provide a pathway to extend a LCIM across the life cycle. The LCIM was further demonstrated on a real-world joint repair project, as well as for a concrete pavement reconstruction, demolition, and waste hauling. The culmination of this research demonstrated that the LCIM can be used to estimate the embodied environmental impacts of a concrete pavement across its life cycle and provided a framework for integrating environmental impacts into the procurement process, facilitating sustainable project procurement for infrastructure owners

Aggregate-Superpose-Project: A Cognitive Model for Quantum Problem Solving

This work proposes a novel cognitive model, called Aggregate-Superpose-Project (ASP), to facilitate problem solving and the analysis of algorithms in Quantum Computing (QC). Our model contains three simple abstractions that help students use classic computing concepts towards specifying quantum states and transformations. Simplicity is a major advantage of ASP along with reinforcing the use of classical concepts in learning QC abstractions. Preliminary evaluations indicate that ASP can provide students with the means to describe quantum algorithms at appropriate levels of abstraction

MEASURING OCCUPANT CONTROLLED VENTILATION AND COOKING FREQUENCY TO AID IN MODELING ENERGY PERFORMANCE OF NORTHERN MICHIGAN HOMES

This research investigates the real-world interplay between energy efficiency and indoor air quality. We explore how home ventilation strategies, heating systems, and weatherization levels in rural homes interact. As part of a larger study on air quality, 17 homes participated in the study. Specifically, this research shows the results and methods for monitoring cooking frequency, kitchen range hood use, and bathroom fan use over two, month-long study periods to build accurate energy and contaminant transport models of homes that were studied. Energy audits to document home characteristics were conducted, including blower door testing and detailed qualitative data regarding the homes’ energy performance and factors influencing indoor air quality. Energy models were created based on these energy audits. The single most common form of kitchen ventilation was no mechanical ventilation, although almost every kitchen had an operable window. The studied homes were far from uniform with widely ranging utilization of kitchen range hoods (0-139 min/day average utilization) and bathrooms exhaust fans (0-264 min/day). The envelope leakage was also quite diverse with some homes being as tight as 3 ACH50 whereas others as loose as 45 ACH50. Future work will include contaminant modeling to show the indoor air quality impacts of the home characteristics, pollutive events inside the home, as well as the ventilation strategies that were employed by the homeowners

Fracture Initiation Pressure as a Measure of Cemented Paste Backfill Strength

This laboratory-scale study presents the development and validation of a hydraulic fracturing technique to directly measure the tensile strength of cemented paste backfill (CPB), providing an alternative to traditional strength testing methods. Fracture initiation pressure (FIP) was used as the primary measure of CPB strength. Experimental results were compared with traditional benchmark measures such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and critical Mode-I fracture toughness (KIc). Regression analysis of experimental results revealed a strong linear relationship between FIP and these benchmark strength measures, indicating that FIP can be used as a reliable predictor of CPB strength. However, traditional linear elastic failure models did not adequately explain the observed FIP values, as they significantly over-predicted the CPB tensile strength. To address this, the Point Stress (PS) model was applied, which provided a more accurate prediction of tensile strength, especially in cases involving small boreholes. The PS model explained observed effects of borehole size on the material’s response to hydraulic pressurization. This study confirms that hydraulic fracturing, interpreted through the PS model, is an effective method for determining CPB strength and provides a practical alternative measure to conventional testing methods

Reconstructing the geological and geomorphological history of Morella Crater, Mars

Ancient impact craters on Mars provide insights into the geological events and are time markers for studying global processes like colossal volcanism and fluvial activities. Among these craters, the 77 km diameter Morella Crater serves as a representative, capable of demonstrating diverse processes that acted on Martian terrain, and hence, the geological and geomorphological history of this crater is studied in detail. Despite its infilling, Morella hosts Ganges Cavus, a significant collapse structure, and Elaver Vallis, an outflow channel. We hypothesize the development of the crater through five stages, from its origin to its current denuded state, exhibiting diverse processes that determine the fate of Martian craters. Crater size-frequency distribution suggests a formation age of 3.8−0.03+0.03 Ga for the plateau hosting Morella Crater and 3.6−0.01+0.06Ga for Morella Plains, the vast expansive plains within the crater. The occurrence of pyroxene and olivine in Morella Plains, identified through hyperspectral data, indicates impact-induced volcanism. The heat source associated with faulting and dike intrusion in the adjoining Ophir Catenae Structural Complex might have ruptured the confined cryosphere, resulting in the formation of Ganges Cavus and eventual filling of Morella with water, which subsequently breached to form Elaver Vallis at3.4−0.10+0.07 Ga. Hydraulic modelling reveals a floodwater volume of 3.27 × 1012 m3 and an estimated peak discharge of 3 × 107 m s−1 associated with this event. Morella witnessed additional fluvial activity at 3.3−0.4+0.1 Ga that created the dark-toned channels. The extensive range of geological and geomorphological processes makes Morella Crater a promising location for future Mars missions

Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2013

Using 2013 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata

EVALUATING CHALLENGES AND SOLUTIONS FOR BUCKTHORN CLASSIFICATION IN SHADOWED ENVIRONMENTS

Invasive species, such as buckthorns, pose significant ecological threats by displacing native vegetation and reducing biodiversity. This study examines the impact of shadows on the classification accuracy of buckthorns using drone-based multispectral imagery collected in a forested area near Michigan Technological University. Shadows impacted approximately 70% of the imagery, notably distorting reflectance in key spectral bands such as near-infrared (NIR) and red edge. The study evaluated vegetation indices like NDVI and the performance of machine learning models, specifically the Random Forest classifier, under these shadowed conditions. Conventional shadow correction techniques, including histogram normalization, Shadow Index (SI), and Inverse Distance Weighting (IDW) interpolation, provided only marginal improvements. The highest classification accuracy achieved was 49.5%, with a Kappa coefficient of 0.24. These findings highlight the challenges of utilizing single-date multispectral imagery in heavily shadowed environments. The study recommends exploring advanced techniques such as Hue Saturation Value (HSV) correction, multi-temporal data fusion, and deep learning approaches to enhance vegetation classification

Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2014

Using 2014 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata