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The Process To Design and Analyze Dynamic Environment Test Fixtures (PDADyE)
No full textVibration qualification testing is an important and common requirement in many industries. In-lab testing is intended to evaluate a component’s life expectancy by exposing it to a dynamic environment similar to what it may experience in its field condition. To successfully perform these tests, it is imperative that the component of interest’s field boundary conditions (BCs) are replicated as closely as possible in the lab. One approach to achieving accurate dynamic environment testing is to design a fixture that sufficiently represents the impedance of the next-structure (field structure). The process to design and analyze dynamic environment test fixtures (PDADyE) is a method created to guide users through the process of successfully developing a vibration test fixture that accurately reproduces components field BCs. These BCs can consist of multiple directions of excitation and attachment locations between the device under test and its field structure. Using lumped parameter models built in MATLAB, this work demonstrates the process procedure and its effectiveness
Feasibility study on replacing ordinary portland cement with coal gangue-based geopolymer based on LCA and LCIA
No full textTo investigate the feasibility of replacing traditional ordinary Portland cement mortar (OPCM) with coal gangue-based geopolymer mortar (CGM), a multi-dimensional evaluation approach covering mechanical performance, environmental impact, uncertainty analysis, and life cycle sustainability cost (LCSC) was established. Using 1 m³ of structurally equivalent mortar as the functional unit and a cradle-to-gate life cycle model based on ISO 14040/14044 standards, the differences between CGM and OPCM were quantified in terms of environmental indicators including Global Warming Potential (GWP100), Acidification Potential (AP), and Abiotic Depletion Potential (ADP). Sobol sensitivity analysis and Monte Carlo uncertainty simulation were introduced to explore the influence mechanisms and variability characteristics of key parameters. The results showed that, compared to the ordinary Portland cement system, the coal gangue geopolymer reduces GWP100 by about 43%, significantly lowering the life-cycle carbon footprint, but it entails higher burdens in the AP and ADP indicators (greater resource consumption and acid gas emissions). Sensitivity analysis indicates that the activators (water glass and NaOH) and coal gangue calcination are the primary driving factors of CGM’s environmental impacts. Uncertainty simulation suggests that the CGM system is relatively stable for GWP100 but shows pronounced variability in the AP and ADP dimensions. Although CGM outperforms OPCM in environmental externality costs, its total LCSC reaches ¥1219 nearly three times that of OPCM primarily due to the high cost of activator materials. In summary, coal gangue geopolymer, as a green building material, has broad application potential in carbon emission reduction and engineering performance, but its sustainable adoption will depend on coordinated efforts such as developing greener activators, optimizing energy pathways, and exploring high value-added applications
Cloud-Top Entrainment Instability in Marine Stratocumulus Clouds: Observational Evidence From Collocated Microphysical, Turbulence, and Radiation Measurements
No full textMarine stratocumulus clouds (MSC) strongly influence Earth\u27s radiation budget, yet the mechanisms governing the descent of entrainment-affected (diluted) parcels, and the relative roles of cloud-top entrainment instability (CTEI) and longwave radiative cooling (RC), remain debated. Using helicopter-borne observations from the ACORES campaign that combine high-resolution in situ vertical profiling with co-located remote sensing, we examine vertical variations of microphysics, thermodynamics, and the entrainment interfacial layer (EIL). When CTEI conditions were strongly met, inhomogeneous mixing (IM) traits appeared near cloud top and transitioned to homogeneous mixing (HM) traits deeper in the cloud layer, accompanied by localized elevations of cloud base, signatures consistent with enhanced descent of diluted parcels. We argue that these apparent HM traits arise from adiabatic warming and evaporation during descent rather than true HM. When CTEI was weakly met or not met, IM traits near the top were weaker, HM traits emerged deeper in the cloud, and cloud base elevation was not observed, these differences are explained by RC-driven buoyancy contrasts modulated by turbulence and EIL thickness. Even in such cases, diluted parcels descended, but weakly. Integrating these results with prior field studies, we provide observational evidence that sufficiently strong CTEI can dominate RC and drive diluted-parcel descent, clarifying how CTEI, RC, and EIL thickness jointly shape MSC structure and offering guidance for improved representation in weather and climate models
2024 Energy and Fuels Community Highlights: Advancing Materials and Technologies in Decarbonization and Renewable Energy Solutions
No full textLeveraging Drinking Water Pumps as Flexible Loads Using Input Convex Neural Networks
No full textDrinking water distribution networks can be operated as flexible loads within the electric power grid due to their substantial pumping demands and water storage capabilities. Optimizing the flexible operation of a water distribution network poses significant challenges due to the complex physical laws within the network, where the hydraulic head difference equations for pipes and pumps are nonconvex. Standard nonconvex optimization solvers often fail to provide globally optimal solutions and the time required for computation can be prohibitively large. To resolve these issues, we present an optimization approach that accurately approximates the nonconvex constraints using input convex neural networks (ICNNs). This method converts the mixed-integer nonconvex optimization problem into a mixed-integer linear program, improving computational efficiency and scalability while maintaining the optimization problem\u27s intuitive structure. In two case studies, we compare the ICNN-aided approach with the original nonconvex problem and found that the ICNN-aided approach outperforms the nonconvex solver in terms of computational time and optimality
A Novel Cooperative Roadside Unit Broadcast Approach for Future Intelligent Transportation Networks
No full textIn this paper, we propose a novel cooperative roadside unit (RSU) broadcast approach that can effectively enhance the vehicles’ packet-reception rates of the messages broadcast cooperatively by nearby RSUs. To enable message-aggregation among RSUs, we propose a new vehicular-message format based on the software-defined multiplexing code which can aggregate multiple messages without any need of packet form. To facilitate our proposed new cooperative scheme, RSUs must be organized into clusters. We formulate this RSU clustering problem as a graph partitioning problem, which aims to make individual cluster sizes as close to each other as possible. Furthermore, we design a new heuristic algorithm to efficiently solve this problem. This heuristic algorithm is also compared with the random clustering and minimum-degree clustering schemes in terms of the modified Gini index. The theoretical analysis of our proposed cooperative RSU broadcast scheme is also conducted to guarantee that our proposed new cooperative RSU-broadcast approach can improve the packet reception rate. Furthermore, we simulate the vehicular environment with the simulation tools SUMO and NS-3 as benchmarks. The simulation demonstrates that our proposed new cooperative RSU-broadcast system will increase the packet reception rate and the number of successfully received packets over the conventional non-cooperative broadcast system
The first 10 years of the HAWC Gamma-Ray Observatory: science results
No full textThe High-Altitude Water Cherenkov (HAWC) Observatory, located on the slopes of the Sierra Negra volcano in Mexico, began operations in March 2015. Over the past decade, HAWC has enabled the exploration of a broad range of topics in high-energy astrophysics and particle physics, resulting in more than 90 peer-reviewed publications. These studies have significantly advanced our understanding of several previously unexplored and poorly understood phenomena in the TeV energy regime. The present work provides an overview of the key scientific contributions of HAWC during its first ten years of operation
Engineering Electrochemical Molecularly Imprinted Polymers for Next-Generation Biosensing
No full textDeveloping reliable, low-cost biosensors is essential for advancing point-of-care diagnostics and expanding access to health monitoring technologies. Traditional sensors based on biological recognition elements such as enzymes and antibodies often suffer from limited stability, high production costs, and dependence on cold storage. This dissertation addresses these limitations through the design and optimization of electrochemically synthesized molecularly imprinted polymers (eMIPs), synthetic recognition elements that combine the specificity of biological systems with the robustness of engineered materials. A systematic framework was established to evaluate how monomer selection, concentration, scan rate, and cycle count affect film formation, morphology, and sensor performance. Using dopamine, pyrrole, and 3-aminophenyl boronic acid monomers, eMIP films were synthesized directly on electrode surfaces and characterized using cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), electrochemical surface plasmon resonance (EC-SPR), and electrochemical quartz crystal microbalance (EC-QCM). Machine learning models were incorporated to identify the most influential fabrication parameters and predict synthesis conditions yielding optimal sensitivity and reproducibility. Case studies demonstrate the versatility of this framework across multiple analytes, including cortisol, lactate, and the SARS-CoV-2 spike protein. Integration of Prussian blue nanoparticles enabled reagent-free detection, eliminating the need for solution-based redox probes and simplifying sensor architecture for field and wearable applications. Extension of eMIP fabrication to protein templates introduced optimized immobilization and elution strategies, addressing challenges associated with imprinting large, fragile biomolecules. Collectively, this work bridges the gap between proof-of-concept MIP studies and practical biosensor design. The results provide fundamental insight into how electrochemical parameters govern film growth and recognition site fidelity, while establishing design principles for stable, selective, and data-driven eMIP sensors. These contributions lay the groundwork for next-generation diagnostic systems that are reagent-free, reusable, and suitable for real-world deployment, advancing the long-term goal of accessible, point-of-care health monitoring technologies
REVIEW AND DEVELOPMENT OF CENTRALIZED ENGINEERING TOOL FRAMEWORKS TO ENHANCE PRODUCTIVITY AND BRIDGE GAPS IN CURRENT ENGINEERING PROCESSES
No full textThis thesis aims to tackle some of the inefficiencies with the current methods of engineering. The issues are specifically the inefficient iteration processes between preliminary design and detail level design, a lack of centralized resources, and poor project organization and management. The thesis provides a case study on the current tools that solve this issue and then presents a new proof of concept framework for an engineering tool. This tool addresses these issues by unifying calculators, databases, external tools, and project management systems under one modular platform.
The tool consists of one central GUI with multiple independent modules such as a beam calculator, CAM designer and material database that are displayed within the main GUI which enhances the modules with project management and metadata systems. Its architecture is designed for modularity and scalability, enabling future expansions of new tools.
Verification tests confirm that the system runs as intended and can create preliminary design CAD models using parametric knowledge-based engineering which can then be analyzed in FEA. This helps reduce time for initial iterations by eliminating initial guess work. The tool was then extensively tested in real engineering scenarios where it proved essential to the success of the projects. Expediting timelines and completing all requirements for the project proving that the tool works and is needed in engineering workflows