Showing papers by "Clarkson University published in 2023"

Spectral Analysis Approach for Assessing Accuracy of a Low-Cost Air Quality Sensor Network Data

Abstract: Abstract. Extensive monitoring of PM2.5 is critical for understanding changes in local air quality due to policy measures. With the emergence of low-cost air quality sensor networks, high spatio-temporal measurements of air quality are now possible. However, the sensitivity, noise, and accuracy of field data from such networks are not fully understood. In this study, we use frequency analysis of a two-year data record of PM2.5 from both the EPA and Purple Air (PA), a low-cost sensor network, to identify the contribution of individual periodic sources to local air quality in Chicago. We find that sources with time periods of 4, 8, 12, and 24 hours have significant but varying relative contributions to the data for both networks. Further analysis reveals that the 8- and 12-hour sources are traffic-related and photochemistry-driven, respectively, and that the contribution of both these sources is significantly lower in the PA data than in the EPA data. We also use a correction model that accounts for the contribution of relative humidity and temperature, and we observe that the PA temporal components can be made to match those of the EPA over the medium- and long-term but not over the short-term. Thus, standard approaches to improve the accuracy of low-cost sensor network data will not result in unbiased measurements. The strong source dependence of low-cost sensor network measurements demands exceptional care in the analysis of ambient data from these networks, particularly when used to evaluate and drive air quality policies.

The “What” and the “Why” of Pro-Environmental Deeds

Abstract: Abstract Self-determined pro-environmental motivation is arguably the most important motivational resource for protecting the environment and supporting a sustainable lifestyle. At the same time, pro-environmental behavior evolves from multiple goals, values, and cues beyond self-determined motivation. This chapter reviews the state of research on self-determination and values as they relate to pro-environmental behavior. It sketches a framework to promote more widespread pro-environmental behavior by integrating motivation, values and goals, and social support. By understanding the manner in which values and motivation interact in predicting pro-environmental behavior, it becomes possible to identify sources of motivational conflict that lead to environmentally unsound choices. Through a process of aligning individuals’ values and motivation, strategies for intervention begin to emerge.

Graphene-Enhanced Ion Transport in Dual-Conducting Composite Films of Polyacetylene and an Imidazolium Iodide Ionic Liquid

Abstract: Dual-conducting polymer films were synthesized by dispersing graphene in an aqueous solution of poly(vinyl alcohol) and 1-propyl-3-methylimidazolium iodide ([C3mim]I) ionic liquid and thermally converting the poly(vinyl alcohol) to polyene in the presence of hydroiodic acid catalyst. The electrical and mechanical properties of the resulting free-standing films of the nanocomposite, containing different concentrations of graphene, were analyzed using electrochemical impedance spectroscopy (EIS) and dynamic mechanical analysis (DMA), respectively. Nyquist plots (imaginary vs real components of the frequency-dependent impedance) showed two characteristic arcs representing the composite's electronic and ionic conduction pathways. The conductivity values corresponding to both charge transport mechanisms increased with temperature and the graphene concentration. The enhancement in electronic conductivity is expected because of graphene's high electron mobility. Interestingly, ionic conductivity also showed a significant increase with graphene concentration, approximately triple the extent of the rise in the electronic conductivity, even though the loss and storage moduli of the films increased. (Generally, a higher modulus results in lower ionic conductivities in ionic gels.) Molecular dynamics simulations of the three-component system provided some insights into this unusual behavior. Mean square displacement data showed that the diffusion of the iodide anions was relatively isotropic. The iodide diffusion coefficient was higher in a blend with 5 vol % graphene than in blends with 3 vol % graphene or no graphene. The improvement is attributed to the interfacial effects of the graphene on the free volume of the blend. Furthermore, an exclusion of the iodide ions from the vicinity of graphene was observed in the radial distribution function analysis. The increase in the effective concentration of iodide due to this exclusion and the increase in its diffusion coefficient because of the excess free volume are the primary reasons for the observed enhancement in ionic conductivity by adding graphene.

Aircraft Structures Projects Involving Aviation Museums Across Canada and the United States

Abstract: COVID-19 presented a unique challenge to the delivery of aerospace engineering education, and education as a whole. Many universities operated fully online courses for Spring 2020, Fall 2020, and Spring 2021 semesters which required a rapid re-development of these courses as many were not online courses previously. One challenge was to maintain student engagement in the course material, with each other, and with the course instructor through online platforms such as Zoom. An approach taken for the delivery of two, consecutive aircraft structures courses involved the collaboration of 10 aviation museums across Canada and the United States. The 75th anniversary of the Victory in Europe and Victory in Japan occurred in 2020, and a number of aviation museums intended to recognize this important anniversary. COVID-19 limited the events that the museums could provide the public; therefore, participation in the Aircraft Structure Museum Project provided a unique opportunity to commemorate these anniversaries. Each museum provided an aircraft from their collections for groups of 5 to 6 junior level engineering students to analyze. During Fall 2020, the groups analyzed the structures of the wings and stabilizers. The groups were reformed for Spring 2021 and the groups analyzed the fuselage structure. The project was continued in person for Fall 2021 and Spring 2022, this iteration commemorating diversity and inclusion in aerospace. Each group analyzed an aircraft flown by an under-represented minority and learned about the challenges encountered by that individual. The groups provided presentations and reports to the museums detailing their analyses. This interaction provided the opportunity for the students to connect with famous aircraft, famous people, communicate effectively to technical and lay audiences, and motivated the students to engage with each other frequently. Qualitative assessment of the course evaluations using NVIVO show strong alignment with the Wiggins' framework for authentic assessments, and a positive sentiment by the students toward the project. Preliminary surveys of the 2021-2022 students indicate a very positive reaction, with 76\% of students stating that the project helped with their conceptual learning.

Physics-informed reduced-order learning from the first principles for simulation of quantum nanostructures

Abstract: Abstract Multi-dimensional direct numerical simulation (DNS) of the Schrödinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high degrees of freedom (DoF). This study employs a physics-based reduced-order learning algorithm, enabled by the first principles, for simulation of the Schrödinger equation to achieve high accuracy and efficiency. The proposed simulation methodology is applied to investigate two quantum-dot structures; one operates under external electric field, and the other is influenced by internal potential variation with periodic boundary conditions. The former is similar to typical operations of nanoelectronic devices, and the latter is of interest to simulation and design of nanostructures and materials, such as applications of density functional theory. In each structure, cases within and beyond training conditions are examined. Using the proposed methodology, a very accurate prediction can be realized with a reduction in the DoF by more than 3 orders of magnitude and in the computational time by 2 orders, compared to DNS. An accurate prediction beyond the training conditions, including higher external field and larger internal potential in untrained quantum states, is also achieved. Comparison is also carried out between the physics-based learning and Fourier-based plane-wave approaches for a periodic case.

Outage Management of Active Distribution Systems With Data Fusion From Multiple Sensors Given Sensor Failures

Abstract: Complex scenarios of multiple faults, sensor failures, and the fault current from distributed generations (DGs) pose a challenge for efficient outage management in distribution systems. To solve the issue, this study proposes a new algorithm for sequential determination of outage scenarios with data fusion from multiple sensors, i.e., smart meters and remote fault indicators (RFIs). To this end, an active distribution system is modeled as an undirected graph with vertices representing load zones, protective devices and RFIs, and edges denoting their adjacency. Set operation is proposed to derive the expected RFI alarms for each load zone if faulted and smart meter outage reports for each protection device. This serves as an input to the proposed evidence-driven methodology to sequentially quantify the credibility of the potential outage scenarios supported by RFIs and smart meters. By ranking the credibility, the most credible scenario of the activated protection and fault location, either on the primary feeder or laterals, is determined for expedited power recovery. Test cases of the IEEE System and a utility circuit show that the proposed approach outperforms the state-of-the-art technologies in terms of its accuracy and computational efficiency, leading to more efficient outage management of distribution systems.