ItemDeveloping electrospun fibers for hydrogen storage applications(Wichita State University, 2023-05) Mohammed, Qamar Saberi; Asmatulu, EylemThe primary objective of this research was to produce an electrospun nanofiber for the application of hydrogen storage, and the absorption kinetics of the highly porous nanocomposite fiber mats. One of the critical components of advancing hydrogen, and fuel cell technologies advancement is successfully storing hydrogen for use in various industries, like transportation, defense, compact gadgets, and energy. Hydrogen energy is the future because of its highest energy density, availability, and environmental and health benefits. Currently, enterprises are searching for a solution for energy distribution management and hydrogen gas storage. In this way, there is a need to develop a hydrogen storage innovation that might be considered for later use in aviation applications. It is being researched that functional nanocomposite fibers incorporated with hydrogen-sensitive inclusions will increase hydrogen storage capacity, and absorption/desorption kinetics of hydrogen gas at lower temperatures and pressures. Here, the electrospinning method has been used to produce polymeric nanofibers with different nanoscale metal hydrides, and conductive particles that can store hydrogen under a controlled environment, and enhance thermal properties. Selected polymeric materials for hydrogen storage that have been investigated are polyacrylonitrile (PAN), poly (methyl methacrylate) (PMMA), and sulfonated polyether ether ketone (SPEEK) in combination with metallic hydrides, and multi-walled carbon nanotubes. On testing, it was observed that hydrogen capacity with SPEEK, which includes 4% MWCNT and 4% MH (Mmni4.5Fe0.5) shows significant H2 uptake compared to PAN/PMMA polymer. ItemPredictive machine learning model for the future trend of energy consumption in fully electricity homes considering occupancy status of the building(Wichita State University, 2023-07) Hosseini, Amin; Kim, Yang-SeonHVAC unit is one of the most power-intensive loads in buildings. It is also very significant for residential customers because indoor temperature must be maintained within an acceptable range of occupant’s comfort. To minimize energy consumption while providing a comfortable environment for the occupants, Building Energy Simulation (BES) gained considerable attention in recent years. Available BES calculates building energy consumption during the design phase and therefore, they optimize building energy consumption in this stage. However, there are still deficiencies that prevent BES from achieving higher efficiencies. Using a fixed occupancy schedule, not considering complexities in the occupant’s interactions with indoor appliances and HVAC unit are some of the setbacks that reduce the accuracy of the BES tools. Introduction of smart thermostats, made it possible for researchers to study the trend of changes in different measurable variables in the indoor environment like temperature, humidity, the runtime of the HVAC system, occupancy schedule, cooling and heating set point temperatures, etc. Data obtained from smart thermostat can be used to build a predictive model using a machine learning technique. Machine learning techniques help to estimate future trends of indoor variables like occupancy schedule, set point temperature and building energy consumption. Feeding the predicted variables by machine learning to the BES software helps to create a more accurate model for the energy simulation of buildings. This study presents a novel predictive model based on a co-simulation method using EnergyPlus and machine learning technique to better manage the energy consumption in the residential buildings. The proposed approach, which combines neural network and physic-based energy modeling, successfully estimates the total energy consumption in buildings with CV(CV(RMSE)) of 2.22% and NMBE of 5.65% on hourly basis. ItemComparison of the hybrid II and hybrid III ATD head-neck responses using the HIC component testing device(Wichita State University, 2023-05) Torline, Matthew; Lankarani, Hamid M.The Head Injury Criterion (HIC) is an injury model which measures the likelihood of head injury resulting from a dynamic impact or collision. This injury model is included in regulations held by the Federal Aviation Administration (FAA) and the National Highway Traffic Safety Administration (NHTSA). Additionally, HIC has been used greatly in research concerning head injury throughout a dynamic event, such as the research of several groups using the HIC Component Testing Device (HCTD). The HCTD is a device which utilizes a Hybrid II Anthropomorphic Test Device (ATD) head-neck system to recreate a similar motion and loading profile to what can be typically seen by crash sled test data of a fully assembled ATD enduring a dynamic event. For use of the HCTD, an ATD head-neck is attached to a pendulum arm which rotates around a revolute joint. Being propelled forward by means of a pneumatic piston, the head-neck undergoes a motion similar to the head motion associated with crash sled testing. Due to this simplification, overall ATD motion can be closely replicated between test iterations. This research seeks to explore the applications of the HCTD through a series of rigid bulkhead impacts. Through attachment of the Hybrid III head-neck to the HCTD pendulum arm, the Hybrid II and Hybrid III head-neck responses are compared through a variety of conditions, where multiple head velocities and bulkhead distances are utilized to initiate varying behaviors. Kinematic head paths are plotted, and head acceleration data is used to calculate the HIC values for comparison. Additionally, an upper neck load cell is utilized for the Hybrid III to measure neck forces and moments at the occipital condyle region during impact. These forces and moments are utilized to calculate additional injury models such as Nij and peak neck compression, tension, flexion, extension, and shear. The results of this study compare common injury mechanisms between the two ATD types and demonstrate the Hybrid III’s exceptional head-neck response. ItemDielectric relaxations in soy protein-dimethyl sulfoxide mixtures(Wichita State University, 2023-05) Tan, Zhao Heng; Li, BinPlant proteins have been extensively studied as promising bioresources for sustainable materials applications, because of their abundance in nature and remarkable tunability in structures and properties, in face of the rising environmental and socioeconomic concerns of petrochemicals. A tremendous number of studies have explored their potential applications in packaging, composites, and biomedical applications, etc... However, unlike synthetic polymers and another popular biopolymer from natural resources, i.e., cellulose, proteins have complex structures and diverse functional groups, and they exhibited sophisticated, but insufficiently understood, interactions with chemical environment, which are vital to their aggregated structures, and eventually applications. To successfully apply proteins to practical materials applications, a better understanding of protein structures and behaviors, subjected to chemical environment, is needed. Among various plant proteins, soy protein has received the greatest attention, due to its richness in nature as the product of the major economic crop, soybeans. This project focused on soy protein isolate (SPI), and its interactions with dimethyl sulfoxide (DMSO), one of the most popular organic solvents in materials and biological research. Pure SPI exhibited 3 relaxation processes, α, β, γ relaxations, related to segmental chain motions, conductivity relaxation, and the fast local motions of protein chain subunits, respectively. The unique interactions between sulfoxide groups in DMSO and amine groups in SPI apparently had an enhancing effect on both β and γ relaxations, besides the plasticizing effects on all three relaxations. The absorbed moisture introduced a new relaxation process between β and α relaxations. With increasing temperature, the coupling of β and γ relaxations with α relaxation became prominent, leading to apparent merge with α relaxation. ItemCarbonized pan unidirectional fiber reinforced composites with nanoscale inclusions for improved thermo-mechanical properties(Wichita State University, 2023-05) Ravi, Nivedhan; Asmatulu, RamazanPolymeric nanocomposites and carbon-carbon composites are generally lighter and stronger and hold the key features for many industrial applications, such as aerospace, automotive, defense, electronics, biomaterials, sensors, energy, and consumer products. The objective of this study is to develop carbon-carbon and carbon-SiC nanocomposites by adding graphene into polyacrylonitrile (PAN) thermoplastic and carbon/SiC fibers where fiber reinforcement is used to enhance the mechanical properties leading to high strength-to-weight ratio nanocomposites. In this study, a mixing ratio of 20:80 for PAN and dimethylformamide (DMF) solvent was used to dissolve the PAN power using a magnetic stirrer. Graphene nanoflakes were then added to the solution at 0-4 wt.% ratio before making the nanocomposite coupons in aluminum grooves with carbon fibers/SiC fibers placed as reinforcement materials. After drying at room temperature, the coupons with PAN, graphene and carbon fibers/SiC fibers were oxidized at 200°C for 2 hours in air, and then carbonized in 650 °C for additional 2 hours in the presence of Argon (Ar) gas. Several material characterization techniques such as tensile tests, Fourier Transform Infrared Spectroscopy (FTIR), water contact angle, Scanning Electron Microscopy (SEM), Differential Scanning Calorimetry (DSC), flame retardancy (UL94), and electrical conductivity were conducted. It was found that the oxidized coupons of carbon fibers containing 4% graphene in PAN were found to be the superior ones in terms of mechanical strength, withstanding a maximum load of 9469.88 N and an Ultimate Tensile Strength (UTS) of 632.8 MPa at fracture. After conducting the water contact angle, all the coupons were found to be hydrophilic. It was found that the carbonized coupons were electrically conductive with a mean value of 344 S/m and fire retardant. While SEM helped to cross-check the right orientation of fibers, FTIR confirmed the presence of different functional groups of PAN and graphene. About 2% graphene carbonized and 4% graphene carbonized SiC reinforced coupons were found to have a Tg value of 126.13°C and 132.73°C, respectively.