MICROWAVE HEATING SIMULATION OF METALS AND DIELECTRIC CERAMICS

The research objectives proposed to study metal processing using a modular industrial microwave oven. The intent of the oven was to perform casting for metal processing purposes. The research objectives were to validate the ovens performance for melting copper and then to compare the results to modeling data. The initial intent was to test the oven for its capability to melt metals. Most researchers would argue that the industrial microwave could not be used for metal processing. However, this research proposed to answer the question as to whether the industrial microwave oven could be used for processing metals or not. The strength of the research lies in the fact that the technology had not been tested on a global scale and industry has not accepted the capabilities of the oven. Nevertheless, developmental efforts have continued and the microwave technology has not ceased to be developed. Although there would be problematic issues, the focus was not to prove the theoretical equations and derive large data sets for the experiments; but to validate that the oven could be used for processing metals and used in an industrial setting where alternative metal processing technologies exist. In order to perform the research, the unit was designed and manufactured and auxiliary components purchased. The research proposed to cast copper in the experimental modular microwave oven and compare the data to the modeling data. Data collection was basically coordinated using thermocouples along the mold and an optical pyrometer for the metal. The final casts were analyzed for both metallurgical and chemical characteristics. A model was designed based upon the dimensions and operational parameters of the experimental oven and data comparison was made. A simulator was then derived using computer code to formulate a user interface panel and simulation environment representative of a laboratory environment. In order to pursue the research goals, material properties were derived as functions of temperature. For the electromagnetic properties the dielectric permittivity was required along with suggestions for the electromagnetic boundary conditions. An experiment was developed and the properties were measured for several dielectric materials; thus the most suitable ceramic material chosen

Design and implementation of sensor systems for control of a closed-loop life support system

The sensing and controlling needs for a Closed-Loop Life Support System (CLLSS) were investigated. The sensing needs were identified in five particular areas and the requirements were defined for workable sensors. The specific areas of interest were atmosphere and temperature, nutrient delivery, plant health, plant propagation and support, and solids processing. The investigation of atmosphere and temperature control focused on the temperature distribution within the growth chamber as well as the possibility for sensing other parameters such as gas concentration, pressure, and humidity. The sensing needs were studied for monitoring the solution level in a porous membrane material along with the requirements for measuring the mass flow rate in the delivery system. The causes and symptoms of plant disease were examined and the various techniques for sensing these health indicators were explored. The study of sensing needs for plant propagation and support focused on monitoring seed viability and measuring seed moisture content as well as defining the requirements for drying and storing the seeds. The areas of harvesting, food processing, and resource recycling, were covered with a main focus on the sensing possibilities for regulating the recycling process

Federated learning framework and energy disaggregation techniques for residential energy management

Residential energy use is a significant part of total power usage in developed countries. To reduce overall energy use and save funds, these countries need solutions that help them keep track of how different appliances are used at residences. Non-Intrusive Load Monitoring (NILM) or energy disaggregation is a method for calculating individual appliance power consumption from a single meter tracking the aggregated power of several appliances. To implement any NILM approach in the real world, it is necessary to collect massive amounts of data from individual residences and transfer them to centralized servers, where they will undergo extensive analysis. The centralized fashion of this procedure makes it time-consuming and costly since transferring the data from thousands of residences to the central server takes a lot of time and storage. This thesis proposes utilizing Federated Learning (FL) framework for NILM in order to make the entire system cost-effective and efficient. Rather than collecting data from all clients (residences) and sending it back to the central server, local models are generated on each client’s end and trained on local data in FL. This allows FL to respond more quickly to changes in the environment and handle data locally in a single household, increasing the system’s speed. On top of that, without any data transfer, FL prevents data leakage and preserves the clients’ privacy, leading to a safe and trustworthy system. For the first time, in this work, the performance of deploying FL in NILM was investigated with two different energy disaggregation models: Short Sequence-to-Point (Seq2Point) and Variational Auto-Encoder (VAE). Short Seq2Point with fewer samples as input window for each appliance, tries to simulate the real-time energy disaggregation for the different appliances. Despite having a light-weighted model, Short Seq2Point lacks generalizability and might confront some challenges while disaggregating multi-state appliances

Addressing the Mars ISRU Challenge: Production of Oxygen and Fuel from CO_2 using Sunlight

Advanced exploration of Mars, particularly human missions, will require vast amounts of fuel and oxygen for extended campaigns and the return of samples or humans back to Earth. If fuel and oxygen can be prepared on Mars from in-situ resources, this would greatly reduce the launch mass of the mission from Earth. In this Keck Institute for Space Sciences (KISS) study, the viability of Mars near-ambient temperature photoelectrochemical (PEC) or electrochemical (EC) production of fuel and oxygen from atmospheric carbon dioxide—with or without available water—was examined. With PEC devices incorporated into lightweight, large-area structures operating near 25°C and collecting solar energy to directly convert carbon dioxide into oxygen, it may be possible to reduce the launch mass (compared with bringing oxygen directly from Earth) by a factor of three or more. There are other numerous benefits of such a system relative to other in-situ resource utilization (ISRU) schemes, notably reduced thermal management (e.g., lower heating demand and decreased amplitude of thermal cycling) and the elimination of a need for a fission power source. However, there are considerable technical hurdles that must be surmounted before a PEC or EC ISRU system could be competitive with other more mature ISRU approaches, such as solid oxide electrolysis (SOXE) technology. Noteworthy challenges include: the identification of highly stable homogeneous or heterogeneous catalysts for oxygen evolution and carbon monoxide or methane evolution; quantification of long-term operation under the harsh Martian conditions; and appropriate coupled catalyst–light absorber systems that can be reliably stowed then deployed over large areas, among other challenges described herein. This report includes recommendations for future work to assess the viability of and advance the state-of-the-art for EC and PEC technologies for future ISRU applications. Importantly, the challenges of mining, transporting, purifying, and delivering water from Mars resources to a PEC or EC reactor system, development and demonstration of a low-temperature-capable, non-aqueous-based CO2 reduction scheme as described below is perhaps the first logical step toward implementing an efficient near-surface Mars temperature oxygen generation system on Mars

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Recent Advances on The Enhanced Thermal Conductivity of Graphene Nanoplatelets Composites: A Short Review

Graphene nanoplatelets (GNPs) have attracted significant attention in the field of thermal management materials due to their unique morphology and remarkable thermal conductive properties. In addition, their impressive thermal properties make them interesting nanofillers for producing multifunctional composite materials with a multitude range of applications. This work specifically reviews the recent advances of the application of GNPs as nanofillers for the development of enhanced thermal conductivity of various materials or composites. In this review, the insight on the improved thermal conductivity of the composites bestowed by the GNPs with comprehensive comparison are briefly discussed. This review might unlock windows of opportunities and paves the way towards the production of enhanced materials for thermal applications including electronics, aerospace devices, batteries, and structural reinforcement

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.Comment: Published on Journal of the American Chemical Society (2023); 102 pages (18-page manuscript, 84 pages of supporting information

Trade-off study and conceptual designs of regenerative Advanced Integrated Life Support Systems /AILSS/, February 1968 - January 1970

Technologies and design concepts of closed integrated life support system for prolonged manned space flight missio

Workshop on Using In Situ Resources for Construction of Planetary Outposts

The workshop examined the potential uses of indigenous materials on the Moon and Mars, other than those associated with the production of propellants for space transportation. The papers presented concerned the needs for construction, based on analysis of the current NASA Mars reference Mission and past studies studies of lunar outposts; the availability of materials on the Moon and Mars; construction techniques that make use of the natural environment; materials production and fabrication techniques based on indigenous materials; and new technologies that could promote the use of indigenous materials in construction