Long Duration Energy Storage for NM (I-West presentation for NM Tech workshop)
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High-temperature particle receivers are being pursued to enable next-generation concentrating solar thermal power (CSP) systems that can achieve higher temperatures (> 700 °C) to enable more efficient power cycles, lower overall system costs, and emerging CSP-based process-heat applications. The objective of this work was to develop characterization methods to quantify the particle and heat losses from the open aperture of the particle receiver. Novel camera- based imaging methods were developed and applied to both laboratory-scale and larger 1 MWt on-sun tests at the National Solar Thermal Test Facility in Albuquerque, New Mexico. Validation of the imaging methods was performed using gravimetric and calorimetric methods. In addition, conventional particle-sampling methods using volumetric particle-air samplers were applied to the on-sun tests to compare particle emission rates with regulatory standards for worker safety and pollution. Novel particle sampling methods using 3-D printed tipping buckets and tethered balloons were also developed and applied to the on-sun particle-receiver tests. Finally, models were developed to simulate the impact of particle size and wind on particle emissions and concentrations as a function of location. Results showed that particle emissions and concentrations were well below regulatory standards for worker safety and pollution. In addition, estimated particle temperatures and advective heat losses from the camera-based imaging methods correlated well with measured values during the on-sun tests.
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Applied Mathematical Modelling
An integrated modeling approach has been developed to better understand the relative impacts of different expiratory and environmental factors on airborne pathogen transport and transmission, motivated by the recent COVID-19 pandemic. Computational fluid dynamics (CFD) modeling was used to simulate spatial-temporal aerosol concentrations and quantified risks of exposure as a function of separation distance, exposure duration, environmental conditions (e.g., airflow/ventilation), and face coverings. The CFD results were combined with infectivity models to determine probability of infection, which is a function of the spatial-temporal aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate. Uncertainty distributions were determined for these parameters from the literature. Probabilistic analyses were performed to determine cumulative distributions of infection probabilities and to determine the most important parameters impacting transmission. This modeling approach has relevance to both pathogen and pollutant dispersion from expelled aerosol plumes.
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