Cold Flow Testing of a Modified Subscale Model Exhaust System for a Space Based Laser

The aim of this research was a continued study of gas-dynamic phenomena that occurred in a set of stacked nozzles as reported by Captains Ian Bautista in 2003 and Scott Bergren in 2002. The arrangement of the stacked nozzles was a modified version of a 1/5th scale-model of one quadrant of the conceptual Space Based Laser Integrated Flight Experiment (SBL IFX) gas dynamic laser. Rather than cylindrical rings of nozzles, the stacked nozzles were flat and able to be rotated about a vector normal to the nozzle exits. This set of stacked flat nozzles was installed on a blow-down/vacuum wind tunnel, which in addition to the nozzles, consisted of a stilling chamber, centerbody, supersonic diffuser, and transition structure to join the vacuum and test sections. The goals of this research were two fold; first, modify the original scale-model of the stacked cylindrical rings of nozzles so schlieren photography could be used to visualize an average flow field across the nozzles. Secondly, using the schlieren photographs, in conjunction with pressure data, observe the interactions between the individual nozzles. Results have shown that the modified nozzle array produces a vastly complex flow field as well as a highly supersonic flow regime, with Mach numbers that reach as high as 5.6

San Joaquin Air Quality and Active Transportation Specific Plan

The focus of this project is to improve the overall air quality in San Joaquin. It will specifically focus on the PM2.5 pollutant, as this pollutant has the highest potential for change. Additionally, the PM2.5 pollutant poses major health risks to the community when there are high levels of it. The primary contributors of PM2.5 are automobiles, geography, and weather. This project will discuss methods that can reduce the reliance on single occupancy vehicles. These methods include the development of pedestrian and bicycle infrastructure as well as the improvement of the area’s regional transport services

A High-Fidelity Ground-Based Space Environment Testbed Utilizing Magnetic Levitation for Fully Integrated Small Spacecraft

Orbital debris has been, and will continue to be an issue for all space-bound systems and technologies. Small spacecraft are an increasing contribution to this issue with high rates of on-orbit failures. Developing a ground-based planar testbed with a near frictionless, vacuum environment for small spacecraft allows for higher fidelity, 3 degree of freedom (3DOF) testing of fully integrated propulsion systems and control algorithms prior to launch. Ultimately, this provides empirical data at a relatively low cost that can be used to reduce risk of on-orbit failures thereby mitigating contributions of small spacecraft to the growing orbital debris issue. A team at NASA’s White Sands Test Facility (WSTF) is investigating the use of high temperature super conducting (HTSC) materials to induce magnetic levitation of a small spacecraft, or a constellation of small spacecraft, to provide this 3 DOF testbed within one of WSTF’s existing space environment simulation chambers

Monitoring wader breeding productivity

A robust understanding of the mechanisms driving demographic change in wild animal populations is fundamental to the delivery of effective conservation interventions. Demographic change can be driven by variation in adult survival, recruitment of juveniles into the breeding population or breeding productivity – the number of fledglings produced per breeding pair. Across Europe, low breeding productivity in wader populations has been a significant driver of population decline, increasing the importance of gathering accurate data on breeding productivity. Monitoring wader breeding productivity is challenging because finding nests can be time‐consuming and requires experienced fieldworkers; wader chicks are nidifugous and difficult to count due to their cryptic behaviour; and waders often have high re‐laying rates following nest failure, meaning that hatching or fledging can be highly asynchronous. This paper reviews approaches to estimating breeding productivity where fieldworkers either record the agitation or alarm‐calling behaviour of adults with dependent young, make direct observations of broods on survey visits, or both. Using a systematic literature search (restricted to Europe where most of these studies have taken place) we identified 38 peer‐reviewed papers which used this approach. The productivity metrics produced can be divided into the following categories: (i) ‘Hatching Success’ (HS), (ii) ‘Fledging Success’ (FS) and (iii) ‘Young Fledged Per Pair’ (YFP), from the coarsest to the most precise. The first two metrics are most often used when direct observations of broods are not possible due to the behaviour of broods or vegetation structure; YFP is preferred if brood counts are possible. Design of an appropriate metric depends on (i) whether accurate brood counts are possible; (ii) whether adults exhibit diagnostic agitation behaviour when young are present; (iii) whether individual breeding territories are separable; (iv) whether re‐nesting rates are assumed to be high; and (v) the availability of experienced surveyors (particularly where behavioural observations are required). Globally there are many wader species for which the methods described here could provide valuable information and we hope this review encourages further development or adoption of these methods

Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

Oral agent offers relief from generalized hyperhidrosis

Hyperhidrosis is a common, self-limiting problem affecting 2% to 3% of the population in the United States. Patients may complain of localized sweating of the hands, feet, face, or underarms or more systemic, generalized sweating in multiple locations. Either way, patients always note a significant impact on their quality of life. Treatment of hyperhidrosis has traditionally focused on topical therapies to the affected areas. The availability of low-cost prescription and over-the-counter aluminum-based antiperspirant agents makes topicals the first-line choice

Transfer Function Modeling of Processes with Dynamic Inputs

Time series structures, which are common occurrences with data in many industrial processes, complicate a quality practitioner\u27s efforts to accurately position control chart limits. ARIMA modeling and a variety of control charting methods have been recommended for monitoring process data with a time series structure. Estimates of ARIMA model parameters may not be reliable, however, if assignable causes of variation are present in the process data used to fit the time series model. Control limits may also be misplaced if the process inputs are dynamic and exhibiting a time series structure. Our purpose in this paper is to explore the ability of a transfer function model to identify assignable causes of variation and to model dynamic relationships between process inputs and outputs. A transfer function model is developed to monitor biochemical oxygen demand output from a wastewater treatment process, a process with dynamic inputs. This model is used to identify periods of disturbance to the wastewater process and to capture the relationship between the variable nature of the input to the process and the resulting output. Simulation results are included in this study to measure the sensitivity of transfer function models and to highlight conditions where transfer function modeling is critical