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The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts.

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The microstructure of impact ice has been shown to correlate with the adhesion strength of ice. Machine vision techniques are explored as a method of decreasing analysis time. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods.

Artificial ice shapes of various geometric fidelity were tested on a wing model based on the Common Research Model. Low Reynolds number tests were conducted at Wichita State University’s Walter H. Beech Memorial Wind Tunnel utilizing an 8.9% scale model, and high Reynolds number tests were conducted at ONERA’s F1 wind tunnel utilizing a 13.3% scale model. Several identical geometrically-scaled ice shapes were tested at both facilities, and the results were compared at overlapping Reynolds and Mach numbers. This was to ensure that the results and trends observed at low Reynolds number could be applied and continued to high, near-flight Reynolds number. The data from Wichita State University and ONERA F1 agreed well at matched Reynolds and Mach numbers. The lift and pitching moment curves agreed very well for most configurations.

Understanding the aerodynamic impact of swept-wing ice accretions is a crucial component of the design of modern aircraft. Computer-simulation tools are commonly used to approximate ice shapes, so the necessary level of detail or fidelity of those simulated ice shapes must be understood relative to high-fidelity representations of the ice. Previous tests were performed in the NASA Icing Research Tunnel to acquire high-fidelity ice shapes. From this database, full-span artificial ice shapes were designed and manufactured for both an 8.9%-scale and 13.3%-scale semispan wing model of the CRM65 which has been established as the full-scale baseline for this swept-wing project. These models were tested in the Walter H. Beech wind tunnel at Wichita State University and at the ONERA F1 facility, respectively. The data collected in the Wichita St.

From November 2010 until May of 2011, NASA's Icing Remote Sensing System was positioned at Platteville, Colorado between the National Science Foundation's S-Pol radar and Colorado State University's CHILL radar (collectively known as FRONT, or ‘Front Range Observational Network Testbed’). This location was also underneath the flight-path of aircraft arriving and departing from Denver's International Airport, which allowed for comparison to pilot reports of in-flight icing. This work outlines how the NASA Icing Remote Sensing System's derived liquid water content and in-flight icing hazard profiles can be used to provide in-flight icing verification and validation during icing and non-icing scenarios with the purpose of comparing these times to profiles of polarized moment data from the two nearby research radars.

JSC-1A lunar simulant has been applied to AZ93 and AgFEP thermal control surfaces on aluminum substrates in a simulated lunar environment. The temperature of these surfaces was monitored as they were heated with a solar simulator using varying angles of incidence and cooled in a 30 K coldbox. Thermal modeling was used to determine the solar absorptivity (a) and infrared emissivity (e) of the thermal control surfaces in both their clean and dusted states. It was found that even a sub-monolayer of dust can significantly raise the α of either type of surface. A full monolayer can increase the α/ε ratio by a factor of 3–4 over a clean surface. Little angular dependence of the α of pristine thermal control surfaces for both AZ93 and AgFEP was observed, at least until 30° from the surface. The dusted surfaces showed the most angular dependence of α when the incidence angle was in the range of 25° to 35°.

A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

Development of new air revitalization system (ARS) technology can initially be performed in a subscale laboratory environment, but in order to advance the maturity level, the technology must be tested in an end-to-end integrated environment. The Air Revitalization Technology Evaluation Facility (ARTEF) at the NASA Johnson Space Center (JSC) serves as a ground test bed for evaluating emerging ARS technologies in an environment representative of spacecraft atmospheres. At the center of the ARTEF is a hypobaric chamber which serves as a sealed atmospheric chamber for closed loop testing. A Human Metabolic Simulator (HMS) was custom-built to simulate the consumption of oxygen, and production of carbon dioxide, moisture and heat by up to eight persons. A variety of gas analyzers and dew point sensors are used to monitor the chamber atmosphere and the process flow upstream and downstream of a test article. A robust vacuum system is needed to simulate the vacuum of space.

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA's Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.

Smoke transport and detection were modeled numerically in the ISS Destiny module using the NIST, Fire Dynamics Simulator code. The airflows in Destiny were modeled using the existing flow conditions and the module geometry included obstructions that simulate the currently installed hardware on orbit. The smoke source was modeled as a 0.152 by 0.152 m region that emitted smoke particulate ranging from 1.46 to 8.47 mg/s. In the module domain, the smoke source was placed in the center of each Destiny rack location and the model was run to determine the time required for the two smoke detectors to alarm. Overall the detection times were dominated by the circumferential flow, the axial flow from the intermodule ventilation and the smoke source strength.

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. The DLN has created a series of live education videoconferences connecting NASA's Extreme Environment Missions Operations (NEEMO) team to students across the United States. Programs are also extended to students around the world via live webcasting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO crewmembers, including NASA astronauts, engineers and scientists, inform and inspire students about the importance of exploration and share the importance of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration's underwater laboratory located 4.5 kilometers off Key Largo in the Florida Keys National Marine Sanctuary.

The wonder of space exploration is a sure way to catch the attention of students of all ages, and space biology is one of many sciences critical to understanding the spaceflight environment. Many systems used in the past for space-to-classroom biology activities have required extensive crew time and material resources, making space-linked education logistically and financially difficult. The new Education Payload Operations Kit C (EPO Kit C) aims to overcome obstacles to space-linked education and outreach by dramatically reducing the resources required for educational activities in plant space biology that have a true spaceflight component. EPO Kit C is expected to be flown from STS-118 to the International Space Station in June 2007. NASA and several other organizations are currently planning an outreach program to complement the flight of EPO Kit C.

The Vision for Space Exploration outlines NASA's goals to return to the Moon, and travel on to Mars. The exploration activities associated with these endeavors will include both space and surface extravehicular activities (EVAs). This paper describes the plans for education outreach activities and products related to the technological developments and challenges similar to those being addressed by the Advanced EVA (AEVA) team. Efforts to involve and coordinate educational research projects with the AEVA team will also be discussed. The proposed activities and products will provide hands-on, interactive exercises through workshops, presentations, and demonstrations to allow students of all levels to learn about and experience the design challenges similar to what NASA deals with everyday in developing EVA systems.

With a renewed focus on manned exploration, NASA is beginning to prepare for the challenges that lie ahead. Future manned missions will require a symbiosis of human and robotic infrastructure. As a step towards understanding the roles of humans and robots in future planetary exploration, NASA headquarters funded ILC Dover and the University of Maryland to perform research in the area of human and robotic interfaces. The research focused on development and testing of communication components, robotic command and control interfaces, electronic displays, EVA navigation software and hardware, and EVA lighting. The funded research was a 12-month effort culminating in a field test with NASA personnel.

In planning for Exploration missions and developing the required suite of environmental monitors, the difficulty lies in down-selecting a multitude of technology options to a few candidates with exceptional potential. Technology selection criteria include conventional analytical parameters (e.g., range, sensitivity, selectivity), operational factors (degree of automation, portability, required level of crew training, maintenance), logistical factors (size, mass, power, consumables, waste generation) and engineering factors such as complexity and reliability. Other more subtle considerations include crew interfaces, data readout and degree of autonomy from the ground control center. We anticipate that technology demonstrations designed toward these goals will be carried out on the International Space Station, the end result of which is a suite of techniques well positioned for deployment during Exploration missions.

The Advanced Integration Matrix (AIM) will design a ground-based test facility for developing revolutionary integrated systems for joint human-robotic missions in order to study and solve systems-level integration issues for exploration missions beyond Low Earth Orbit (LEO). This paper describes development plans for educational outreach activities related to technological and operational integration scenarios similar to the challenges that will be encountered through this project. The education outreach activities will provide hands-on, interactive exercises to allow students of all levels to experience design and operational challenges similar to what NASA deals with everyday in performing the integration of complex missions. These experiences will relate to and impact students' everyday lives by demonstrating how their interests in science and engineering can develop into future careers, and reinforcing the concepts of teamwork and conflict resolution.

To support NASA's goal to improve aviation safety, the Aircraft Icing Project of the Aviation Safety Program has developed a number of education and training aids for pilots and operators on the hazards of atmospheric icing. A review of aircraft incident and accident investigations has revealed that flight crews have not always understood the effects of ice contamination on their aircraft. To increase this awareness, NASA has partnered with regulatory agencies and pilot trade organizations to assure relevant and practical materials that are focused toward the intended pilot audience. A number of new instructional design approaches and media delivery methods have been introduced to increase the effectiveness of the training materials by enhancing the learning experience, expanding user interactivity and participation, and, hopefully, increasing learner retention rates.

The airworthiness authorities (FAA, JAA, Transport Canada) will be releasing a draft rule in the 2006 timeframe concerning the operation of aircraft in a Supercooled Large Droplet (SLD) environment aloft. The draft rule will require aircraft manufacturers to demonstrate that their aircraft can operate safely in an SLD environment for a period of time to facilitate a safe exit from the condition. It is anticipated that aircraft manufacturers will require a capability to demonstrate compliance with this rule via experimental means (icing tunnels or tankers) and by analytical means (ice prediction codes). Since existing icing research facilities and analytical codes were not developed to account for SLD conditions, current engineering tools are not adequate to support compliance activities in SLD conditions. Therefore, existing capabilities need to be augmented to include SLD conditions.

NASA is developing and validating technology to incorporate aircraft icing effects into a flight training device concept demonstrator. Flight simulation models of a DHC-6 Twin Otter were developed from wind tunnel data using a subscale, complete aircraft model with and without simulated ice, and from previously acquired flight data. The validation of the simulation models required additional aircraft response time histories of the airplane configured with simulated ice similar to the subscale model testing. Therefore, a flight test was conducted using the NASA Twin Otter Icing Research Aircraft. Over 500 maneuvers of various types were conducted in this flight test. The validation data consisted of aircraft state parameters, pilot inputs, propulsion, weight, center of gravity, and moments of inertia with the airplane configured with different amounts of simulated ice.

A high-fidelity simulation model for icing effects flight training was developed from wind tunnel data for the DeHavilland DHC-6 Twin Otter aircraft. First, a flight model of the un-iced airplane was developed and then modifications were generated to model the icing conditions. The models were validated against data records from the NASA Twin Otter Icing Research flight test program with only minimal refinements being required. The goals of this program were to demonstrate the effectiveness of such a simulator for training pilots to recognize and recover from icing situations and to establish a process for modeling icing effects to be used for future training devices.