Overview of HLLV effluents in stratosphere and above

The injection of large quantities of rocket exhaust effluent into the upper atmosphere by the heavy lift launch vehicle (HLLV) of the solar power satellite system is discussed. The exhaust products considered are CO, CO2, H2O, H2, and NO. The effects on the composition of the atmosphere at different altitudes are estimated

Ground cloud air quality effects

The effects of the ground cloud associated with launching of a large rocket on air quality are discussed. The ground cloud consists of the exhaust emitted by the rocket during the first 15 to 25 seconds following ignition and liftoff, together with a large quantity of entrained air, cooling water, dust and other debris. Immediately after formation, the ground cloud rises in the air due to the buoyant effect of its high thermal energy content. Eventually, at an altitude typically between 0.7 and 3 km, the cloud stabilizes and is carried along by the prevailing wind at that altitude. For the use of heavy lift launch vehicles small quantities of nitrogen oxides, primarily nitric oxide and nitrogen dioxide, are expected to be produced from a molecular nitrogen impurity in the fuel or liquid oxygen, or from entrainment and heating of ambient air in the hot rocket exhaust. In addition, possible impurities such as sulfur in the fuel would give rise to a corresponding amount of oxidation products such as sulfur dioxide

A fast algorithm for control and estimation using a polynomial state-space structure

One of the major problems associated with the control of flexible structures is the estimation of system states. Since the parameters of the structures are not constant under varying loads and conditions, conventional fixed parameter state estimators can not be used to effectively estimate the states of the system. One alternative is to use a state estimator which adapts to the condition of the system. One such estimator is the Kalman filter. This filter is a time varying recursive digital filter which is based upon a model of the system being measured. This filter adapts the model according to the output of the system. Previously, the Kalman filter has only been used in an off-line capacity due to the computational time required for implementation. With recent advances in computer technology, it is becoming a viable tool for use in the on-line environment. A distributed Kalman filter implementation is described for fast estimation of the state of a flexible arm. A key issue, is the sensor structure and initial work on a distributed sensor that could be used with the Kalman filter is presented

Computational tools for multi-linked flexible structures

A software module which designs and tests controllers and filters in Kalman Estimator form, based on a polynomial state-space model is discussed. The user-friendly program employs an interactive graphics approach to simplify the design process. A variety of input methods are provided to test the effectiveness of the estimator. Utilities are provided which address important issues in filter design such as graphical analysis, statistical analysis, and calculation time. The program also provides the user with the ability to save filter parameters, inputs, and outputs for future use