Search Results

The development and characterization of an innovative approach for the control and elimination of gaseous toxins using single walled carbon nanotubes (SWNTs) promise superior performance over conventional approaches. This is due to the ability of the nanotubes to direct the selective uptake of gaseous species based on their controllable pore size, high adsorptive capacity and their effectiveness as catalyst supports for gaseous conversion. A metal impregnated SWNT material has been proposed and synthesized for removing and converting the toxins in the life support system.

The success of physico-chemical waste processing and resource recovery technologies for life support application depends partly on the ability of gas clean-up systems to efficiently remove trace contaminants generated during the process with minimal use of expendables. Highly purified metal-impregnated carbon nanotubes promise superior performance over conventional approaches to gas clean-up due to their ability to direct the selective uptake gaseous species based both on the nanotube’s controlled pore size, high surface area, and ordered chemical structure that allows functionalization and on the nanotube’s effectiveness as a catalyst support material for toxic contaminants removal. We present results on the purification of single walled carbon nanotubes (SWCNT) and efforts at metal impregnation of the SWCNT’s.

One of the major problems associated with solid waste processing technologies is effluent contaminants that are released in gaseous forms from the processes. This is a concern in both biological as well as physicochemical solid waste processing. Carbon dioxide (CO2), the major gas released, does not present a serious problem and there are currently in place a number of flight-qualified technologies for CO2 removal. However, a number of other gases, in particular NOx, SO2, NH3, and various hydrocarbons (e.g. CH4) do present health hazards to the crew members in space habitats. In the present configuration of solid waste processing in the International Space Station (ISS), some of these gases are removed by the Trace Contaminant Control System (TCCS), demands a major resupply. Reduction of the resupply can be effective by using catalyst impregnated carbon nanotubes. For example, NO decomposition to N2 and O2 is thermodynamically favored.

A common approach to the validation of simulation models focuses on validation throughout the entire design space. A more recent methodology validates designs as they are generated during a simulation-based optimization process. The latter method relies on validating the simulation model in a sequence of local domains. To improve its computational efficiency, this paper proposes an iterative process, where the size and shape of local domains at the current step are determined from a parametric bootstrap methodology involving maximum likelihood estimators of unknown model parameters from the previous step. Validation is carried out in the local domain at each step. The iterative process continues until the local domain does not change from iteration to iteration during the optimization process ensuring that a converged design optimum has been obtained.

Many studies show that under diesel engine operating conditions, SCR reductant sprays will impinge on the wall of exhaust pipes. In order to understand this impinging process of SCR reductant spray, and to analyze what factors affect it, a test bench was set up by means of high speed video camera. At atmospheric pressure, SCR spray was injected on a heated metal wall, the impacts of wall temperature, injection pressure, injection height and angle on developing characteristics of SCR reductant spray after impinging on the heated wall have been researched and analyzed. The results show that the heated wall temperature has a great impact on the spray developing process, when wall temperature is lower than 405K, after water evaporated the crystallized urea will remain on the wall to block exhaust pipes. When wall temperature is higher, the atomization and evaporation of SCR reductant spray will be better, and the hydrolysis process of urea will be faster.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.