Innovative electric propulsion thruster modeling

The objective of this program is to model and evaluate advanced nuclear electric propulsion (NEP) system concepts as an aid to the performance of NEP mission benefits studies. The two primary goals are as follows: (1) provide scaling relationships for mass, power, and efficiency, as functions of Isp, propellant type, and other important quantities. The discussion is presented in vugraph form

Advanced propulsion concepts

A variety of Advanced Propulsion Concepts (APC) is discussed. The focus is on those concepts that are sufficiently near-term that they could be developed for the Space Exploration Initiative. High-power (multi-megawatt) electric propulsion, solar sails, tethers, and extraterrestrial resource utilization concepts are discussed. A summary of these concepts and some general conclusions on their technology development needs are presented

An Upper Bound on High Speed Satellite Collision Probability When Only One Object has Position Uncertainty Information

Upper bounds on high speed satellite collision probability, PC , have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum PC. If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but potentially useful Pc upper bound

An Upper Bound on Orbital Debris Collision Probability When Only One Object has Position Uncertainty Information

Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do no good because the analyst defaults to no knowledge of the combined, relative position error covariance matrix. It is reasonable to think, given an assumption of no covariance information, an analyst might still attempt to determine the error covariance matrix that results in an upper bound on the P (sub c). Without some guidance on limits to the shape, size and orientation of the unknown covariance matrix, the limiting case is a degenerate ellipse lying along the relative miss vector in the collision plane. Unless the miss position is exceptionally large or the at-risk object is exceptionally small, this method results in a maximum P (sub c) too large to be of practical use. For example, assuming that the miss distance is equal to the current ISS alert volume along-track (+ or -) distance of 25 kilometers and that the at-risk area has a 70 meter radius. The maximum (degenerate ellipse) P (sub c) is about 0.00136. At 40 kilometers, the maximum P (sub c) would be 0.00085 which is still almost an order of magnitude larger than the ISS maneuver threshold of 0.0001. In fact, a miss distance of almost 340 kilometers is necessary to reduce the maximum P (sub c) associated with this degenerate ellipse to the ISS maneuver threshold value. Such a result is frequently of no practical value to the analyst. Some improvement may be made with respect to this problem by realizing that while the position error covariance matrix of one of the objects (usually the debris object) may not be known the position error covariance matrix of the other object (usually the asset) is almost always available. Making use of the position error covariance information for the one object provides an improvement in finding a maximum P (sub c) which, in some cases, may offer real utility. The equations to be used are presented and their use discussed

An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method

State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem

An Empirical State Error Covariance Matrix for Batch State Estimation

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors)

Pioglitazone Prevents Capillary Rarefaction in Streptozotocin-Diabetic Rats Independently of Glucose Control and Vascular Endothelial Growth Factor Expression

Background/Aims: Reduction of capillary network density occurs early in the development of metabolic syndrome and may be relevant for the precipitation of diabetes. Agonists of the peroxisome proliferator-activated receptor (PPAR)-gamma transcription factor are vasculoprotective, but their capacity for structural preservation of the microcirculation is unclear. Methods: Male Wistar rats were rendered diabetic by streptozotocin and treated with pioglitazone in chow for up to 12 weeks. Capillary density was determined in heart and skeletal muscle after platelet endothelial cell adhesion molecule-1 (PECAM-1) immunostaining. Hallmarks of apoptosis and angiogenesis were determined. Results: Capillary density deteriorated progressively in the presence of hyperglycemia (from 971/mm(2) to 475/mm(2) in quadriceps muscle during 13 weeks). Pioglitazone did not influence plasma glucose, left ventricular weight, or body weight but nearly doubled absolute and relative capillary densities compared to untreated controls (1.2 vs. 0.6 capillaries/myocyte in heart and 1.5 vs. 0.9 capillaries/myocyte in quadriceps muscle) after 13 weeks of diabetes. No antiapoptotic or angiogenic influence of pioglitazone was detected while a reduced expression of hypoxia-inducible factor-3 alpha and PPAR coactivator-1 alpha (PGC-1 alpha) mRNA as well as vascular endothelial growth factor (VEGF) protein possibly occurred as a consequence of improved vascularization. Conclusion: Pioglitazone preserves microvascular structure in diabetes independently of improvements in glycemic control and by a mechanism unrelated to VEGF-mediated angiogenesis. Copyright (C) 2012 S. Karger AG, Base

Fatty acid nitroalkenes ameliorate glucose intolerance and pulmonary hypertension in high-fat diet-induced obesity

Aims Obesity is a risk factor for diabetes and cardiovascular diseases, with the incidence of these disorders becoming epidemic. Pathogenic responses to obesity have been ascribed to adipose tissue (AT) dysfunction that promotes bioactive mediator secretion from visceral AT and the initiation of pro-inflammatory events that induce oxidative stress and tissue dysfunction. Current understanding supports that suppressing pro-inflammatory and oxidative events promotes improved metabolic and cardiovascular function. In this regard, electrophilic nitro-fatty acids display pleiotropic anti-inflammatory signalling actions. Methods and results It was hypothesized that high-fat diet (HFD)-induced inflammatory and metabolic responses, manifested by loss of glucose tolerance and vascular dysfunction, would be attenuated by systemic administration of nitrooctadecenoic acid (OA-NO2). Male C57BL/6j mice subjected to a HFD for 20 weeks displayed increased adiposity, fasting glucose, and insulin levels, which led to glucose intolerance and pulmonary hypertension, characterized by increased right ventricular (RV) end-systolic pressure (RVESP) and pulmonary vascular resistance (PVR). This was associated with increased lung xanthine oxidoreductase (XO) activity, macrophage infiltration, and enhanced expression of pro-inflammatory cytokines. Left ventricular (LV) end-diastolic pressure remained unaltered, indicating that the HFD produces pulmonary vascular remodelling, rather than LV dysfunction and pulmonary venous hypertension. Administration of OA-NO2 for the final 6.5 weeks of HFD improved glucose tolerance and significantly attenuated HFD-induced RVESP, PVR, RV hypertrophy, lung XO activity, oxidative stress, and pro-inflammatory pulmonary cytokine levels. Conclusions These observations support that the pleiotropic signalling actions of electrophilic fatty acids represent a therapeutic strategy for limiting the complex pathogenic responses instigated by obesity.Fil: Kelley, Eric E.. University of Pittsburgh; Estados UnidosFil: Baust, Jeff. University of Pittsburgh; Estados UnidosFil: Bonacci, Gustavo Roberto. University of Pittsburgh; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Golin Bisello, Franca. University of Pittsburgh; Estados UnidosFil: Devlin, Jason E.. University of Pittsburgh; Estados UnidosFil: Croix, Claudette M. St.. University of Pittsburgh; Estados UnidosFil: Watkins, Simon C.. University of Pittsburgh; Estados UnidosFil: Gor, Sonia. University of Pittsburgh; Estados UnidosFil: Cantu Medellin, Nadiezhda. University of Pittsburgh; Estados UnidosFil: Weidert, Eric R.. University of Pittsburgh; Estados UnidosFil: Frisbee,Jefferson C.. University of Virginia; Estados UnidosFil: Gladwin, Mark T.. University of Pittsburgh; Estados UnidosFil: Champion, Hunter C.. University of Pittsburgh; Estados UnidosFil: Freeman, Bruce A.. University of Pittsburgh; Estados UnidosFil: Khoo, Nicholas K.H.. University of Pittsburgh; Estados Unido