A method for calculating a real-gas two-dimensional nozzle contour including the effects of gamma

A method for calculating two-dimensional inviscid nozzle contours for a real gas or an ideal gas by the method of characteristics is described. The method consists of a modification of an existing nozzle computer program. The ideal-gas nozzle contour can be calculated for any constant value of gamma. Two methods of calculating the center-line boundary values of the Mach number in the throat region are also presented. The use of these three methods of calculating the center-line Mach number distribution in the throat region can change the distance from the throat to the inflection point by a factor of 2.5. A user's guide is presented for input to the computer program for both the two-dimensional and axisymmetric nozzle contours

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Supersonic wind tunnel nozzles: A selected, annotated bibliography to aid in the development of quiet wind tunnel technology

This bibliography, with abstracts, consists of 298 citations arranged in chronological order. The citations were selected to be helpful to persons engaged in the design and development of quiet (low disturbance) nozzles for modern supersonic wind tunnels. Author, subject, and corporate source indexes are included to assist with the location of specific information

Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups

Seasonal Road Layout Design In Mountainous Terrain Using GIS With The Side Hill And Least Cost Path Methods

Seasonal road design in mountainous terrain consists of four main phases: route selection, field investigation, surveying, and analysis. The first phase, route selection, consists of two parts: selecting control points at strategic locations, and then determining potential routes between those points. Two geographic information system (GIS) geoprocessing automations were developed to aid a road planner in determining routes between control points. Both automations utilized Environmental Systems Research Institute’s (ESRI) ArcGIS software package. The first method developed was the least cost path method, which makes use of ArcGIS’s cost path tool to find a route between points following a gradual slope. The second automation was the side hill method, which utilized a variety of ArcGIS tools to maintain a uniform grade along the side of a hill between two points. The two methods were compared and contrasted based on control point locations. The least cost path method was determined to be preferable for main thoroughfares along flat valleys and ridge lines, while the side hill method was preferable for secondary roads that could be used to access steeper ground. It was concluded that the two methods can save time and increase accuracy of GIS road features for land managers planning new seasonal roads

Search methods for an autonomous underwater vehicle using scalar measurements

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution July 1996The continuing development of the autonomous underwater vehicle as an oceanographic research tool has opened up the realm of scientific possibility in the field of deep ocean research. The ability of a vehicle to travel to the ocean floor untethered, collect data for an extended period of time and return to the surface for recovery can make precise oceanographic surveying more economically practical and more efficient. This thesis investigates several scalar parameter searching techniques which have their basis in mathematical optimization algorithms and their applicability for use specifically within the context of autonomous underwater vehicle dynamics. In particular, a modified version of the circular gradient evaluation in the simulated environment of a hydrothermal plume is examined as a test case. Using a priori knowledge of the expected structure of the scalar parameter contour is shown to be advantageous in optimizing the search

Kinematic Modeling of the Determinants of Diastolic Function

Multiple modalities are routinely used in clinical cardiology to determine cardiovascular function, and many of the indexes derived from these modalities are causally interconnected. A correlative approach to cardiovascular function however, where indexes are correlated to disease presence and progression, fails to fully capitalize on the information content of the indexes. Causal quantitative modeling of cardiovascular physiology on the other hand offers a predictive rather than accommodative approach to cardiovascular function determination. In this work we apply a kinematic modeling approach to understanding diastolic function. We discuss novel insights related to the physiological determinants of diastolic function, and define novel causal indexes of diastolic function that go beyond the limitations of current established clinical indexes. Diastolic function is typically characterized by physiologists and cardiologists as being determined by the interplay between chamber stiffness, chamber relaxation/viscoelasticity, and chamber filling volume or load. In this work we provide kinematic modeling based analysis of each of these clinical diastolic function determinants. Considering the kinematic elastic (stiffness) components of filling, we argue for the universality of diastolic suction and define a novel in-vivo equilibrium volume. Application of this novel equilibrium volume in the clinical setting results in a novel approach to determination of global chamber stiffness. Considering the viscoelastic components of filling, we demonstrate the limitations associated with ignoring viscoelastic effects, an assumption often made in the clinical setting. We extend the viscoelastic component of filling into the invasive hemodynamic domain, and demonstrate the causal link between invasively recorded LV pressure and noninvasively recorded transmitral flow by describing a method for extracting flow contours from pressure signals alone. Finally, in considering load, we solve the problem of load dependence in diastolic function analysis. Indeed all traditional clinical indexes of diastolic function are load dependent, and therefore are imperfect indexes of intrinsic diastolic function. Applying kinematic modeling, we derive a load independent index of diastolic function. Validation involves showing that the index is indeed load-independent and can differentiate between control and diastolic dysfunction states. We apply this novel analysis to derive surrogates for filling pressure, and generalize the kinematic modeling approach to the analysis of isovolumic relaxation. To aid widespread adoption of the load independent index, we derive and validate simplified expressions for model-based physiological parameters of diastolic function. Our goal is to provide a causal approach to cardiovascular function analysis based on how things move, to explain prior phenomenological observations of others under a single causal paradigm, to discover `new physiology\u27, facilitate the discovery of more robust indexes of cardiovascular function, and provide a means for widespread adoption of the kinematic modeling approach suitable for the general clinical setting

Hyperbolastic type-III diffusion process: Obtaining from the generalized Weibull diffusion process

The modeling of growth phenomena has become a matter of great interest in many different fields of application and research. New stochastic models have been developed, and others have been updated to this end. The present paper introduces a diffusion process whose main characteristic is that its mean function belongs to a wide family of curves derived from the classic Weibull curve. The main characteristics of the process are described and, as a particular case, a di usion process is considered whose mean function is the hyperbolastic curve of type III, which has proven useful in the study of cell growth phenomena. By studying its estimation we are able to describe the behavior of such growth patterns. This work considers the problem of the maximum likelihood estimation of the parameters of the process, including strategies to obtain initial solutions for the system of equations that must be solved. Some examples are provided based on simulated sample paths and real data to illustrate the development carried out.This work was supported in part by the Ministerio de Economía, Industria y Competitividad, Spain, under Grant MTM2017-85568-P

Methods for Shape-Constrained Kernel Density Estimation

Nonparametric density estimators are used to estimate an unknown probability density while making minimal assumptions about its functional form. Although the low reliance of nonparametric estimators on modelling assumptions is a benefit, their performance will be improved if auxiliary information about the density\u27s shape is incorporated into the estimate. Auxiliary information can take the form of shape constraints, such as unimodality or symmetry, that the estimate must satisfy. Finding the constrained estimate is usually a difficult optimization problem, however, and a consistent framework for finding estimates across a variety of problems is lacking. It is proposed to find shape-constrained density estimates by starting with a pilot estimate obtained by standard methods, and subsequently adjusting its shape until the constraints are satisfied. This strategy is part of a general approach, in which a constrained estimation problem is defined by an estimator, a method of shape adjustment, a constraint, and an objective function. Optimization methods are developed to suit this approach, with a focus on kernel density estimation under a variety of constraints. Two methods of shape adjustment are examined in detail. The first is data sharpening, for which two optimization algorithms are proposed: a greedy algorithm that runs quickly but can handle a limited set of constraints, and a particle swarm algorithm that is suitable for a wider range of problems. The second is the method of adjustment curves, for which it is often possible to use quadratic programming to find optimal estimates. The methods presented here can be used for univariate or higher-dimensional kernel density estimation with shape constraints. They can also be extended to other estimators, in both the density estimation and regression settings. As such they constitute a step toward a truly general optimizer, that can be used on arbitrary combinations of estimator and constraint