Response of a small-turboshaft-engine compression system to inlet temperature distortion

An experimental investigation was conducted into the response of a small-turboshaft-engine compression system to steady-state and transient inlet temperature distortions. Transient temperature ramps range from less than 100 K/sec to above 610 K/sec and generated instantaneous temperatures to 420 K above ambient. Steady-state temperature distortion levels were limited by the engine hardware temperature list. Simple analysis of the steady-state distortion data indicated that a particle separator at the engine inlet permitted higher levels of temperature distortion before onset of compressor surge than would be expected without the separator

Nuclear design and experiments for a space power reactor

Design and experiments with compact fast spectrum reactor for generating electric power in spac

Assessment of alternative power sources for mobile mining machinery

Alternative mobile power sources for mining applications were assessed. A wide variety of heat engines and energy systems was examined as potential alternatives to presently used power systems. The present mobile power systems are electrical trailing cable, electrical battery, and diesel - with diesel being largely limited in the United States to noncoal mines. Each candidate power source was evaluated for the following requirements: (1) ability to achieve the duty cycle; (2) ability to meet Government regulations; (3) availability (production readiness); (4) market availability; and (5) packaging capability. Screening reduced the list of candidates to the following power sources: diesel, stirling, gas turbine, rankine (steam), advanced electric (batteries), mechanical energy storage (flywheel), and use of hydrogen evolved from metal hydrides. This list of candidates is divided into two classes of alternative power sources for mining applications, heat engines and energy storage systems

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to ‘phenotype the population’ seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services

Experimental study of ceramic coated tip seals for turbojet engines

Ceramic gas-path seals were fabricated and successfully operated over 1000 cycles from flight idle to maximum power in a small turboshaft engine. The seals were fabricated by plasma spraying zirconia over a NiCoCrAlX bond boat on the Haynes 25 substrate. Coolant-side substrate temperatures and related engine parameters were recorded. Post-test inspection revealed mudflat surface cracking with penetration to the ceramic bond-coat interface

Characteristics of Five Ejector Configurations at Free-Stream Mach Numbers from 0 to 2.0

Thrust, air-handling, and base-pressure characteristics of five ejector configurations were investigated in the Lewis 8-by 6-foot wind tunnel at free-stream Mach numbers from 0 to 2.0 over ranges of primary-jet pressure ratio up to 24 and corrected secondary weight-flow ratio up to 13 percent. The ejector-shroud geometries varied from convergent to divergent. Base pressure ratio and ejector performance were interrelated by means of an exit-momentum parameter. Correlations, to at least a first approximation, with base pressure ratio, of both internal-ejector-flow separation and external-flow separation over the model boattail were shown. Furthermore, it was shown that magnitudes and exact trends in base pressure ratio depended largely, and in a complicated fashion, on ejector geometry and amount of secondary flow. External-stream effects on ejector jet thrust were determined for a typical schedule of jet-engine pressure ratios. With the exception of the ejector having the largest (1.81) shroud-exit-to primary-diameter ratio, there were no stream effects at Mach numbers from 1.5 to 2.0 and variations from quiescent-air thrust data were less than 2.5 percent at the subsonic speed investigated

An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data

Indiana University-Purdue University Indianapolis (IUPUI)Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is available it is frequently incorrect. Therefore it is important to look beyond traditional guideline-based decision support to more readily available resources in order to bring this technology into widespread use. This study proposes that the wisdom of physicians within a practice is a rich, untapped knowledge source that can be harnessed for this purpose. I hypothesize and demonstrate that this wisdom is reflected by order entry data well enough to partially reconstruct the knowledge behind treatment decisions. Automated reconstruction of such knowledge is used to produce dynamic, situation-specific treatment suggestions, in a similar vein to Amazon.com shopping recommendations. This approach is appealing because: it is local (so it reflects local standards); it fits into workflow more readily than the traditional local-wisdom approach (viz. the curbside consult); and, it is free (the data are already being captured). This work develops several new machine-learning algorithms and novel applications of existing algorithms, focusing on an approach called Bayesian network structure learning. I develop: an approach to produce dynamic, rank-ordered situation-specific treatment menus from treatment data; statistical machinery to evaluate their accuracy using retrospective simulation; a novel algorithm which is an order of magnitude faster than existing algorithms; a principled approach to choosing smaller, more optimal, domain-specific subsystems; and a new method to discover temporal relationships in the data. The result is a comprehensive approach for extracting knowledge from order-entry data to produce situation-specific treatment menus, which is applied to order-entry data at Wishard Hospital in Indianapolis. Retrospective simulations find that, in a large variety of clinical situations, a short menu will contain the clinicians' desired next actions. A prospective survey additionally finds that such menus aid physicians in writing order sets (in completeness and speed). This study demonstrates that clinical knowledge can be successfully extracted from treatment data for decision support

Coarse-grained brownian dynamics simulation of rule-based models

International audienceStudying spatial effects in signal transduction, such as co-localization along scaffold molecules, comes at a cost of complexity. In this paper, we propose a coarse-grained, particle-based spatial simulator, suited for large signal transduction models. Our approach is to combine the particle-based reaction and diffusion method, and (non-spatial) rule-based modeling: the location of each molecular complex is abstracted by a spheric particle, while its internal structure in terms of a site-graph is maintained explicit. The particles diffuse inside the cellular compartment and the colliding complexes stochastically interact according to a rule-based scheme. Since rules operate over molecular motifs (instead of full complexes), the rule set compactly describes a combinatorial or even infinite number of reactions. The method is tested on a model of Mitogen Activated Protein Kinase (MAPK) cascade of yeast pheromone response signaling. Results demonstrate that the molecules of the MAPK cascade co-localize along scaffold molecules, while the scaffold binds to a plasma membrane bound upstream component, localizing the whole signaling complex to the plasma membrane. Especially we show, how rings stabilize the resulting molecular complexes and derive the effective dissociation rate constant for it