Variable mixture ratio performance through nitrogen augmentation

High/variable mixture ratio O2/H2 candidate engine cycles are examined for earth-to-orbit vehicle application. Engine performance and power balance information are presented for the candidate cycles relative to chamber pressure, bulk density, and mixture ratio. Included in the cycle screening are concepts where a third fluid (liquid nitrogen) is used to achieve a variable mixture ratio over the trajectory from liftoff to earth orbit. The third fluid cycles offer a very low risk, fully reusable, low operation cost alternative to high/variable mixture ratio bipropellant cycles. Variable mixture ratio engines with extendible nozzle are slightly lower performing than a single mixture ratio engine (MR = 7:1) with extendible nozzle. Dual expander engines (MR = 7:1) have slightly better performance than the single mixture ratio engine. Dual fuel dual expander engines offer a 16 percent improvement over the single mixture ratio engine

Diminiode thermionic conversion with 111-iridium electrodes

Preliminary data indicating thermionic-conversion potentialities for a 111-iridium emitter and collector spaced 0.2 mm apart are presented. These results comprise output densities of current and of power as functions of voltage for three sets of emitter, collector, and reservoir temperatures: 1553, 944, 561 K; 1605, 898, 533 K; and 1656, 1028, 586 K. For the 1605 K evaluation, estimates produced work-function values of 2.22 eV for the emitter and 1.63 eV for the collector with a 2.0-eV barrier index (collector work function plus interelectrode voltage drop) corresponding to the maximum output of 5.5 W/sq cm at 0.24 volt. The current, voltage curve for the 1656 K 111-iridium diminiode yields a 6.2 W/sq cm maximum at 0.25 volt and is comparable with the 1700 K envelope for a diode with an etched-rhenium emitter and a 0.025-mm electrode gap made by TECO and evaluated by NASA

Diminiode thermionic energy conversion with lanthanum-hexaboride electrodes

Thermionic conversion data obtained from a variable gap cesium diminiode with a hot pressed, sintered lanthanum hexaboride emitter and an arc melted lanthanum hexaboride collector are presented. Performance curves cover a range of temperatures: emitter 1500 to 1700 K, collector 750 to 1000 K, and cesium reservoir 370 to 510 K. Calculated values of emitter and collector work functions and barrier index are also given

Least weight and least cost optimisation of a passenger vessel

In the scantling design of a passenger ship, minimum production costminimum weight and maximum moment of inertia (stiffness) are conflicting objectives. For that purpose, recent improvements have been made to the LBR-5 software (French acronym of Stiffened Panels Software”, version 5.0) to optimize the scantling of ship sections by considering production cost, weight and moment of inertia in the optimisation objective function. A real multi-criterion optimisation of a passenger ship is presented in this paper. Results highlight that LBR-5 is competitive software to optimise scantling of ships at very early design stage with management of critical problems studied normally at a later step of the design

Leveraging Multiple Descriptive Features for Robust Few-shot Image Learning

Modern image classification is based upon directly predicting model classes via large discriminative networks, making it difficult to assess the intuitive visual ``features'' that may constitute a classification decision. At the same time, recent works in joint visual language models such as CLIP provide ways to specify natural language descriptions of image classes but typically focus on providing single descriptions for each class. In this work, we demonstrate that an alternative approach, arguably more akin to our understanding of multiple ``visual features'' per class, can also provide compelling performance in the robust few-shot learning setting. In particular, we automatically enumerate multiple visual descriptions of each class -- via a large language model (LLM) -- then use a vision-image model to translate these descriptions to a set of multiple visual features of each image; we finally use sparse logistic regression to select a relevant subset of these features to classify each image. This both provides an ``intuitive'' set of relevant features for each class, and in the few-shot learning setting, outperforms standard approaches such as linear probing. When combined with finetuning, we also show that the method is able to outperform existing state-of-the-art finetuning approaches on both in-distribution and out-of-distribution performance