A comparative study on the performance of SSDR coreless AFPM generators with conventional and Halbach magnet arrays

An axial flux permanent magnet (AFPM) generator is known to be a good candidate for both low- and high-speed applications. In this paper, a comprehensive comparison of four coreless AFPM generators is presented with conventional and Halbach magnet arrangement combined with iron and ironless rotor (epoxy). With the same coreless stator structure, four different AFPM generators with different rotor magnet arrangement and materials are compared in terms of voltage, current, power, machine weight, and power density. The analytical design approach is first presented and is subsequently validated using ANSYS Maxwell electromagnetic finite element analysis (FEA) software. Results show that AFPM machine with fully coreless topology using Halbach array with epoxy rotor can deliver similar power density but has a lower cost compared to conventional magnet array with iron rotor. Hence, this topology is favorable for low power low cost applications.

Analysis of the Reverse Expansion Process in a Reciprocating Hybrid Power Machine with Two Suction Valves

The paper analyzes the existing design schemes of hybrid power machines with a coolant moving in the jacket space due to the vacuum at the suction and introduces a new design of a single-stage single-cylinder piston hybrid power machine with two suction valves, in which the forced movement of the coolant in the forward direction is provided due to the vacuum at the suction, and in the reverse direction due to the pressure drop of the compressed gas. The analysis of the reverse expansion process allowed us to determine the value of the reduced dead space, which directly affects the volumetric feed rate of the reciprocating compressor and the feed rate in general. The parametric analysis made it possible to determine the influence of the main design and operating parameters on the value of the reduced dead space and accordingly on the volumetric feed rate. The results obtained can be useful in the design and research of reciprocating hybrid power machines with the forced movement of the coolant.

Polis: Escalar de la deliberación mediante el mapeo de espacios de opinión de alta dimensión

Deliberative and participatory approaches to democracy seek to directly include citizens in decision-making and agenda-setting processes. These methods date back to the very foundations of democracy in Athens, where regular citizens shared the burden of governance and deliberated every major issue. However, thinkers at the time rightly believed that these methods could not function beyond the scale of the city-state, or polis. Representative democracy as an innovation improved on the scalability of collective decision making, but in doing so, sacrificed the extent to which regular citizens could participate in deliberation. Modern technology, including advances in computational power, machine learning algorithms, and data visualization techniques, presents a unique opportunity to scale out deliberative processes. Here we describe Polis, an open source web application capable of collecting and synthesizing feedback from people in a scalable and distributed fashion. Polis has shown itself capable of building shared understanding, disincentivizing counterproductive behavior (trolling), and cultivating points of consensus. It has done this in the context of journalistic and academic research, and directly as part of decision-making bodies at local and national levels, directly affecting legislation. These results demonstrate that deliberative processes can be scaled up beyond the constraints of in-person gatherings and small groups.

Analysis of the influence of geometric parameters on working processes in a two!stage hybrid power machine of positive displacement without a gas cap with a profiled working chamber of the second stage

A principal scheme is considered and a mathematical model of work processes is developed in a two-stage hybrid power machine of positive displacement without a gas cap with a profiled working chamber of the second stage. The mathematical model is based on the basic laws of conservation of energy, volume and mass for a dropping liquid and a compressible gas in the second stage. The diameter of the supply pipeline and the initial radius of the profiled working chamber in the second stage are selected as the main geometric parameters that have the greatest influence on the work processes. The response functions are found that determine the dynamics of the fluid flow in the machine under study and its main integral characteristics. Keywords: work process, cooling, reciprocating compressor, hybrid power machine of positive displacement, profiled working chamber [email protected]

Using large-scale experiments and machine learning to discover theories of human decision-making

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.