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Open Access Publications from the University of California

Scholarly Works (84 results)

  • Article
LBL Publications (2011)

A microwave ion source has been designed and constructed for use with a sealed-tube, high-yield neutron generator. When operated with a tritium-deuterium gas mixture the generator will be capable of producing 5*1011 n/s in non-proliferation applications. Microwave ion sources are well suited for such a device because they can produce high extracted beam currents with a high atomic fraction at low gas pressures of 0.2-0.3 Pa required for sealed tube operation. The magnetic field strength for achieving electron cyclotron resonance (ECR) condition, 87.5 mT at 2.45 GHz microwave frequency, was generated and shaped with permanent magnets surrounding the plasma chamber and a ferromagnetic plasma electrode. This approach resulted in a compact ion source that matches the neutron generator requirements. The needed proton-equivalent extracted beam current density of 40 mA/cm^2 was obtained at moderate microwave power levels of 400 W. Results on magnetic field design, pressure dependency and atomic fraction measured for different wall materials are presented.

    Cover page: Development of a Permanent-Magnet Microwave Ion Source for a Sealed-Tube Neutron Generator
    • Article
    Lawrence Berkeley National Laboratory (2011)

    We present recent work on the development of a microwave ion source that will be used in a high-yield compact neutron generator for active interrogation applications. The sealed tube generator will be capable of producing high neutron yields, 5x1011 n/s for D-T and ~;;1x1010 n/s for D-D reactions, while remaining transportable. We constructed a microwave ion source (2.45 GHz) with permanent magnets to provide the magnetic field strength of 87.5 mT necessary for satisfying the electron cyclotron resonance (ECR) condition. Microwave ion sources can produce high extracted beam currents at the low gas pressures required for sealed tube operation and at lower power levels than previously used RF-driven ion sources. A 100 mA deuterium/tritium beam will be extracted through a large slit (60x6 mm2) to spread the beam power over a larger target area. This paper describes the design of the permanent-magnet microwave ion source and discusses the impact of the magnetic field design on the source performance. The required equivalent proton beam current density of 40 mA/cm2 was extracted at a moderate microwave power of 400 W with an optimized magnetic field.

      Cover page: A Permanent-Magnet Microwave Ion Source for a Compact High-Yield Neutron Generator
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 36 (2014)
        Cover page: Causal agency and the perception of force
        • Article
        • Peer Reviewed
        Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 33 (2011)
          Cover page: Heuristics in Covariation-based Induction of Causal Models: Sufficiency and Necessity Priors
          • Article
          • Peer Reviewed
          Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 39 (2017)
          In the Michotte (1963) launching scenario, an object (X) moves toward a resting object (Y), eventually colliding withit. In the moment of contact, X stops und Y starts moving - creating the strong impression that X caused Y’s motion and that Xexerted a force on Y (but not vice versa). These asymmetries contradict the (symmetrical) laws of Newtonian mechanics, whichare at the heart of the popular “noisy Newton” theories of intuitive physics. As an alternative, we propose that inferences inphysical scenarios operate over pre-Newtonian representations that are based on the asymmetrical concept of impetus, a motiveforce that keeps objects moving and that is transferred and reflected in object collisions. We present a formal model of impetusand show that, unlike noisy Newton theories, it provides an explanation of asymmetrical judgments. Other related findings canalso be modeled (e.g., biases in mass judgments).
            Cover page: Causal asymmetry and the intuitive physics of collision events
            • Article
            • Peer Reviewed
            Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 32 (2010)
              Cover page: Deconfounding Distance Effects in Moral Reasoning
              • Article
              • Peer Reviewed
              Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 38 (2016)
              Some researchers have argued that mass perception, causal ascriptions, and predictions in simple billiard ball in-teractions can be modeled as inductive Bayesian inference over a (noisy) Newtonian representation of the world. However,there are phenomena, such as the asymmetrical ascription of forces to colliding objects, that are conceptually incompatible withthe symmetry of Newtonian physics. We propose that human inference in physical scenarios operates over a pre-Newtonianphysical representation that is based on impetus intuitions. Impetus theories assume that object movements are caused by aninternal force, impetus, that is transferred and reflected when objects collide with each other. Moreover, impetus interactionsare inherently asymmetric. We present a mathematical model that implements impetus theory and show that the theory is wellsuited to model perceived causal asymmetry. Moreover, the theory can also explain phenomena that so far have been presentedas unique evidence for (noisy) Newtonian representations.
                Cover page: Towards a Pre-Newtonian Intuitive Physics of Object Collisions
                • Article
                • Peer Reviewed
                Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 35 (2013)
                  Cover page: Agency Intuitions in Physical Interactions
                  • Article
                  • Peer Reviewed
                  Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 20 (1998)
                  Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues that represent effects. Our Bayesian network model overcomes this restriction. The model starts learning with initial stractural assumptions about the causal model underlying the learning domain. This causal model guides the estimation of causal strength, and suggests integration schemas for multiple cues. In this way, causal models effectively reduce the potential computational complexity inherent in even relatively simple learning tasks. The Bayesian model is applied to a number of experimental findings, including studies on estimation of causal strength, cue competition, base rate use, and learning linearly and nonlinearly separable categories.
                    Cover page: A Bayesian Network Model of Causal Learning
                    • Article
                    • Peer Reviewed
                    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 36 (2014)
                      Cover page: Do Social Norms Influence Causal Inferences?
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