1. A method of passivating a metal surface in a primary system of a nuclear power plant having a nuclear core and coolant water flowing through the primary system, comprising:
adding zinc to the coolant water during hot functional testing conducted prior to an initial loading of fuel in the nuclear core in an amount such that zinc concentration in the coolant water is from about 5 to about 300 ppb;
allowing the zinc to contact the metal surface in the primary system; and
forming an initial zinc-containing oxide film on the metal surface.
2. The method of claim 1, wherein the zinc is added in the form of zinc acetate.
3. The method of claim 1, further comprising adding base to the coolant water.
4. The method of claim 3, wherein the base is selected from the group consisting of lithium hydroxide, potassium hydroxide and mixtures thereof.
5. The method of claim 4, wherein the lithium hydroxide has a concentration sufficient for the coolant water to have an alkaline pH from about 6.9 to about 7.4 at the operating temperature or from about 9.5 to 10.1 at 25\xb0 C.
6. The method of claim 5, wherein the concentration of lithium in the coolant water is from about 0.3 ppm to about 2.0 ppm.
7. The method of claim 3, further comprising adding boric acid to the coolant water.
8. The method of claim 7, wherein the boric acid is added in an amount to such that there is a concentration of about 100 ppm boron or less.
9. The method of claim 3, further comprising adding hydrogen.
10. The method of claim 9, wherein a concentration of the hydrogen in the coolant water is selected from a range consisting of at least about 4 cckg, from about 4 to about 50 cckg, from about 4 to about 15 cckg, from about 15 to about 30 cckg, and about 4.5 cckg.
11. The method of claim 1, wherein the adding of the zinc is initiated when the coolant water has a temperature of about 350\xb0 F. or higher.
12. The method of claim 9, wherein the adding of the zinc, the lithium hydroxide and the hydrogen is initiated when the coolant water has a temperature of about 350\xb0 F. or higher.
13. A method for passivating a metal surface in a primary system of a new nuclear power plant during pre-core hot functional testing, the nuclear power plant having a nuclear core and coolant water flowing through the primary system, comprising:
initiating an alkaline-reducing phase, comprising:
adding hydroxide in an amount sufficient to maintain the coolant water at an alkaline pH;
adding zinc to the coolant water;
allowing the zinc to contact the metal surface in the primary system; and
forming a zinc-containing oxide film on the metal surface;
then initiating an acid-reducing phase, comprising:
borating the coolant water; and
then initiating an acid-oxidizing phase, comprising:
adding an oxidizing agent to the coolant water.
14. The method of claim 13, further comprising adding an oxygen scavenger to the coolant water prior to initiating the alkaline-reducing phase.
15. A method of controlling the addition of zinc into coolant water flowing through a primary system and a nuclear core of a nuclear power plant during a preconditioning prior to an initial fuel load and normal power operation, comprising:
initiating an alkaline-reducing phase, comprising:
adding to the coolant water zinc in an amount sufficient to provide a concentration from about 5 to about 300 ppb in the coolant water and at least one compound selected from the group consisting of hydroxide and hydrogen; and
increasing the coolant temperature to initiate a normal operating temperature plateau;
then initiating an acid-reducing phase, comprising:
adding boric acid to the coolant water at or near end of the operating temperature plateau; and
then initiating an acid-oxidizing phase, comprising:
adding an oxidizing agent to the coolant water.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A brain-based device comprising:
a) a mechanism enabling movement of the brain-based device in a real-world environment;
b) a simulated nervous system, being interactive with the real-world environment, for controlling said mechanism to cause the brain-based device to move in the real-world environment; and
c) said simulated nervous system including a model of the cerebellum having synapses which are eligible for plasticity only after a given time interval to enable the model to predict motor control of said mechanism.
2. A brain-based device, according to claim 1, wherein the device has a real-world learning mode and a real-world post-learning mode to predict obstacles in the path of movement of the device.
3. A brain-based device, according to claim 2, wherein, during the learning mode,
a) at a time t0, visual input to the brain-based device activates certain synapses of the cerebellum model above a threshold, said certain synapses are stored in a delay buffer, and activity of the cerebellum model is not strong enough to evoke a motor response to control said mechanism; and
b) at a time tn, subsequent to time t0, the certain synapses stored in the buffer are eligible for synaptic change, an error signal is generated in response to an obstacle in the path of movement of the brain-based device, synaptic plasticity change of said certain synapses occurs in response to the error signal, and synaptic connections become strong enough to evoke a motor control of said mechanism to cause the brain-based device to move away from the obstacle.
4. A brain-based device according to claim 3, wherein, during the post-learning mode,
a) at a time t0, visual input to the brain-based device activates certain synapses above a threshold, said certain synapses are stored in a delay buffer, activity of the cerebellum evokes a motor control response, and due to synaptic change occurring during the learning mode, causes the brain-based device to avoid the obstacle in its path before a collision; and
b) during a time period subsequent to time t0, no error signal is generated and no additional synaptic change occurs.
5. A brain-based device, according to claim 4, wherein said model of said cerebellum comprises precerebellum nuclei (PN) and a cerebellar cortex having Purkinje cells (PC) and deep cerebellar nuclei (DCN), and an inferior olive (IO), wherein said precerebellar nuclei output to said cerebellar cortex, said Purkinje cells (PC) inhibit said deep cerebellar nuclei (DCN) for turning and velocity control of the brain-based device, and said interior olive (IO) simulates climbing fiber input to said cerebellar cortex.
6. A brain-based device, according to claim 5, wherein said simulated nervous system further comprises a visual cortical area (MT) for providing input to said Purkinje cells (PC).
7. A brain-based device, according to claim 6, wherein said mechanism comprises a camera for providing visual input to said visual cortical area (MT).
8. A brain-based device, according to claim 7, wherein said mechanism comprises detectors for providing turn and velocity input to said inferior olive (IO).
9. A brain-based device, according to claim 8, wherein said mechanism comprises a motor and wheels driven by said motor, and wherein said motor receives input from said deep cerebellar nuclei (DCN) and from said inferior olive (IO) to control the turning and velocity of the brain-based device.
10. A brain-based device, according to claim 9, wherein said given time interval is fixed delay in the range of 2-4 seconds.
11. A method for controlling movement of a mobile brain-based device in a real-world environment, the brain-based device including a simulated nervous system modeling the cerebellum, the method comprising:
during a learning stage of the brain-based device (i) providing visual input to activate synapses from precerebellum nuclei (PN) to Purkinje cells (PC) and from precerebellum nuclei (PN) to deep cerebellar nuclei (DCN) above a threshold, (ii) storing those synapses which are above the threshold, (iii) after a certain time, generating an error signal in the event the mobile brain-based device hits an obstacle, (iv) providing the error signal from an inferior olive (IO) to the synapses of the PN\u2192PC and PN\u2192DCN paths, in which those synapses which have been stored for at least the certain time undergo synaptic change due to the error signal, and in which the synaptic connectors in the path IO\u2192DCN are strong enough to evoke a response causing the mobile brain-based device to move away from the obstacle.
12. A method according to claim 11, further comprising:
during a testing stage, (i) providing visual input to activate synapses in the path PN\u2192PC and PN\u2192DCN above a threshold, (ii) storing the above threshold synapses, wherein, as a result of the synaptic change during the learning stage, activity of the deep cerebellar nuclei (DCN) evokes a response to cause the brain-based device to avoid the obstacle, and (iii) wherein after a certain time no error signal is generated from the inferior olive (IO) and no further synaptic change occurs.
13. A method according to claim 12, wherein the certain time is predetermined, and is in the range of 2-4 seconds.