And I read on this forum from someone else that their friend accedently turned off the ignition during key learning, and it was a nightmare. I was shattered, vvdi2 pretty much said 'don't turn off the ignition until key learning is complete, or your screwed). I tried every different positions, removed plastic from case ect, obviously the new key was wrong. I learnt the original first, dash changed to 1/2. So I got to the car, read eeprom and saved, read immo and saved. Iv done a few mqb keyless for audi, and VW (vw I did, the part numbers of my key where a bit different from the original and still worked). I had a VW mqb keyless key, that I saw someone online saying should work for octavia. This morning I had a job for a spare key for a Skoda octavia 2016 mqb keyless (stop start button was on the steering column).ĥE0 959 752E was the part number of the original key. Iv got a guy that won't touch euro cars, and has started sending them to me. Hopefully it might help someone if they come across the same problem. In previous work, ADVANTG was applied to a suite of nine radiation detection, special nuclear material (SNM) movement detection, and safeguards test cases.Iv definitely learnt more from this forum then I have given, so I will share my experience today, even though it is embarrassing, and a mistake that could have been easily avoided. for detecting the diversion of fuel elements from spent nuclear fuel (SNF) assemblies. For the passive detection problems, ADVANTG obtains an O(10Cf Interrogation with Prompt Neutron (CIPN) detection system proposed by Hu et al. We also present results for two passive detection problems in which a cargo container holding a shielded neutron or gamma source is placed near a portal monitor. We present numerical results for an 'active-interrogation' problem in which a standard cargo container is irradiated by a deuterium-tritium fusion neutron generator. In this paper, we describe the application of ADVANTG to a set of threat-detection simulations. The ADVANTG code has been developed to automate the generation of weight-window more » parameters for MCNP using the Consistent Adjoint Driven Importance Sampling method and employs the TORT or Denovo 3-D discrete ordinates codes to generate importance maps. For these problems, automated hybrid methods offer an efficient alternative to trial-and-error variance reduction techniques (e.g., geometry splitting or the stochastic weight window generator). An active source of new applications is the field of homeland security-particularly the detection of nuclear material threats. As the use of this hybrid computational technique is becoming more widespread, the scope of applications in which it is being applied is expanding. Deterministic transport codes have been used for some time to generate weight-window parameters that can improve the efficiency of Monte Carlo simulations.

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