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Pulsar Physics

and the General Particle Tracer (GPT) code

Highlight of GPT version 3.3: This release includes a number of performance upgrades, as well as the capability to include wakefields and CSR effects.

Continuously differentiable electromagnetic fields are not only more realistic, they also significantly speed up tracking speed compared to hard-edge models. This new version contains several new beamline components, such as a quardruple with analytical fringes (thanks to Bruno Muratory from Daresbuty lab), and other 'smooth' magnetostatic- and rf-structures. The tracking engine itself is rewritten such that a single GPT run can now be distributed over several nodes on MPI clusters.

Wakefields and CSR
Longitudinal and Transverse wakes can be included, where the wake-potential is convoluted with the 1D charge density profile to obtain the fields.

CSR effects can be calculated from first principles using retarded Liénard–Wiechert potentials. The CSR module makes use of the stored history of the beam, preventing potential erros with entrance and exit effects, and circumventing all problems related to rigid-bunch approximations.

Highlight of GPT version 3.2: A new multi-objective genetic optimizer that can optionally be run on an MPI Linux cluster to greatly aid and accelerate the design process.

Multi-objective genetic optimizer
Most real-life design processes have a large number of variables, several constraints, and a few conflicting objectives. The 'old-style' method to handle such cases is to cast everything into one single objective function by introducing weight factors for the different objectives and penalties for the constraints. This is problematic because usually the weight factors can not be given a priori because often it is the tradeoff itself that one is intersted in. Furthermore, such optimizations very narrowly aim for a single point in variable- and objective-space whereas for decision making the behavior of the optimzed set of variables as function of the different objectives is needed. The new genetic multi-objective optimizer was developed to adress these issues. The code works with a population of sample solutions that is ranked and genetically recombined in order to improve the overall population as a whole. During this process, valuable information is not lost but presented in a useful way: The output is the optimal tradeoff between all objectives, taking into account optional constraints, where each point along the line is fully optimized in all its variables.

Note: We use NGSA-II for ranking and Differential Evolution (DE) to create new candidate solutions. Load-balancing is achieved by being flexible in the definition of a new 'generation' so that a few runs can take much longer than the typical time to create a new generarion without significant loss of performance.

MPI Linux version
The stochastic nature of the genetic optimizer has significant advantages such as very good global convergence properties and insensitivity to numerical noise. However this comes at a price in terms of required CPU power. We created a load-balanced version of the multi-objective optimizer targeting MPI Linux clusters with 100 - 1000 cores. This is a perfect match for typical population sizes and speedups in the same range can be achieved. In addition, the multi-dimensional parameter scanning utility (MR) has been rewritten for MPI clusters.

Common features: A selection of the common features of GPT is listed below. Please contact us whenever you have any questions about the capabilities of GPT related to your project.

Equations of motion
5th order embedded Runge-Kutta
Adaptive stepsize control
Best accuracy over 10-10
Any number and any type of particles
Additional differential equations
    Windows User Interface
Fully integrated set-up editor
On-line help
Plots of raw GPT output
Plots of all data-analysis results
Plots of particle trajectories
Multiple synchronized windows
Wizards for custom elements
GPTwin User Interface
At specified simulation times
At 3D planes (nondestructive screens)
Coordinates and electromagnetic fields
Trajectory output
3D particle-in-cell
3D point-to-point 1)
2D point-to-ray
2D point-to-circle
Fully hierarchical
Standard macroscopic quantities
RMS, 90% and 100% Emittance
Courant-Snyder parameters
Color-density plots
Support for files >>4 GB
GPT plotting  

Scanning and solving
All multi-dimensional
Parameter scans
Multi-objective optimizer
Load balanced MPI support 2)

Root-finding and solving
      Collector design
plate, pipe, cone, torus, sphere, iris
Current/Power density plots
Collector design
Beam line components
barmagnet, bend, bz, bzsolenoid,
circlecharge, drift, ecyl, erect,
ezcell, linecharge, magline, magplate,
magpoint, multislit, platecharge,
pointcharge, quadrupole, rectcoil,
rmax, sextupole, solenoid, trwcell,
trwlinac, trwlinbm, undueqfo, unduplan,
Custom elements
    Interfaces with other codes
2D and 3D electrostatic field-maps
2D and 3D magnetostatic field-maps
2D TM cavity field-map
Poisson/ Superfish interface
Tabulated ascii input and output
DXF output for 3D drawing software
SDDS conversion utility
Field import

1) The 2.8 release contains a simple O(N2) version, later releases contain a fast O(N log N) tree-based model.
Platform dependent, see the GPT versions page for details.