Performance Based Track Geometry Phase 2 (2015) / Chapter Skim
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C H A P T E R 3 Findings and Application 3.1 Model Validation A NUCARS model was built to represent the PATH PA5 car, using design data updated by the measured characteristics. The model included a full nonlinear representation of the air suspension, including the effects of damping due to air flow in the orifices between the reservoirs and airbags.
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Figure 33. Measured Vertical Accelerations Compared with Predicted Vertical Accelerations -0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 Dr iv er 's Ca b Ve rt ic al A cc el er at io ns (g 's)
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Figure 34. Measured Lateral Accelerations Compared to Predicted Lateral Accelerations -0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 Dr iv er 's Ca b La te ra l A cc el er at io ns (g 's)
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Figure 35. Measured Vertical Acceleration Frequency Content Compared with Predicted Frequency Content Figure 36.
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3.2 Neural Net Development A complex dynamic relationship exists between vehicle response and track geometry. PBTG inspection emphasizes that car dynamic response directly results from a combination of many track geometry variables acting together with vehicle operating conditions.
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response of the network. If the network response is not adequately close to the desired response, a backward pass is used and the NN weights are iteratively adjusted based on an error-correction rule to fine-tune the response in order to move it closer to the desired response.
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Figure 38. DART – Alignment of Ride Quality Data and Track Geometry Data The NNs were trained using a portion of the measured ride quality data.
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Figure 39. DART – Neural Net Training Data for Point-by-Point Approach Figure 40 shows the trained NN deployed on LBJ station to Spring Valley Stations track geometry for validation.
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Figure 41 shows NN training and validation data using a segment-based approach. The NN predicted the carbody vertical accelerations with 0.25 percent confidence.
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Carbody accelerations were the output variables the NN models were trained to predict. Figure 42 shows an example of a predicted front carbody vertical acceleration (in red)
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Figure 43. PATH Training and Validation Data at Center Carbody Acceleration Figure 44.
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• More track geometry deviations trends and corresponding high dynamic responses were present in the data collected on PATH tracks • Geometry deviations and high dynamic responses are patterns that NN models are capable of recognizing reasonably well if available in the training data. By contrast, good track conditions and low dynamic response are seen as patternless noise that the NNs tend not to recognize the trends or the patterns.
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