CAFPLab-VisJS/index.html at master · gabe7murphy/CAFPLab-VisJS · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629

<!--
  In the following URLs you may want to replace @latest by @version
  to prevent unexpected potentionally breaking updates.
  For example vis-data@1.0.0 instead of vis-data@latest.
-->
<script type="text/javascript" src="https://unpkg.com/vis-data@latest/peer/umd/vis-data.min.js"></script>
<script type="text/javascript" src="https://unpkg.com/vis-network@latest/peer/umd/vis-network.min.js"></script>
<link rel="stylesheet" type="text/css" href="https://unpkg.com/vis-network/styles/vis-network.min.css" />
<link rel="stylesheet" href="style.css">
<!-- <link rel="stylesheet" href="style.css"> -->
<!-- You may include other packages like Vis Timeline or Vis Graph3D here. -->
<div class="loader"></div>
<script>
  window.addEventListener("load", () => {
  const loader = document.querySelector(".loader");

  loader.classList.add("loader--hidden");

  loader.addEventListener("transitionend", () => {
    document.body.removeChild(loader);
  });
});
</script>

<html>
  <head>
    <title>CAFPLab Research Landscape</title>
  </head>
  <body>
    <p><strong style="color:#FF3131;  font-size:24px;">References </strong> / <strong style="color:#1F51FF;  font-size:24px;"> Citations </strong></p>
    <p></p>
  </body>
</html>

<div id="mynetwork"></div>
<script type="text/javascript">
  // create an array with nodes
  var nodes = new vis.DataSet([
    // Paper 1: Assessment of Turbulence Models in a Hypersonic Cold-Wall Turbulent Boundary Layer
    // 1 to 13 Asssesment CITES, 14-38 Referenced
    { id: 1, label: "Assessment of Turbulence Models in a Hypersonic Cold-Wall Turbulent Boundary Layer" },
    { id: 2, label: "Error quantification among CFD solvers for high-speed, non-adiabatic, wall-bounded turbulent flows" },
    { id: 3, label: "Prediction of aerothermal characteristics of a generic hypersonic inlet flow" },
    { id: 4, label: "Turbulence Modeling in Hypersonic Turbulent Boundary Layers Subject to Convex Wall Curvature" },
    { id: 5, label: "Instabilities and transition in cooled wall hypersonic boundary layers" },
    { id: 6, label: "Internal energy balance and aerodynamic heating predictions for hypersonic turbulent boundary layers" },
    { id: 7, label: "Non-Linear Turbulence Modeling (LES/DES/RSM/DNS) with Case Studies" },
    { id: 8, label: "Assessment of Reynolds Averaged Navier-Stokes Models for a Hypersonic Cold-Wall Turbulent Boundary Layer" },
    { id: 9, label: "Compressibility Effectsy in One-Equation Turbulence Models" },
    { id: 10, label: "Direct Numerical Simulation of Boundary Layer Receptivity to Acoustic Radiation in a Hypersonic Compression Ramp Flow" },
    { id: 11, label: "Essentially non-oscillatory and weighted essentially non-oscillatory schemes" },
    { id: 12, label: "Estimation of the rate of turbulent liquid flow in the pipeline based on surface measurements of flow noise" },
    { id: 13, label: "Turbulence Modeling with Case Studies" },
    { id: 14, label: "A critical Commentary on Mean Flow Data for Two-Dimensional Compressible BoundaryLayers" },
    { id: 15, label: "Review and Assessment of Turbulence Models for Hypersonic Flows" },
    { id: 16, label: "Turbulent Shear Layers in Supersonic Flow" },
    { id: 17, label: "Response of a hypersonic turbulent boundary layer tofavourable pressure gradients" },
    { id: 18, label: "Scaling of heat transfer augmentation due to mechanical distortions inhypervelocity boundary layers" },
    { id: 19, label: "Inﬂuence of Streamline Adverse Pressure Gradients onthe Structure of a Mach 5 Turbulent Boundary Layer" },
    { id: 20, label: "Crosshatch Roughness Distortions on a Hypersonic TurbulentBoundary Layer" },
    { id: 21, label: "Experiments on the structure and scaling ofhypersonic turbulent boundary layers" },
    { id: 22, label: "Direct Numerical Simulation Database for Supersonic and HypersonicTurbulent Boundary Layers" },
    { id: 23, label: "Compressibility Considerations for k-omega Turbulence Models in Hypersonic Boundary-Layer Applications" },
    { id: 24, label: "A Summary of Viscosity and Heat-Conduction Data for He, A,H2,O2,CO,CO2,H2O, and Air" },
    { id: 25, label: "Numerical Study of Pressure Fluctuations due to a Supersonic TurbulentBoundary Layer" },
    { id: 26, label: "Efﬁcient Implementation of Weighted ENO Schemes" },
    { id: 27, label: "Optimization of Nonlinear Error Sources for Weighted Non-OscillatoryMethods in Direct Numerical Simulations of Compressible Turbulence" },
    { id: 28, label: "Direct Numerical Simulation of Supersonic Boundary Layer over a Compression Ramp" },
    { id: 29, label: "Pressure Fluctuations Induced by a Hypersonic Turbulent BoundaryLayer" },
    { id: 30, label: "Direct Numerical Simulation of Hypersonic TurbulentBoundary Layers inside an Axisymmetric Nozzle" },
    { id: 31, label: "Direct Numerical Simulation of Acoustic Noise Generation from theNozzle Wall of a Hypersonic Wind Tunnel" },
    { id: 32, label: "Characterization of Freestream Disturbances in Conventional HypersonicWind Tunnels" },
    { id: 33, label: "Direct Numerical Simulationof Nozzle-Wall Pressure Fluctuations in a Mach 8 Wind Tunnel" },
    { id: 34, label: "Implementing Turbulence Models into the Compressible RANS Equations" },
    { id: 35, label: "A One-Equation Turbulence Model for Aerodynamic Flows" },
    { id: 36, label: "Two-equation eddy-viscosity turbulence models for engineering applications" },
    { id: 37, label: "An Evaluation of Theories for Predicting Turbulent Skin Friction and Heat Transferon Flat Plates at Supersonic and Hypersonic Mach Numbers" },
    { id: 38, label: "The drag of a compressible turbulent boundary layer on a smooth ﬂat plate with andwithout heat transfer" },
    //paper 2: Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning, Citations 39-77
    { id: 39, label: "Toward More General Turbulence Models via Multicase Computational-Fluid-Dynamics-Driven Training" },
    { id: 40, label: "Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach" },
    { id: 41, label: "Preliminary Development of Machine Learning-Aided Reactor Physics Simulation" },
    { id: 42, label: "A Mobility Model for Synthetic Travel Demand From Sparse Traces" },
    { id: 43, label: "Validation of URANS and STRUCT-ε turbulence models for stratified sodium flow" },
    { id:44, label: "A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics" },
    { id: 45, label: "A Perspective on Data-driven Coarse Grid Modeling for System Level Thermal Hydraulics" },
    { id: 46, label: "Machine-learning for turbulence and heat-flux model development: A review of challenges associated with distinct physical phenomena and progress to date" },
    { id: 47, label: "Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability" },
    { id: 48, label: "Pre-processing DNS data to improve statistical convergence and accuracy of mean velocity fields in invariant data-driven turbulence models" },
    { id: 49, label: "A perspective on data-driven approaches for multiscale bridging in system thermal hydraulics" },
    { id: 50, label: "Analysis on numerical stability and convergence of Reynolds averaged Navier–Stokes simulations from the perspective of coupling modes" },
    { id: 51, label: "Data-driven model for improving wall-modeled large-eddy simulation of supersonic turbulent flows with separation" },
    { id: 52, label: "Assessment of Regularized Ensemble Kalman Method for Inversion of Turbulence Quantity Fields" },
    { id: 53, label: "Development and Validation of a Machine Learned Turbulence Model" },
    { id: 54, label: "Real-Time Simulation of Parameter-Dependent Fluid Flows through Deep Learning-Based Reduced Order Models" },
    { id: 55, label: "Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models" },
    { id: 56, label: "Analysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes" },
    { id: 57, label: "Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels" },
    { id: 58, label: "A Bi-fidelity ensemble kalman method for PDE-constrained inverse problems in computational mechanics" },
    { id: 59, label: "Feasibility of estimating travel demand using geolocations of social media data" },
    { id: 60, label: "Feature selection and processing of turbulence modeling based on an artificial neural network" },
    { id: 61, label: "Review of Physics-based and Data-driven Multiscale Simulation Methods for Computational Fluid Dynamics and Nuclear Thermal Hydraulics" },
    { id: 62, label: "Essentially non-oscillatory and weighted essentially non-oscillatory schemes" },
    { id: 63, label: "WearGP: A UQ/ML Wear Prediction Framework for Slurry Pump Impellers and Casings" },
    { id: 64, label: "Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels" },
    { id: 65, label: "On the Explainability of Machine-Learning-Assisted Turbulence Modeling for Transonic Flows" },
    { id: 66, label: "Feature selection and processing of turbulence modeling based on an artificial neural network" },
    { id: 67, label: "Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networks" },
    { id: 68, label: "Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networks" },
    { id: 69, label: "A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems" },
    { id: 70, label: "WearGP: A UQ/ML wear prediction framework for slurry pump impellers and casings" },
    { id: 71, label: "Explainable Machine Learning for Scientific Insights and Discoveries" },
    { id: 72, label: "Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach" },
    { id: 73, label: "Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations" },
    { id: 74, label: "Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows" },
    { id: 75, label: "Quantification of model uncertainty in RANS simulations: A review" },
    { id: 76, label: "Recent progress in augmenting turbulence models with physics-informed machine learning" },
    { id: 77, label: "Quantification of Model Uncertainty in RANS Simulations: A Review" },
  // References for Prediction Paper 78 - 130
  { id: 78, label: "Turbulence Modeling in the Age of Data" },
    { id: 79, label: "Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework" },
    { id: 80, label: "Effect of wall cooling on boundary-layer-induced pressure fluctuations at Mach 6" },
    { id: 81, label: "A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling" },
    { id: 82, label: "Physics-informed machine learning for predictive turbulence modeling: Toward a complete framework" },
    { id: 83, label: "A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling" },
    { id: 84, label: "Visualization of High Dimensional Turbulence Simulation Data using t-SNE" },
    { id: 85, label: "Statistical theory and modeling for turbulent flows" },
    { id: 86, label: "Statistical theory and modeling for turbulent flows" },
    { id: 87, label: "Reynolds averaged turbulence modelling using deep neural networks with embedded invariance" },
    { id: 88, label: "Computational Study of Hypersonic Flow Past Spiked Blunt Body Using RANS and DSMC Method" },
    { id: 89, label: "Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow" },
    { id: 90, label: "Scikit-Iearn: Machine learning in python" },
    { id: 91, label: "Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data" },
    { id: 92, label: "Pressure Fluctuations induced by a Hypersonic Turbulent Boundary Layer" },
    { id: 93, label: "Using field inversion to quantify functional errors in turbulence closures" },
    { id: 94, label: "Bayesian Parameter Estimation of a k-ε Model for Accurate Jet-in-Crossflow Simulations" },
    { id: 95, label: "Inflow Turbulence Generation Methods" },
    { id: 96, label: "Anisotropic Reynolds stress tensor representation in shear flows using DNS and experimental data" },
    { id: 97, label: "Machine learning strategies for systems with invariance properties" },
    { id: 98, label: "Quantification of Uncertainties in Turbulence Modeling: A Comparison of Physics-Based and Random Matrix Theoretic Approaches" },
    { id: 99, label: "Incorporating Prior Knowledge for Quantifying and Reducing Model-Form Uncertainty in RANS Simulations" },
    { id: 100, label: "Mean velocity scaling for compressible wall turbulence with heat transfer" },
    { id: 101, label: "Anisotropic Reynolds stress tensor representation in shear flows using DNS and experimental data" },
    { id: 102, label: "Bayesian calibration of computer models - Discussion" },
    { id: 103, label: "A Bayesian Calibration-Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations" },
    { id: 104, label: "A paradigm for data-driven predictive modeling using field inversion and machine learning" },
    { id: 105, label: "Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications" },
    { id: 106, label: "Analysis of numerical simulation database for pressure fluctuations induced by high-speed turbulent boundary layers" },
    { id: 107, label: "Quantifying and reducing model-form uncertainties in Reynolds averaged Navier–Stokes equations: A data-driven, physics-informed Bayesian approach" },
    { id: 108, label: "Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty" },
    { id: 109, label: "An Introduction to Statistical Learning" },
    { id: 110, label: "Analysis of Numerical Simulation Database for Acoustic Radiation from High-Speed Turbulent Boundary Layers" },
    { id: 111, label: "Quantification of Structural Uncertainties in the k -w Turbulence Model" },
    { id: 112, label: "Predictive RANS simulations via Bayesian Model-Scenario Averaging" },
    { id: 113, label: "Oblique Shock Impinging on a Turbulent Boundary Layer: Low-Frequency Mechanisms." },
    { id: 114, label: "Springer Texts in Statistics" },
    { id: 115, label: "Numerical study of acoustic radiation due to a supersonic turbulent boundary layer" },
    { id: 116, label: "Compressible turbulent channel flows: DNS results and modelling" },
    { id: 117, label: "Inflow boundary conditions for compressible turbulent boundary layers" },
    { id: 118, label: "Bayesian estimates of parameter variability in the k − ε turbulence model" },
    { id: 119, label: "Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures" },
    { id: 120, label: "Direct Numerical Simulation of Supersonic Turbulent Boundary Layer over a Compression Ramp" },
    { id: 121, label: "Hypersonic Flow Predictions Using Linear and Nonlinear Turbulence Closures" },
    { id: 122, label: "Bayesian uncertainty analysis with applications to turbulence modeling" },
    { id: 123, label: "Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches" },
    { id: 124, label: "Direct numerical simulation of hypersonic turbulent boundary layers. Part 3. Effect of Mach number" },
    { id: 125, label: "Viualizing data using t-SNE" },
    { id: 126, label: "An explicit algebraic Reynolds stress and heat flux model for incompressible turbulence: Part I Non-isothermal flow" },
    { id: 127, label: "Bayesian uncertainty quantification applied to RANS turbulence models" },
    { id: 128, label: "Nonlinear eddy viscosity and algebraic stress models for solving complex turbulent flows" },
    { id: 129, label: "Scikit-learn: Machine Learning in Python" },
    { id: 130, label: "Efficient Implementation of Weighted ENO Schemes" },
    // Paper 3: Interaction of a Tunnel-like Acoustic Disturbance Field with a Blunt Cone Boundary Layer at Mach 8: citations 131 - 134
    { id: 131, label: "Direct numerical simulation of compressible turbulence accelerated by graphics processing unit: An open-access database of high-resolution direct numerical simulation" },
    { id: 132, label: "Linear and Nonlinear Disturbance Evolution on the Frustum of Hypersonic Ogive-Cylinders" },
    { id: 133, label: "DNS of a Mach 14 Flow Over a Sharp Cone in AEDC Tunnel 9" },
    { id: 134, label: "Interaction of a Tunnel-like Acoustic Disturbance Field with a Normal Shock Wave: Theory and Simulation" },
    // References 135 - 172
    { id: 135, label: "Experimental Measurements of Hypersonic Instabilities over Ogive-Cylinders at Mach 6" },
    { id: 136, label: "Supersonic transition induced by numerical tunnel disturbances" },
    { id: 137, label: "Effects of Nose Bluntness on Hypersonic Boundary-Layer Receptivity and Stability" },
    { id: 138, label: "Nonmodal Growth of Traveling Waves on Blunt Cones at Hypersonic Speeds" },
    { id: 139, label: "Towards simulating natural transition in hypersonic boundary layers via random inflow disturbances" },
    { id: 140, label: "Transition on a Variable Bluntness 7-Degree Cone at High Reynolds Number" },
    { id: 141, label: "Boundary-Layer Stability Analysis for Stetson’s Mach 6 Blunt-Cone Experiments" },
    { id: 142, label: "Algorithmic enhancements to the VULCAN Navier-Stokes Solver" },
    { id: 143, label: "Transition Prediction of Boundary Layers in the Presence of Backward-Facing Steps" },
    { id: 144, label: "Direct Numerical Simulation of Acoustic Disturbances in a Hypersonic Two-Dimensional Nozzle Configuration" },
    { id: 145, label: "Characterization of instability mechanisms on sharp and blunt slender cones at Mach 6" },
    { id: 146, label: "Experimental Measurements of Hypersonic Instabilities over Ogive-Cylinders at Mach 6" },
    { id: 147, label: "Mechanism for frustum transition over blunt cones at hypersonic speeds" },
    { id: 148, label: "Stochastic receptivity analysis of boundary layer flow" },
    { id: 149, label: "Influence of Nose-Tip Bluntness on Conical Boundary-Layer Instabilities at Mach 10" },
    { id: 150, label: "Direct Numerical Simulation of Nozzle-Wall Pressure Fluctuations in a Mach 8 Wind Tunnel" },
    { id: 151, label: "Nose-Tip Bluntness Effects on Transition at Hypersonic Speeds" },
    { id: 152, label: "Characterization of Freestream Disturbances in Conventional Hypersonic Wind Tunnels" },
    { id: 153, label: "Understanding effects of nose-cone bluntness on hypersonic boundary layer transition using input-output analysis" },
    { id: 154, label: "Nosetip bluntness effects on transition at hypersonic speeds: experimental and numerical analysis under NATO STO AVT-240" },
    { id: 155, label: "Receptivity and Forced Response to Acoustic Disturbances in High-Speed Boundary Layers" },
    { id: 156, label: "Transition Prediction in Hypersonic Boundary Layers Using Receptivity and Freestream Spectra" },
    { id: 157, label: "Direct Numerical Simulation of Acoustic Noise Generation from the Nozzle Wall of a Hypersonic Wind Tunnel" },
    { id: 158, label: "Direct Numerical Simulation of Hypersonic Turbulent Boundary Layers inside an Axisymmetric Nozzle" },
    //{ id: 159, label: "Pressure Fluctuations induced by a Hypersonic Turbulent Boundary Layer" }, //92
    { id: 160, label: "Optimal Growth in Hypersonic Boundary Layers" },
    { id: 161, label: "Hypersonic Wind-Tunnel Measurements of Boundary-Layer Transition on a Slender Cone" },
    { id: 162, label: "A summary of viscosity and heat conduction data for H, A, O, N, CO, CO, HO and air22222" },
    { id: 163, label: "Nosetip bluntness effects on cone frustum boundary layer transition in hypersonic flow" },
    { id: 164, label: "Investigation of Hypersonic Laminar Heating Augmentation in the Stagnation Region" },
    { id: 165, label: "Aerodynamic Noise in Supersonic Wind Tunnels" },
    //{ id: 166, label: "Numerical study of acoustic radiation due to a supersonic turbulent boundary layer" }, //115
    { id: 167, label: "Procedure to Validate Direct Numerical Simulations of Wall-Bounded Turbulence Including Finite-Rate Reactions" },
    { id: 168, label: "Effects of High-Speed Tunnel Noise on Laminar-Turbulent Transition" },
    { id: 169, label: "Some Statistical Properties of the Pressure Field Radiated by a Turbulent Boundary Layer" },
    { id: 170, label: "Dominance of radiated aerodynamic noise on boundary-layer transition in supersonic-hypersonic wind tunnels. Theory and application" },
    { id: 171, label: "Boundary-layer receptivity of Mach 7.99 flow over a blunt cone to free-stream acoustic waves" },
    { id: 172, label: "Finite differences for coarse azimuthal discretization and for reduction of effective resolution near origin of cylindrical flow equations" },
    //paper 4 only references: Direct Numerical Simulation of High-Speed Boundary-Layer Separation due to Forward Facing Curvature 173 - 189
    { id: 173, label: "Direct Numerical Simulation of High-Speed Boundary-Layer Separation due to Forward Facing Curvature" },
    { id: 174, label: "Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number" },
   // ID = 78 { id: 175, label: "Turbulence Modeling in the Age of Data" },
    { id: 176, label: "The Effect of Particle Lag on Statistics of Hypersonic Turbulent Boundary Layers Subject to Pressure Gradients" },
   // ID = 73 { id: 177, label: "Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations" },
    { id: 178, label: "Direct Numerical Simulation Database for Supersonic and Hypersonic Turbulent Boundary Layers" },
    { id: 179, label: "Numerical study of turbulent separation bubbles with varying pressure gradient and Reynolds number" },
    { id: 180, label: "Experiments on the structure and scaling of hypersonic turbulent boundary layers" },
   // ID = 92 { id: 181, label: "Pressure Fluctuations induced by a Hypersonic Turbulent Boundary Layer" },
   // ID = 115 { id: 182, label: "Numerical study of acoustic radiation due to a supersonic turbulent boundary layer" },
    { id: 183, label: "Response of Hypersonic Turbulent Boundary Layer to Favorable Pressure Gradients" },
    { id: 184, label: "Assessment of direct numerical simulation data of turbulent boundary layers" },
    { id: 185, label: "A high-resolution code for turbulent boundary layers" },
    { id: 186, label: "Low-storage Runge-Kutta schemes" },
    { id: 187, label: "Time-Dependent Boundary Condition for Hyperbolic Systems" },
    { id: 188, label: "Performance of popular turbulence model for attached and separated adverse pressure gradient flows" },
    { id: 189, label: "Menter, F.: Two-Equation Eddy-Viscosity Transport Turbulence Model for Engineering Applications. AIAA Journal 32(8), 1598-1605" },
    // paper 5: Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number
    // citations: 190 - 205
    { id: 190, label: "Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number" },
    { id: 191, label: "Large-scale motions and self-similar structures in compressible turbulent channel flows" },
   //ID = 131 { id: 192, label: "Direct numerical simulation of compressible turbulence accelerated by graphics processing unit: An open-access database of high-resolution direct numerical simulation" },
    { id: 193, label: "Energy exchanges in hypersonic flows" },
    { id: 194, label: "Natural Grid Stretching for Dns of Compressible Wall-Bounded Flows" },
   //ID = 173 { id: 195, label: "Direct Numerical Simulation of High-Speed Boundary-Layer Separation due to Forward Facing Curvature" },
    { id: 196, label: "Direct-Numerical and Large-Eddy Simulations of Hypersonic Turbulent Couette Flow at Mach 6, 7 and 8" },
    { id: 197, label: "Probing Resolution Effects of Particle Image Velocimetry for Measuring High-Speed Turbulent Boundary Layers Using Lagrangian Particle Tracking" },
    { id: 198, label: "Numerical Investigation of Wall-Cooling Effect on Aero-Optical Distortions for Hypersonic Boundary Layer" },
    { id: 199, label: "Mean Velocity Scaling of High-Speed Turbulent Flows Under Nonadiabatic Wall Conditions" },
    { id: 200, label: "Numerical tripping of high-speed turbulent boundary layers" },
    { id: 201, label: "Wall-cooling effects on pressure fluctuations in compressible turbulent boundary layers from subsonic to hypersonic regimes" },
    { id: 202, label: "Wall heat transfer in high-enthalpy hypersonic turbulent boundary layers" },
    //ID = 6{ id: 203, label: "Internal energy balance and aerodynamic heating predictions for hypersonic turbulent boundary layers" },
    { id: 204, label: "Direct numerical simulation of supersonic and hypersonic turbulent boundary layers at moderate-high Reynolds numbers and isothermal wall condition" },
    { id: 205, label: "Effect of the Reynolds Number on the Freestream Disturbance Environment in a Mach 6 Nozzle" },
    // paper 5 references 206 - 284
   //ID = 80 { id: 222, label: "Effect of wall cooling on boundary-layer-induced pressure fluctuations at Mach 6" },
    { id: 223, label: "Attached Eddy Model of Wall Turbulence" },
   //ID = 180 { id: 224, label: "Experiments on the structure and scaling of hypersonic turbulent boundary layers" },
    { id: 225, label: "Turbulent Boundary Layer in Compressible Fluids" },
    { id: 226, label: "On the invariant mean velocity profile for compressible turbulent boundary layers" },
   //ID = 92 { id: 227, label: "Pressure Fluctuations induced by a Hypersonic Turbulent Boundary Layer" },
    { id: 228, label: "Turbulent Inflow Generation for Direct Simulations of Hypersonic Turbulent Boundary Layers and their Freestream Acoustic Radiation" },
    { id: 229, label: "Crosshatch roughness distortions on a hypersonic turbulent boundary layer" },
    { id: 230, label: "Reynolds and Mach number effects in compressible turbulent channel flow" },
    { id: 231, label: "On the identification of a vortex-" },
    { id: 232, label: "Mean velocity scaling for compressible wall turbulence with heat transfer" },
   //ID = 16 { id: 233, label: "Turbulent Shear Layers in Supersonic Flow" },
    { id: 234, label: "Influence of temperature ratio on heat transfer to a flat plate through a turbulent boundary layer in air" },
    { id: 235, label: "Semi-local scaling and turbulence modulation in variable property turbulent channel flows" },
    { id: 236, label: "Resolution Effects in Compressible, Turbulent Boundary Layer Simulations" },
    { id: 237, label: "Low-Reynolds-Number Turbulent Boundary Layers in Zero and Favorable Pressure Gradients" },
    { id: 238, label: "Improved Large-Eddy Simulation Validation Methodology: Application to Supersonic Inlet/Isolator Flow" },
    { id: 239, label: "Digital Filter-based Turbulent Inflow Generation for Jet Aeroacoustics on Non-Uniform Structured Grids" },
    { id: 240, label: "Uncertainty Assessments of Hypersonic Shock Wave-Turbulent Boundary-Layer Interactions at Compression Corners" },
    { id: 241, label: "DNS of reacting hypersonic turbulent boundary layers" },
    { id: 242, label: "Direct Numerical Simulation of a Hypersonic Turbulent Boundary Layer on a Large Domain" },
    { id: 243, label: "DNS of transition to turbulence in a hypersonic boundary layer" },
    { id: 244, label: "Uncertainty Assessments of 2D and Axisymmetric Hypersonic Shock Wave - Turbulent Boundary Layer Interaction Simulations at Compression Corners" },
    { id: 245, label: "Direct numerical simulation of turbulent channel flow up to Reτ ≈ 5200" },
    { id: 246, label: "DNS of Hypersonic Turbulent Boundary Layers" },
    { id: 247, label: "Compressible turbulent boundary layers" },
    { id: 248, label: "A generalized Reynolds analogy for compressible wall-bounded turbulent flows" },
    { id: 249, label: "One-point statistics for turbulent wall-bounded flows at Reynolds numbers up to δ+ ~ 2000" },
   //ID = 115 { id: 250, label: "Numerical study of acoustic radiation due to a supersonic turbulent boundary layer" },
    { id: 251, label: "Simulation and validation of a spatially evolving turbulent boundary layer up to Reθ=8300" },
  //ID = 120  { id: 252, label: "Direct Numerical Simulation of Supersonic Turbulent Boundary Layer over a Compression Ramp" },
    { id: 253, label: "Flow physics and RANS modelling of oblique shock/turbulent boundary layer interaction" },
  //ID = 116  { id: 254, label: "Compressible turbulent channel flows: DNS results and modelling" },
    { id: 255, label: "On the logarithmic region in wall turbulence" },
    { id: 256, label: "Direct numerical simulation of hypersonic turbulent boundary layers. Part 2. Effect of wall temperature" },
    { id: 257, label: "Numerical study of turbulent supersonic isothermal-wall channel flow" },
    { id: 258, label: "Probing high-Reynolds-number effects in numerical boundary layers" },
    { id: 259, label: "DNS of the very large anisotropic scales in a turbulent channel" },
    { id: 260, label: "Self-consistent high-Reynolds-number asymptotics for zero-pressure-gradient turbulent boundary layers" },
  //ID = 183  { id: 261, label: "Response of Hypersonic Turbulent Boundary Layer to Favorable Pressure Gradients" },
   //ID = 185 { id: 283, label: "A high-resolution code for turbulent boundary layers" },
    { id: 284, label: "Optimization of Nonlinear Error Sources for Weighted Essentially Non-Oscillatory Methods in Direct Numerical Simulations of Compressible Turbulence" },


  ]);

  // create an array with edges
  var edges = new vis.DataSet([
    //start citations 1 - 13
    { from: 1, to: 2, color: { highlight: '#1F51FF'} },
    { from: 1, to: 3 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 4, color: { highlight: '#1F51FF'} },
    { from: 1, to: 5, color: { highlight: '#1F51FF'} },
    { from: 1, to: 6 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 7 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 8 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 9 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 10 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 11 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 12 , color: { highlight: '#1F51FF'}},
    { from: 1, to: 13 , color: { highlight: '#1F51FF'}},
    ///start references 14 - 38
    { from: 14, to: 1, color: { highlight: '#FF3131'} },
    { from: 15, to: 1, color: { highlight: '#FF3131'}},
    { from: 16, to: 1, color: { highlight: '#FF3131'} },
    { from: 17, to: 1, color: { highlight: '#FF3131'} },
    { from: 18, to: 1 , color: { highlight: '#FF3131'}},
    { from: 19, to: 1 , color: { highlight: '#FF3131'}},
    { from: 20, to: 1 , color: { highlight: '#FF3131'}},
    { from: 21, to: 1 , color: { highlight: '#FF3131'}},
    { from: 22, to: 1 , color: { highlight: '#FF3131'}},
    { from: 23, to: 1 , color: { highlight: '#FF3131'}},
    { from: 24, to: 1 , color: { highlight: '#FF3131'}},
    { from: 25, to: 1 , color: { highlight: '#FF3131'}},
    { from: 26, to: 1, color: { highlight: '#FF3131'} },
    { from: 27, to: 1, color: { highlight: '#FF3131'}},
    { from: 28, to: 1, color: { highlight: '#FF3131'} },
    { from: 29, to: 1, color: { highlight: '#FF3131'} },
    { from: 30, to: 1 , color: { highlight: '#FF3131'}},
    { from: 31, to: 1 , color: { highlight: '#FF3131'}},
    { from: 32, to: 1 , color: { highlight: '#FF3131'}},
    { from: 33, to: 1 , color: { highlight: '#FF3131'}},
    { from: 34, to: 1 , color: { highlight: '#FF3131'}},
    { from: 35, to: 1 , color: { highlight: '#FF3131'}},
    { from: 36, to: 1 , color: { highlight: '#FF3131'}},
    { from: 37, to: 1 , color: { highlight: '#FF3131'}},
    { from: 38, to: 1 , color: { highlight: '#FF3131'}},
    //paper 2 citations 39 to 77
    { from: 39, to: 40, color: { highlight: '#1F51FF'} },
    { from: 39, to: 41, color: { highlight: '#1F51FF'}},
    { from: 39, to: 42, color: { highlight: '#1F51FF'} },
    { from: 39, to: 43 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 44, color: { highlight: '#1F51FF'} },
    { from: 39, to: 45 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 46, color: { highlight: '#1F51FF'} },
    { from: 39, to: 47 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 48, color: { highlight: '#1F51FF'} },
    { from: 39, to: 49 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 50, color: { highlight: '#1F51FF'} },
    { from: 39, to: 51 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 52, color: { highlight: '#1F51FF'} },
    { from: 39, to: 53 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 54, color: { highlight: '#1F51FF'} },
    { from: 39, to: 55 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 56, color: { highlight: '#1F51FF'} },
    { from: 39, to: 57 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 58, color: { highlight: '#1F51FF'} },
    { from: 39, to: 59 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 60, color: { highlight: '#1F51FF'} },
    { from: 39, to: 61, color: { highlight: '#1F51FF'}},
    { from: 39, to: 62, color: { highlight: '#1F51FF'} },
    { from: 39, to: 63 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 64, color: { highlight: '#1F51FF'} },
    { from: 39, to: 65, color: { highlight: '#1F51FF'}},
    { from: 39, to: 66, color: { highlight: '#1F51FF'} },
    { from: 39, to: 67 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 68, color: { highlight: '#1F51FF'} },
    { from: 39, to: 69, color: { highlight: '#1F51FF'}},
    { from: 39, to: 70, color: { highlight: '#1F51FF'} },
    { from: 39, to: 71, color: { highlight: '#1F51FF'}},
    { from: 39, to: 72, color: { highlight: '#1F51FF'} },
    { from: 39, to: 73 , color: { highlight: '#1F51FF'}},
    { from: 39, to: 74, color: { highlight: '#1F51FF'} },
    { from: 39, to: 75, color: { highlight: '#1F51FF'}},
    { from: 39, to: 76, color: { highlight: '#1F51FF'} },
    { from: 39, to: 77, color: { highlight: '#1F51FF'}},
    // paper 2 references: 78 - 130
    { from: 78, to: 39, color: { highlight: '#FF3131'} },
    { from: 79, to: 39, color: { highlight: '#FF3131'}},
    { from: 80, to: 39, color: { highlight: '#FF3131'} },
    { from: 81, to: 39, color: { highlight: '#FF3131'} },
    { from: 82, to: 39, color: { highlight: '#FF3131'} },
    { from: 83, to: 39, color: { highlight: '#FF3131'}},
    { from: 84, to: 39, color: { highlight: '#FF3131'} },
    { from: 85, to: 39, color: { highlight: '#FF3131'} },
    { from: 86, to: 39, color: { highlight: '#FF3131'} },
    { from: 87, to: 39, color: { highlight: '#FF3131'}},
    { from: 88, to: 39, color: { highlight: '#FF3131'} },
    { from: 89, to: 39, color: { highlight: '#FF3131'} },
    { from: 90, to: 39, color: { highlight: '#FF3131'} },
    { from: 91, to: 39, color: { highlight: '#FF3131'}},
    { from: 92, to: 39, color: { highlight: '#FF3131'} },
    { from: 93, to: 39, color: { highlight: '#FF3131'} },
    { from: 94, to: 39, color: { highlight: '#FF3131'} },
    { from: 95, to: 39, color: { highlight: '#FF3131'}},
    { from: 96, to: 39, color: { highlight: '#FF3131'} },
    { from: 97, to: 39, color: { highlight: '#FF3131'} },
    { from: 98, to: 39, color: { highlight: '#FF3131'} },
    { from: 99, to: 39, color: { highlight: '#FF3131'}},
    { from: 100, to: 39, color: { highlight: '#FF3131'} },
    { from: 101, to: 39, color: { highlight: '#FF3131'} },
    { from: 102, to: 39, color: { highlight: '#FF3131'} },
    { from: 103, to: 39, color: { highlight: '#FF3131'}},
    { from: 104, to: 39, color: { highlight: '#FF3131'} },
    { from: 105, to: 39, color: { highlight: '#FF3131'} },
    { from: 106, to: 39, color: { highlight: '#FF3131'} },
    { from: 107, to: 39, color: { highlight: '#FF3131'}},
    { from: 108, to: 39, color: { highlight: '#FF3131'} },
    { from: 109, to: 39, color: { highlight: '#FF3131'} },
    { from: 110, to: 39, color: { highlight: '#FF3131'} },
    { from: 111, to: 39, color: { highlight: '#FF3131'}},
    { from: 112, to: 39, color: { highlight: '#FF3131'} },
    { from: 113, to: 39, color: { highlight: '#FF3131'} },
    { from: 114, to: 39, color: { highlight: '#FF3131'} },
    { from: 114, to: 39, color: { highlight: '#FF3131'}},
    { from: 115, to: 39, color: { highlight: '#FF3131'} },
    { from: 116, to: 39, color: { highlight: '#FF3131'} },
    { from: 117, to: 39, color: { highlight: '#FF3131'} },
    { from: 118, to: 39, color: { highlight: '#FF3131'}},
    { from: 119, to: 39, color: { highlight: '#FF3131'} },
    { from: 120, to: 39, color: { highlight: '#FF3131'} },
    { from: 121, to: 39, color: { highlight: '#FF3131'} },
    { from: 122, to: 39, color: { highlight: '#FF3131'}},
    { from: 123, to: 39, color: { highlight: '#FF3131'} },
    { from: 124, to: 39, color: { highlight: '#FF3131'} },
    { from: 125, to: 39, color: { highlight: '#FF3131'} },
    { from: 126, to: 39, color: { highlight: '#FF3131'}},
    { from: 127, to: 39, color: { highlight: '#FF3131'} },
    { from: 128, to: 39, color: { highlight: '#FF3131'} },
    { from: 129, to: 39, color: { highlight: '#FF3131'} },
    { from: 130, to: 39, color: { highlight: '#FF3131'}},
    // paper 3 citations 131 - 134
    { from: 131, to: 132, color: { highlight: '#1F51FF'} },
    { from: 131, to: 133, color: { highlight: '#1F51FF'}},
    { from: 131, to: 134, color: { highlight: '#1F51FF'} },
    // references 135 - 172
    { from: 135, to: 131, color: { highlight: '#FF3131'} },
    { from: 136, to: 131, color: { highlight: '#FF3131'}},
    { from: 137, to: 131, color: { highlight: '#FF3131'} },
    { from: 138, to: 131, color: { highlight: '#FF3131'} },
    { from: 139, to: 131, color: { highlight: '#FF3131'} },
    { from: 140, to: 131, color: { highlight: '#FF3131'} },
    { from: 141, to: 131, color: { highlight: '#FF3131'}},
    { from: 142, to: 131, color: { highlight: '#FF3131'} },
    { from: 143, to: 131, color: { highlight: '#FF3131'} },
    { from: 144, to: 131, color: { highlight: '#FF3131'} },
    { from: 145, to: 131, color: { highlight: '#FF3131'} },
    { from: 146, to: 131, color: { highlight: '#FF3131'}},
    { from: 147, to: 131, color: { highlight: '#FF3131'} },
    { from: 148, to: 131, color: { highlight: '#FF3131'} },
    { from: 149, to: 131, color: { highlight: '#FF3131'} },
    { from: 150, to: 131, color: { highlight: '#FF3131'} },
    { from: 151, to: 131, color: { highlight: '#FF3131'}},
    { from: 152, to: 131, color: { highlight: '#FF3131'} },
    { from: 153, to: 131, color: { highlight: '#FF3131'} },
    { from: 154, to: 131, color: { highlight: '#FF3131'} },
    { from: 155, to: 131, color: { highlight: '#FF3131'} },
    { from: 156, to: 131, color: { highlight: '#FF3131'}},
    { from: 157, to: 131, color: { highlight: '#FF3131'} },
    { from: 158, to: 131, color: { highlight: '#FF3131'} },
    { from: 92, to: 131, color: { highlight: '#FF3131'} },
    { from: 160, to: 131, color: { highlight: '#FF3131'} },
    { from: 161, to: 131, color: { highlight: '#FF3131'}},
    { from: 162, to: 131, color: { highlight: '#FF3131'} },
    { from: 163, to: 131, color: { highlight: '#FF3131'} },
    { from: 164, to: 131, color: { highlight: '#FF3131'} },
    { from: 165, to: 131, color: { highlight: '#FF3131'} },
    { from: 115, to: 131, color: { highlight: '#FF3131'}},
    { from: 167, to: 131, color: { highlight: '#FF3131'} },
    { from: 168, to: 131, color: { highlight: '#FF3131'} },
    { from: 169, to: 131, color: { highlight: '#FF3131'} },
    { from: 170, to: 131, color: { highlight: '#FF3131'} },
    { from: 171, to: 131, color: { highlight: '#FF3131'}},
    { from: 172, to: 131, color: { highlight: '#FF3131'} },
    //paper 4 references 173 - 189
    { from: 174, to: 173, color: { highlight: '#FF3131'} },
    { from: 78, to: 173, color: { highlight: '#FF3131'}},
    { from: 176, to: 173, color: { highlight: '#FF3131'} },
    { from: 73, to: 173, color: { highlight: '#FF3131'}},
    { from: 178, to: 173, color: { highlight: '#FF3131'} },
    { from: 179, to: 173, color: { highlight: '#FF3131'}},
    { from: 180, to: 173, color: { highlight: '#FF3131'} },
    { from: 92, to: 173, color: { highlight: '#FF3131'}},
    { from: 115, to: 173, color: { highlight: '#FF3131'} },
    { from: 183, to: 173, color: { highlight: '#FF3131'}},
    { from: 184, to: 173, color: { highlight: '#FF3131'} },
    { from: 185, to: 173, color: { highlight: '#FF3131'}},
    { from: 186, to: 173, color: { highlight: '#FF3131'} },
    { from: 187, to: 173, color: { highlight: '#FF3131'}},
    { from: 188, to: 173, color: { highlight: '#FF3131'} },
    { from: 189, to: 173, color: { highlight: '#FF3131'}},
    //paper 5 citations: 190 - 205
    { from: 190, to: 191, color: { highlight: '#1F51FF'} },
    { from: 190, to: 131, color: { highlight: '#1F51FF'}},
    { from: 190, to: 193, color: { highlight: '#1F51FF'} },
    { from: 190, to: 194, color: { highlight: '#1F51FF'} },
    { from: 190, to: 173, color: { highlight: '#1F51FF'}},
    { from: 190, to: 196, color: { highlight: '#1F51FF'} },
    { from: 190, to: 197, color: { highlight: '#1F51FF'} },
    { from: 190, to: 198, color: { highlight: '#1F51FF'}},
    { from: 190, to: 199, color: { highlight: '#1F51FF'} },
    { from: 190, to: 200, color: { highlight: '#1F51FF'} },
    { from: 190, to: 201, color: { highlight: '#1F51FF'}},
    { from: 190, to: 202, color: { highlight: '#1F51FF'} },
    { from: 190, to: 6, color: { highlight: '#1F51FF'} },
    { from: 190, to: 204, color: { highlight: '#1F51FF'}},
    { from: 190, to: 205, color: { highlight: '#1F51FF'} },
    // paper 5 references: 206 -284
    { from: 80, to: 190, color: { highlight: '#FF3131'} },
    { from: 223, to: 190, color: { highlight: '#FF3131'} },
    { from: 180, to: 190, color: { highlight: '#FF3131'} },
    { from: 225, to: 190, color: { highlight: '#FF3131'} },
    { from: 226, to: 190, color: { highlight: '#FF3131'} },
    { from: 92, to: 190, color: { highlight: '#FF3131'} },
    { from: 228, to: 190, color: { highlight: '#FF3131'} },
    { from: 229, to: 190, color: { highlight: '#FF3131'} },
    { from: 230, to: 190, color: { highlight: '#FF3131'} },
    { from: 231, to: 190, color: { highlight: '#FF3131'} },
    { from: 232, to: 190, color: { highlight: '#FF3131'} },
    { from: 16, to: 190, color: { highlight: '#FF3131'} },
    { from: 234, to: 190, color: { highlight: '#FF3131'} },
    { from: 235, to: 190, color: { highlight: '#FF3131'} },
    { from: 236, to: 190, color: { highlight: '#FF3131'} },
    { from: 237, to: 190, color: { highlight: '#FF3131'} },
    { from: 238, to: 190, color: { highlight: '#FF3131'} },
    { from: 239, to: 190, color: { highlight: '#FF3131'} },
    { from: 240, to: 190, color: { highlight: '#FF3131'} },
    { from: 241, to: 190, color: { highlight: '#FF3131'} },
    { from: 242, to: 190, color: { highlight: '#FF3131'} },
    { from: 243, to: 190, color: { highlight: '#FF3131'} },
    { from: 244, to: 190, color: { highlight: '#FF3131'} },
    { from: 245, to: 190, color: { highlight: '#FF3131'} },
    { from: 246, to: 190, color: { highlight: '#FF3131'} },
    { from: 247, to: 190, color: { highlight: '#FF3131'} },
    { from: 248, to: 190, color: { highlight: '#FF3131'} },
    { from: 249, to: 190, color: { highlight: '#FF3131'} },
    { from: 115, to: 190, color: { highlight: '#FF3131'} },
    { from: 251, to: 190, color: { highlight: '#FF3131'} },
    { from: 120, to: 190, color: { highlight: '#FF3131'} },
    { from: 253, to: 190, color: { highlight: '#FF3131'} },
    { from: 116, to: 190, color: { highlight: '#FF3131'} },
    { from: 255, to: 190, color: { highlight: '#FF3131'} },
    { from: 256, to: 190, color: { highlight: '#FF3131'} },
    { from: 257, to: 190, color: { highlight: '#FF3131'} },
    { from: 258, to: 190, color: { highlight: '#FF3131'} },
    { from: 259, to: 190, color: { highlight: '#FF3131'} },
    { from: 260, to: 190, color: { highlight: '#FF3131'} },
    { from: 183, to: 190, color: { highlight: '#FF3131'} },
    { from: 185, to: 190, color: { highlight: '#FF3131'} },
    { from: 284, to: 190, color: { highlight: '#FF3131'} },

  ]);

  // create a network
  var container = document.getElementById("mynetwork");
  var data = {
    nodes: nodes,
    edges: edges
  };
  var options = {
    nodes : {
        shape : 'dot',
        size : 25,
        color : '#808080', // select color
},
edges: {
    length: 100
  },
  interaction: { hover:true },
  physics: {
    forceAtlas2Based: {
      gravitationalConstant: -100,
      springLength: 30,
      damping: 1,
      avoidOverlap: 10
    },

    maxVelocity: 30,
    minVelocity: 0,
    solver: "forceAtlas2Based"
  }
    }


    function updateNodesColor() {
  // Loop through all nodes
  nodes.forEach(function(node) {
    // Get the edges connected to the node
    var connectedEdges = edges.get({
      filter: function(edge) {
        return (edge.from === node.id || edge.to === node.id);
      }
    });

    // Check if the number of connected edges is greater than three
    if (connectedEdges.length > 3) {
      // Update the color of the node to #990000
      node.color = "#990000";
      nodes.update(node);
    }
  });
}


updateNodesColor(190, '#808080')
updateNodesColor(131,'#808080')
updateNodesColor(39,'#808080')
updateNodesColor(1, '#808080')
updateNodesColor(173, '#808080')


var network = new vis.Network(container, data, options);

</script>