# EDEM颗粒操作API代码详解 ## 1. 颗粒替换操作API实现 ### 1.1 基础颗粒替换功能 颗粒替换是EDEM仿真中的重要操作，用于在模拟过程中动态更换颗粒属性或类型，以模拟材料磨损、相变等物理过程[ref_1]。 ```python import edem.api as edem import numpy as np class ParticleReplacementAPI: def __init__(self, simulation): self.sim = simulation self.replacement_rules = {} def define_replacement_rule(self, rule_name, condition_func, replacement_func): """ 定义颗粒替换规则 rule_name: 规则名称 condition_func: 条件判断函数 replacement_func: 替换执行函数 """ self.replacement_rules[rule_name] = { 'condition': condition_func, 'replacement': replacement_func } def execute_replacement(self, time_step): """ 执行颗粒替换操作 time_step: 当前时间步 """ particles = self.sim.get_particles() for particle in particles: for rule_name, rule in self.replacement_rules.items(): if rule['condition'](particle, time_step): # 执行替换操作 new_particle = rule['replacement'](particle) self.sim.replace_particle(particle.id, new_particle) print(f"时间步 {time_step}: 颗粒 {particle.id} 已替换") break # 使用示例 def wear_condition(particle, time_step): """磨损条件：当颗粒应力超过阈值时触发替换""" stress_threshold = 1e6 # Pa return particle.stress > stress_threshold and time_step > 1000 def wear_replacement(particle): """磨损替换：创建新的磨损后颗粒""" new_diameter = particle.diameter * 0.9 # 直径减小10% new_material = edem.Material( density=particle.material.density, youngs_modulus=particle.material.youngs_modulus * 0.8 ) return edem.Particle( position=particle.position, velocity=particle.velocity, diameter=new_diameter, material=new_material ) # 初始化并应用替换规则 sim = edem.Simulation() replacement_api = ParticleReplacementAPI(sim) replacement_api.define_replacement_rule( "wear_replacement", wear_condition, wear_replacement ) ``` ### 1.2 高级条件替换功能 对于更复杂的替换场景，需要实现基于多种条件的智能替换逻辑： ```python class AdvancedReplacementAPI: def __init__(self, simulation): self.sim = simulation self.replacement_history = [] def conditional_replacement_by_region(self, region_bounds, old_material, new_material): """ 基于区域的颗粒替换 region_bounds: 区域边界 [(x_min, x_max), (y_min, y_max), (z_min, z_max)] """ particles = self.sim.get_particles() replaced_count = 0 for particle in particles: if (region_bounds[0][0] <= particle.position[0] <= region_bounds[0][1] and region_bounds[1][0] <= particle.position[1] <= region_bounds[1][1] and region_bounds[2][0] <= particle.position[2] <= region_bounds[2][1] and particle.material.id == old_material.id): # 创建新颗粒保持原有运动状态 new_particle = edem.Particle( position=particle.position, velocity=particle.velocity, angular_velocity=particle.angular_velocity, diameter=particle.diameter, material=new_material ) self.sim.replace_particle(particle.id, new_particle) replaced_count += 1 self.replacement_history.append({ 'time_step': self.sim.current_step, 'particle_id': particle.id, 'old_material': old_material.id, 'new_material': new_material.id }) return replaced_count def time_based_replacement(self, target_material, replacement_schedule): """ 基于时间表的颗粒替换 replacement_schedule: {时间步: 新材料} """ current_step = self.sim.current_step if current_step in replacement_schedule: new_material = replacement_schedule[current_step] particles = self.sim.get_particles_by_material(target_material) for particle in particles: new_particle = edem.Particle( position=particle.position, velocity=particle.velocity, diameter=particle.diameter, material=new_material ) self.sim.replace_particle(particle.id, new_particle) print(f"时间步 {current_step}: 替换了 {len(particles)} 个颗粒") ``` ## 2. 颗粒填充操作API实现 ### 2.1 基础颗粒填充功能 颗粒填充操作用于在容器中生成颗粒床，模拟真实的填充过程[ref_1]。 ```python class ParticleFillingAPI: def __init__(self, simulation): self.sim = simulation def create_container(self, container_id, dimensions, position=(0, 0, 0)): """ 创建填充容器 dimensions: (长, 宽, 高) """ container = edem.Geometry.Box( id=container_id, length=dimensions[0], width=dimensions[1], height=dimensions[2], position=position ) self.sim.add_geometry(container) return container def random_fill_container(self, container, particle_properties, fill_ratio=0.7): """ 随机填充容器 fill_ratio: 填充比率 (0-1) """ container_volume = container.length * container.width * container.height particle_volume = (4/3) * np.pi * (particle_properties['diameter']/2)**3 max_particles = int((container_volume * fill_ratio) / particle_volume) particles_created = 0 max_attempts = max_particles * 10 # 防止无限循环 attempts = 0 while particles_created < max_particles and attempts < max_attempts: # 生成随机位置 x = np.random.uniform( container.position[0] - container.length/2, container.position[0] + container.length/2 ) y = np.random.uniform( container.position[1] - container.width/2, container.position[1] + container.width/2 ) z = np.random.uniform( container.position[2], container.position[2] + container.height ) position = (x, y, z) # 检查位置是否有效（简化版本） if self.is_valid_position(position, container): particle = edem.Particle( position=position, diameter=particle_properties['diameter'], material=particle_properties['material'] ) self.sim.add_particle(particle) particles_created += 1 attempts += 1 return particles_created def is_valid_position(self, position, container): """ 检查位置是否在容器内（简化实现） """ x, y, z = position container_min = [ container.position[0] - container.length/2, container.position[1] - container.width/2, container.position[2] ] container_max = [ container.position[0] + container.length/2, container.position[1] + container.width/2, container.position[2] + container.height ] return (container_min[0] <= x <= container_max[0] and container_min[1] <= y <= container_max[1] and container_min[2] <= z <= container_max[2]) ``` ### 2.2 高级填充算法 针对不同的应用场景，实现多种填充策略： ```python class AdvancedFillingAPI: def __init__(self, simulation): self.sim = simulation def layered_filling(self, container, layers_config): """ 分层填充 layers_config: [{'height': 层高, 'material': 材料, 'diameter': 直径}] """ total_height = 0 for layer_config in layers_config: current_height = total_height + container.position[2] layer_container = edem.Geometry.Box( length=container.length, width=container.width, height=layer_config['height'], position=(container.position[0], container.position[1], current_height + layer_config['height']/2) ) particle_properties = { 'diameter': layer_config['diameter'], 'material': layer_config['material'] } filling_api = ParticleFillingAPI(self.sim) filling_api.random_fill_container(layer_container, particle_properties) total_height += layer_config['height'] def patterned_filling(self, container, pattern_function, particle_properties): """ 模式化填充 pattern_function: 函数，输入(x,y)返回z坐标 """ grid_resolution = particle_properties['diameter'] * 1.5 x_coords = np.arange( container.position[0] - container.length/2, container.position[0] + container.length/2, grid_resolution ) y_coords = np.arange( container.position[1] - container.width/2, container.position[1] + container.width/2, grid_resolution ) for x in x_coords: for y in y_coords: z = pattern_function(x, y) + container.position[2] if (container.position[2] <= z <= container.position[2] + container.height): particle = edem.Particle( position=(x, y, z), diameter=particle_properties['diameter'], material=particle_properties['material'] ) self.sim.add_particle(particle) ``` ## 3. 颗粒床生成API实现 ### 3.1 基础颗粒床生成 颗粒床生成是许多工业仿真的基础，需要创建稳定、真实的颗粒堆积[ref_1]。 ```python class ParticleBedGenerator: def __init__(self, simulation): self.sim = simulation self.bed_profiles = {} def generate_dense_packed_bed(self, container, particle_properties, packing_density=0.64): """ 生成密堆积颗粒床 packing_density: 堆积密度 (0-1) """ # 使用六方最密堆积算法 diameter = particle_properties['diameter'] base_radius = diameter / 2 # 计算基础网格 x_spacing = diameter y_spacing = diameter * np.sqrt(3) / 2 z_spacing = diameter * np.sqrt(2/3) particles_created = 0 bed_height = 0 layer_index = 0 while bed_height < container.height: current_height = container.position[2] + bed_height for i in range(int(container.length / x_spacing)): for j in range(int(container.width / y_spacing)): x = container.position[0] - container.length/2 + i * x_spacing y = container.position[1] - container.width/2 + j * y_spacing # 交替层偏移 if layer_index % 2 == 1: x += x_spacing / 2 y += y_spacing / 2 position = (x, y, current_height) if self.is_valid_position(position, container): particle = edem.Particle( position=position, diameter=diameter, material=particle_properties['material'] ) self.sim.add_particle(particle) particles_created += 1 bed_height += z_spacing layer_index += 1 return particles_created def generate_porous_bed(self, container, particle_properties, porosity=0.4): """ 生成多孔颗粒床 porosity: 孔隙率 """ solid_fraction = 1 - porosity return self.generate_dense_packed_bed(container, particle_properties, solid_fraction) ``` ### 3.2 复杂颗粒床配置 ```python class ComplexBedConfiguration: def __init__(self, simulation): self.sim = simulation def create_multi_material_bed(self, container, material_layers): """ 创建多材料颗粒床 material_layers: [{'height': 高度, 'material': 材料, 'diameter': 直径, 'porosity': 孔隙率}] """ current_height = container.position[2] for layer in material_layers: layer_container = edem.Geometry.Box( length=container.length, width=container.width, height=layer['height'], position=( container.position[0], container.position[1], current_height + layer['height']/2 ) ) particle_properties = { 'diameter': layer['diameter'], 'material': layer['material'] } generator = ParticleBedGenerator(self.sim) if 'porosity' in layer: generator.generate_porous_bed(layer_container, particle_properties, layer['porosity']) else: generator.generate_dense_packed_bed(layer_container, particle_properties) current_height += layer['height'] def create_graded_bed(self, container, base_properties, grading_function): """ 创建级配颗粒床 grading_function: 函数，输入高度返回颗粒直径 """ layer_height = base_properties['diameter'] * 2 current_height = container.position[2] while current_height < container.position[2] + container.height: current_diameter = grading_function(current_height) layer_container = edem.Geometry.Box( length=container.length, width=container.width, height=layer_height, position=( container.position[0], container.position[1], current_height + layer_height/2 ) ) layer_properties = { 'diameter': current_diameter, 'material': base_properties['material'] } generator = ParticleBedGenerator(self.sim) generator.generate_dense_packed_bed(layer_container, layer_properties) current_height += layer_height ``` ## 4. 完整应用示例 ### 4.1 综合仿真场景 ```python def comprehensive_simulation_example(): """ 综合仿真示例：包含颗粒床生成、填充和替换的完整流程 """ # 初始化仿真环境 sim = edem.Simulation() sim.initialize() # 创建容器 container = edem.Geometry.Box( id=1, length=0.1, # 10cm width=0.1, # 10cm height=0.05, # 5cm position=(0, 0, 0.025) ) sim.add_geometry(container) # 定义材料 steel_material = edem.Material( id=1, density=7800, # kg/m³ youngs_modulus=2e11 # Pa ) ceramic_material = edem.Material( id=2, density=3900, youngs_modulus=3e11 ) # 颗粒床生成 bed_generator = ParticleBedGenerator(sim) particle_properties = { 'diameter': 0.001, # 1mm 'material': steel_material } print("开始生成颗粒床...") particle_count = bed_generator.generate_dense_packed_bed(container, particle_properties) print(f"生成了 {particle_count} 个颗粒") # 颗粒填充（在现有床层上添加更多颗粒） filling_api = ParticleFillingAPI(sim) additional_particles = { 'diameter': 0.002, # 2mm 'material': ceramic_material } print("开始补充填充...") filled_count = filling_api.random_fill_container(container, additional_particles, 0.1) print(f"补充填充了 {filled_count} 个颗粒") # 设置替换规则 replacement_api = ParticleReplacementAPI(sim) def high_stress_condition(particle, time_step): return particle.stress > 5e6 # 5MPa应力阈值 def material_upgrade_replacement(particle): return edem.Particle( position=particle.position, velocity=particle.velocity, diameter=particle.diameter, material=ceramic_material # 升级为陶瓷材料 ) replacement_api.define_replacement_rule( "stress_upgrade", high_stress_condition, material_upgrade_replacement ) # 运行仿真 print("开始仿真运行...") for step in range(10000): sim.step() # 每100步检查并执行替换 if step % 100 == 0: replacement_api.execute_replacement(step) print("仿真完成") # 运行示例 if __name__ == "__main__": comprehensive_simulation_example() ``` ### 4.2 API性能优化建议 在实际应用中，还需要考虑性能优化： ```python class OptimizedParticleAPI: def __init__(self, simulation): self.sim = simulation self.spatial_hash = {} # 空间哈希用于快速邻居查找 def update_spatial_hash(self): """更新空间哈希表""" self.spatial_hash.clear() particles = self.sim.get_particles() for particle in particles: cell_x = int(particle.position[0] / self.cell_size) cell_y = int(particle.position[1] / self.cell_size) cell_z = int(particle.position[2] / self.cell_size) cell_key = (cell_x, cell_y, cell_z) if cell_key not in self.spatial_hash: self.spatial_hash[cell_key] = [] self.spatial_hash[cell_key].append(particle) def get_nearby_particles(self, position, radius): """获取指定位置附近的颗粒""" nearby = [] center_cell = ( int(position[0] / self.cell_size), int(position[1] / self.cell_size), int(position[2] / self.cell_size) ) # 检查相邻的27个网格 for dx in [-1, 0, 1]: for dy in [-1, 0, 1]: for dz in [-1, 0, 1]: cell_key = (center_cell[0] + dx, center_cell[1] + dy, center_cell[2] + dz) if cell_key in self.spatial_hash: for particle in self.spatial_hash[cell_key]: distance = np.linalg.norm(np.array(particle.position) - np.array(position)) if distance <= radius: nearby.append(particle) return nearby ``` 以上API代码提供了完整的EDEM颗粒操作功能实现，涵盖了颗粒替换、填充和床层生成的核心需求。这些代码基于EDEM的Python API设计，可以根据具体的EDEM版本和配置进行适当调整。在实际应用中，建议结合具体的物理模型和边界条件进行进一步的优化和验证[ref_1]。