 Here is a simplified Python code snippet that uses the pydm-core library to calculate the average elemental stress on a given volume. Please note that this is a basic example and might need adjustments based on your specific use case.

```python
from pydm.core import Model, Workflow, SequentialOperator
from pydm.data import ArrayVariable, ScalarVariable
import numpy as np

# Load the model and get all node IDs
model = Model('result_file')
nodes = [node.id for node in model.nodes]

# Find the minimum amount of surrounding elements to get a minimum volume
min_elements = 6  # Adjust this value based on your needs
min_volume_nodes = set()
for node in nodes:
    neighbors = model.nodes[node].neighbors
    if len(neighbors) >= min_elements:
        min_volume_nodes.add(node)

# Read the volume by element and find the minimum list of elements by node to get the volume check
element_volumes = np.zeros(model.elements.size)
for element in model.elements:
    volume = np.prod(element.geometry.dimensions)
    element_volumes[model.elements.index(element)] = volume

min_volume_elements = []
for node in min_volume_nodes:
    element_ids = [element.id for element in model.nodes[node].elements]
    min_volume_elements.append(element_ids)

# Create a workflow to compute equivalent stress averaged on elements
stress = Workflow()
stress.addOperator(SequentialOperator(
    [model.elements.stress, model.elements.volume],
    lambda stress, volume: np.sqrt(np.dot(stress, stress)) * volume
))

# Apply dot product seqv.volume, sum up those on the list of elements, and divide this sum by the total volume on these elements
total_stress = 0
total_volume = sum(element_volumes[element_ids] for element_ids in min_volume_elements)
for element_ids in min_volume_elements:
    stress_values = stress(element_ids)
    total_stress += np.sum(stress_values * element_volumes[element_ids])

average_stress = total_stress / total_volume

# Plot equivalent elemental stress and volume averaged elemental equivalent stress
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(element_volumes[min_volume_elements[0]], stress(min_volume_elements[0]), label='Min Volume Elements')
plt.plot(element_volumes, stress(model.elements.index_array), label='All Elements')
plt.xlabel('Element Volume')
plt.ylabel('Equivalent Stress')
plt.title('Equivalent Stress vs Element Volume')
plt.legend()
plt.show()

# Use the operator with the same algorithm that has been implemented
average_volume_stress = SequentialOperator(
    [ArrayVariable('element_volume'), ArrayVariable('element_stress')],
    lambda volume, stress: np.sqrt(np.dot(stress, stress)) * volume
)

# Save the operator for future use
average_volume_stress.save('average_volume_stress.pydm')
```

This code assumes that you have already installed the pydm-core library and that the result file is in the correct format for the library to read. Adjust the code as needed to fit your specific use case.