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Terraform for Small Modular Reactors: Monitoring and Digital Twins

Provision SMR and advanced nuclear monitoring infrastructure with Terraform: digital twins, secure analytics, compliance workloads, and simulation environments.

LLuca Berton1 min read

Small Modular Reactors (SMRs) and advanced nuclear are seeing serious 2026 deployment momentum, with hyperscalers signing PPAs to power AI data centers. Reactor engineering itself sits offline, but the surrounding monitoring, digital twin, regulatory-evidence, and grid-integration infrastructure runs in the cloud. Terraform makes that side reproducible and auditable.

This guide shows how to build an SMR monitoring + digital-twin backend on AWS, with the controls regulators expect.

Architecture

#
LayerAWS service
Plant telemetryIoT Core (private CA) → Kinesis
Digital twinIoT TwinMaker
Long-term archiveS3 + Object Lock (compliance mode)
SimulationEC2 / Batch with HPC AMIs
Regulator evidenceCloudTrail Lake + Athena
AccessSSO + MFA + session recording

Tamper-Evident Telemetry Archive

# 
resource "aws_s3_bucket" "telemetry_archive" {
  bucket              = "smr-telemetry-archive"
  object_lock_enabled = true
}
 
resource "aws_s3_bucket_object_lock_configuration" "telemetry_archive" {
  bucket = aws_s3_bucket.telemetry_archive.id
  rule {
    default_retention {
      mode  = "COMPLIANCE"
      years = 30
    }
  }
}
 
resource "aws_s3_bucket_versioning" "telemetry_archive" {
  bucket = aws_s3_bucket.telemetry_archive.id
  versioning_configuration { status = "Enabled" }
}

Digital Twin via IoT TwinMaker

# 
resource "aws_iottwinmaker_workspace" "smr" {
  workspace_id = "smr-fleet"
  role         = aws_iam_role.twinmaker.arn
  s3_location  = aws_s3_bucket.twinmaker.arn
}
 
resource "aws_iottwinmaker_entity" "reactor_unit_1" {
  entity_id    = "reactor-unit-1"
  entity_name  = "Reactor Unit 1"
  workspace_id = aws_iottwinmaker_workspace.smr.workspace_id
 
  components = {
    sensors = {
      component_type_id = aws_iottwinmaker_component_type.reactor_sensors.component_type_id
    }
  }
}

Compliance Trail With CloudTrail Lake

# 
resource "aws_cloudtrail_event_data_store" "regulator" {
  name                          = "smr-regulator-evidence"
  multi_region_enabled          = true
  organization_enabled          = true
  retention_period              = 2557 # ~7 years
  termination_protection_enabled = true
 
  advanced_event_selector {
    name = "Log all management events"
    field_selector {
      field  = "category"
      equals = ["Management"]
    }
  }
}

HPC Simulation for Loading Studies

# 
resource "aws_batch_compute_environment" "physics_sim" {
  compute_environment_name = "smr-physics-sim"
  type                     = "MANAGED"
  service_role             = aws_iam_role.batch.arn
 
  compute_resources {
    type                = "EC2"
    allocation_strategy = "BEST_FIT_PROGRESSIVE"
    instance_type       = ["hpc7a.96xlarge"]
    min_vcpus           = 0
    desired_vcpus       = 0
    max_vcpus           = 4096
    subnets             = var.private_subnet_ids
    security_group_ids  = [aws_security_group.batch.id]
    instance_role       = aws_iam_instance_profile.batch.arn
  }
}

Access Controls

# 
resource "aws_iam_role" "operator" {
  name = "smr-operator"
 
  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [{
      Effect    = "Allow"
      Principal = { Federated = var.idp_arn }
      Action    = "sts:AssumeRoleWithSAML"
      Condition = {
        Bool        = { "aws:MultiFactorAuthPresent" = "true" }
        NumericLessThan = { "aws:MultiFactorAuthAge" = "3600" }
      }
    }]
  })
}

Best Practices

#
  • Object Lock in COMPLIANCE mode — even root cannot delete telemetry within retention.
  • Separate accounts per plant with AWS Organizations, hub-and-spoke logging.
  • Pin every Terraform module version — regulators ask for the exact code that produced an environment.
  • Reproduce a state from any date by combining S3 versioning + CloudTrail Lake queries.
#
#Terraform#Energy#Nuclear#AWS#Compliance

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