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‎docs/docs/fmi/API.md

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---
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sidebar_position: 3
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---
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# API Documentation
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Welcome to the Example API documentation. This API allows developers to interact with our services easily and efficiently.
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## Table of Contents
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- [Introduction](#introduction)
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- [Endpoints](#endpoints)
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- [GET /users](#get-users)
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- [POST /users](#post-users)
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## Introduction
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This API provides access to FMU data and allows for the creation and management of simulation schemas and simulations. It is based on REST and returns responses in JSON format.
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## Endpoints
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### FMU endpoints
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| Method | Endpoint | Description |
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|--------|------------------------------|---------------------------------------------|
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| GET | /fmi/fmus/{context} | Retrieve a list of FMUs |
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| POST | /fmi/fmus/{context} | Upload a new FMU |
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| GET | /fmi/fmus/{context}/{fmuName}| Retrieve FMU information within a context |
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| DELETE | /fmi/fmus/{context}/{fmuName}| Delete a FMU within a context |
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---
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### Schema endpoints
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| Method | Endpoint | Description |
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|--------|-----------------------------------|---------------------------------------------|
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| GET | /fmi/schemas/{context} | Retrieve a list of schemas |
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| POST | /fmi/schemas/{context} | Upload a new schema |
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| GET | /fmi/schemas/{context}/{schema_id}| Retrieve a schema within a context |
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| DELETE | /fmi/schemas/{context}/{schema_id}| Delete a schema within a context |
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---
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### Simulation endpoints
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| Method | Endpoint | Description |
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|--------|--------------------------------------------------|-------------------------------------------|
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| GET | /fmi/simulations/{context} | Retrieve a list of running simulations |
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| POST | /fmi/simulations/{context} | Deploy simulation |
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| GET | /fmi/simulations/{context}/{simulation_id} | Retrieve simulation info within a context |
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| DELETE | /fmi/simulations/{context}/{simulation_id} | Delete a simulation within a context |
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| POST | /fmi/simulations/{context}/{simulation_id}/resume| Resume a specific simulation |
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| POST | /fmi/simulations/{context}/{simulation_id}/pause | Stops a specific simulation |

‎docs/docs/fmi/_category_.json

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{
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"label": "FMI Simulation",
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"position": 5,
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"link": {
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"type": "generated-index",
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"description": "OpenTwins supports the Functional Mock-up Interface (FMI) standard for simulation."
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}
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}

‎docs/docs/fmi/concepts.mdx

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---
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sidebar_position: 2
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---
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ThemedImage from '@theme/ThemedImage';
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# FMI Simulation concepts
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:::warning
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The FMI simulation service is currently being tested.
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:::
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## Simulation schema
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The schema is used to create simulation blueprints to store and create several simulation instances.
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The schema can be create for a single FMU or several FMUs. The schema for a single FMU is:
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```json
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{
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"id":"schema1",
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"name":"Schema 1",
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"description":"Sample schema",
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"relatedTwins":[
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"Twin1"
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],
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"fmus":[
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{
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"id": "Controller",
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"inputs": [
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{"id": "u_s"},
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{"id": "u_m"}
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],
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"outputs": [
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{"id": "y"}
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]
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}
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]
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}
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```
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<details>
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<summary><b>Schema for several FMUs</b></summary>
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The following schema is designed to be used with multiple FMUs:
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```tsx
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{
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"id":"schema1",
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"name":"Schema 1",
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"description":"Sample schema",
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"relatedTwins":[
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"Twin1"
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],
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"fmus":[
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{
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"id": "Controller",
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"inputs": [
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{"id": "u_s"},
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{"id": "u_m"}
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],
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"outputs": [
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{"id": "y"}
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]
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},
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{
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"id": "Drivetrain",
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"inputs": [
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{"id": "tau"}
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],
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"outputs": [
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{"id": "w"}
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]
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}
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],
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"schema":[
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{
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"from": {"var": "w_ref"},
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"to": {"id": "controller", "var": "u_s"}
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},
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{
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"from": {"id": "drivetrain", "var": "w"},
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"to": {"id": "controller", "var": "u_m"}
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},
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{
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"from": {"id": "controller", "var": "y"},
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"to": {"id": "drivetrain", "var": "tau"}
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},
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{
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"from": {"id": "drivetrain", "var": "w"},
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"to": {"var": "w"}
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}
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]
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}
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```
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This will work properly, although hovering over`ApparentGreetProps`may be a little intimidating. You can reduce this boilerplate with the`ComponentProps` utility detailed below.
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</details>
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## Simulation running schema
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Once you have a schema stored in the system, you can create a simulation instance using that schema:
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```json
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{
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"id":"Simulation1",
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"name":"Simulation1",
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"schemaId": "schema1",
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"targetConnection":{
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"BROKER_TYPE" : "mqtt",
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"BROKER_IP" : "",
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"BROKER_PORT" : "",
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"BROKER_TOPIC" : "",
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"BROKER_USERNAME" : "",
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"BROKER_PASSWORD" : ""
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},
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"configuration":{
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"SIMULATION_START_TIME":1,
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"SIMULATION_END_TIME":7,
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"SIMULATION_STEP_SIZE":1,
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"SIMULATION_DELAY_WARNING": 1,
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"SIMULATION_LAST_VALUE": true,
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"SIMULATION_TYPESCHEDULE": "one-time"
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},
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"inputs":[],
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"outputs": []
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}
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```
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This schema is not different for one or several FMUs execution. It only contains information about execution.

‎docs/docs/fmi/installation.md

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---
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sidebar_position: 1
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---
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# Installation Guide
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:::warning
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The FMI simulation service is currently being tested, so please be patient, as soon as it is properly tested, the public image will be available on Docker Hub.
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:::
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This guide explains how to install the component using two methods:
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1. **Helm** (Work in Progress)
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2. **Manual Installation** using Kubernetes manifests (Deployment and Service)
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## Prerequisites
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This guide asumes that you have OpenTwins already installed.
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Before you begin, ensure you have the following:
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- Access to a Kubernetes cluster
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- `kubectl` installed and configured
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- [Helm](https://helm.sh/docs/intro/install/) (for Helm installation)
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---
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## Method 1: Helm Installation (WIP)
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:::warning
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This method is currently a Work in Progress (WIP) and may not be fully functional yet. We recomend using manual installation.
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:::
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1. **Add the Helm repository** (once available):
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```bash
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helm repo add ertis https://ertis-research.github.io/Helm-charts/
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```
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2. **Update Helm repositories**:
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```bash
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helm repo update
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```
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3. **Install the component**:
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```bash
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helm install <release-name> <chart-name> --namespace <namespace>
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```
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For additional configuration options, refer to the [Helm documentation](https://helm.sh/docs/).
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---
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## Method 2: Manual Installation
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You can manually deploy the component by creating a Kubernetes Deployment resource and a Service.
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### Step 1: Deploy the Kubernetes Deployment
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Create a YAML file for the Deployment (e.g., `deployment.yaml`):
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```yaml
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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labels:
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name: opentwins-fmi-api
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name: opentwins-fmi-api
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spec:
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replicas: 1
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selector:
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matchLabels:
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name: pod-opentwins-fmi-api
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template:
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metadata:
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labels:
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name: pod-opentwins-fmi-api
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name: opentwins-fmi-api
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spec:
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serviceAccountName: ot-agents
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automountServiceAccountToken: true
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containers:
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- image: ertis/opentwins-fmi-simulator-api-v2:latest
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name: opentwins-fmi-api
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env:
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- name: KUBE_NAMESPACE
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value:
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- name: INSIDE_CLUSTER
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value:
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- name: INFLUXDB_HOST
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value:
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- name: INFLUXDB_TOKEN
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value:
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- name: INFLUXDB_DB
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value:
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- name: MINIO_TOKEN
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value:
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- name: MINIO_URL
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value:
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- name: MINIO_A_KEY
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value:
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- name: MINIO_S_KEY
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value:
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- name: POSTGRE_HOST
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value:
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- name: POSTGRE_PORT
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value:
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- name: POSTGRE_DB
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value:
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- name: POSTGRE_USER
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value:
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- name: POSTGRE_PASSWORD
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value:
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- name: BROKER_TYPE
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value:
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- name: BROKER_IP
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value:
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- name: BROKER_PORT
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value:
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- name: BROKER_TOPIC
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value:
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- name: BROKER_USERNAME
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value:
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- name: BROKER_PASSWORD
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value:
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ports:
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- containerPort: 8001
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imagePullPolicy: Always
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```
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Apply the Deployment using the following command:
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```bash
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kubectl apply -f deployment.yaml -n <namespace>
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```
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### Step 2: Create a Kubernetes Service
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Next, create a YAML file for the Service (e.g., `service.yaml`):
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```yaml
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apiVersion: v1
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kind: Service
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metadata:
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name: opentwins-fmi-api
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spec:
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selector:
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name: pod-opentwins-fmi-api
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type: NodePort
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ports:
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- protocol: "TCP"
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port: 8000
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nodePort: <PORT>
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targetPort: 8000
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```
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Apply the Service configuration using the following command:
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```bash
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kubectl apply -f service.yaml -n <namespace>
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```
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### Step 3: Verify the installation
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After deploying both the deployment and the service, veryfy that everything is running correctly:
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```bash
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kubectl get deployments -n <namespace>
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kubectl get services -n <namespace>
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```
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You should see your Deployment and Service listed, and the component should be ready for use.

‎docs/docs/installation/requirements.md

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This page lists both software and hardware requirements for using OpenTwins.
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## Hardware requirements
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- 2 CPU cores
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- 8 GB of RAM
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- ### For standard architecture
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- 2 CPU cores
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- 8 GB of RAM
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- ### For lightweight architecture
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- Raspberry Pi 4
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## Software requirements
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- Container manager:

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