Find Simulation Software Projects Easily

Design resource used: Mostly from NXP

Key RF Performance:

Rated Output Power:
100W (50 dBm) minimum, adjustable from 10-100W.
Small Signal Gain: 50 ±2 dB.

Linearity:
Requires high linearity for 256-QAM, demanding 9-12 dB power backoff for EVM ≤-27 dB. Other modulation schemes (BPSK to 64-QAM) require less backoff.
ACPR: ≥55 dBc for 802.11ah compliance.
Impedance: 50Ω nominal with Input VSWR ≤1.5:1.

Architecture and Systems:
Semiconductor Technology: Recommended GaN on SiC or LDMOS.

Protection:
Comprehensive system against over-temperature (shutdown at greater than 110°C), overcurrent (greater than4A), VSWR (greater than3:1), over-voltage (greater than36V DC), and RF overdrive (greater than15 dBm input).

RF Blanking:
Fast-response (less than 200 ns rising) TTL-compatible blanking with greater than80 dB attenuation for noise reduction and power saving.

Thermal Management:
Designed for a maximum 82W heat dissipation at 100W output, assuming 55% efficiency.
Requires a heat sink with ≤0.5°C/W thermal resistance for industrial operation (Case Temp ≤85°C). Cooling uses forced air with a fan providing ≥25 CFM.

Testing:
Includes mandatory tests for Output Power, Gain, EVM, ACPR, and Thermal Shutdown.

Deliverables:
BOM list
Simulation, schematic, layout (ready for mufactures): on ADS
Design guidelines

Full specification can be found here: https://docs.google.com/document/d/1GpMK3mIuRJxn5owygPLi_JZgw-WoMl_D0zb0d4JgiQ8/edit?tab=t.0#heading=h.7qaxnw66x06p
*Details are opened to discuss for flexibility

Project Description: CFD Aircraft Paint Hangar Ventilation
We are looking for a CFD engineer to perform a ventilation airflow analysis for an aircraft paint hangar (down-draft ventilation concept).

Project Goal / Acceptance Criteria (PASS / FAIL):
The goal of the CFD study is to verify that the hangar ventilation design provides compliant, uniform down-draft airflow over the aircraft during painting operations.

The CFD work will be considered acceptable if the following are achieved:
Aircraft surface airflow
- Air velocity on fuselage and wings is generally within 0.3–0.8 m/s
- No strong jets or significant dead zones around the aircraft
Airflow quality
-Airflow around the aircraft is predominantly smooth and downward
-No large-scale turbulence or recirculation near the aircraft
Ventilation performance
-Airflow distribution is consistent with operation between 4 and 8 air changes per hour (ACH) during painting
Pressure behaviour
-Results indicate a slight negative pressure tendency inside the hangar (qualitative confirmation acceptable)
Operating cases
-Above conditions are checked for both A320 and B737 configurations
If the above criteria are met, the CFD scope is considered complete.

Scope of Work
Develop the full CFD model (geometry, mesh, boundary conditions)
Perform steady-state CFD simulations to assess airflow distribution during painting operations

Aircraft Cases
A320
B737 (simplified geometry: fuselage + wings only)

Operating Conditions (Painting Mode)
Air temperature: 24–27 °C
Relative humidity: 40–70% (can be assumed uniform)
Doors: Closed
Focus on airflow distribution only

Ventilation Concept
Supply air from ceiling diffusers / filters
Downward airflow over aircraft
Exhaust via low-level / floor exhaust points
Balance between supply and exhaust is required

Important Note (Inputs Not Final Yet)
Final 3D / GA drawings are not available at this stage
Hangar height is not confirmed yet
Please prepare a budgetary quotation based on reasonable engineering assumptions, and clearly state all assumptions used

Simulation Requirements
One baseline steady-state CFD per aircraft
One optional optimisation iteration per aircraft if baseline does not meet acceptance criteria
No transient simulations
No particle, chemical, fire, or explosion modelling

Deliverables
Velocity contour plots at aircraft surface and key sections
Streamline plots showing airflow paths
Short written summary of findings and any key issues

Please Include in Your Proposal
All assumptions used (especially hangar height)
Minimum inputs required once final drawings are available
How pricing may change if final geometry differs significantly from assumptions
Estimated timeline

• Write down all Possible questions ?
• Describe your recent experience with similar projects

We are seeking an experienced engineer to design a robust H∞ controller combined with a PID controller for a missile autopilot system, utilizing reinforcement learning techniques. The ideal candidate should have a strong background in control systems, reinforcement learning, and missile guidance systems. You will be responsible for developing algorithms, simulating performance, and optimizing controller parameters. If you have a passion for advanced control methodologies and missile technology, we want to hear from you!

Relevant skills:
- Control Systems Engineering
- H∞ Control Theory
- PID Control Design
- Reinforcement Learning
- Simulation Software (e.g., MATLAB, Simulink)
- Missile Guidance Systems

4×4 inset-fed microstrip patch antenna array on Rogers 4350B (h = 1.524 mm) operating at 2.1 GHz, using a corporate power divider network, targeting gain > 12 dBi, HPBW < 50°, and ~100 MHz bandwidth.

Simulation software preference: CST
Polarization : circular
Impedance of the antenna :50 Ω
Required deliverables : CST simulation file, and explanation
size constraints : 150 x 150

Skills: Electronics, Antenna Design, Signal Processing, Simulation

I am looking for an RF/Antenna Design Engineer to design and optimize a CPW-fed planar antenna in ANSYS HFSS that achieves triple-band operation with S11 response similar to the attached plot.

The antenna must be CPW-fed (Coplanar Waveguide feed) and fabricated on a single-layer PCB substrate.

Target Electrical Requirements:

The antenna must resonate at the following frequencies:

~1.22 GHz

~1.60–1.66 GHz

~2.20–2.23 GHz

With:

Return loss (S11) ≤ −25 dB at all three bands

Preferably deep nulls (−30 dB or better) similar to the attached S-parameter plot

Exact resonance alignment within ±3–5% is acceptable.

Antenna Constraints:

Feeding technique: CPW-fed (mandatory)

Substrate: Rogers RO4350B

εr ≈ 4.4

Thickness = 1.6 mm

Antenna type:

CPW-fed monopole / slot
Board size: Compact (≤ 100 × 100 mm preferred)

Fabrication-friendly design (no vias, single-layer copper)

Simulation & Tools:

Simulation software: ANSYS HFSS (mandatory)

Proper wave port excitation for CPW feed

Radiation boundary / air box must be correctly defined

Deliverables Required:

HFSS project file (.aedt)

Exact antenna dimensions & coordinates

S11 (dB(S11)) plot clearly showing the three resonant bands

Surface current distribution plots at:

~1.22 GHz

~1.60 GHz

~2.22 GHz

Short explanation of:

How each resonance is generated

Role of CPW feed and resonant paths

(Optional but preferred)

Radiation pattern & gain plots

Important Notes:

The antenna must be CPW-fed — microstrip-fed designs will NOT be accepted.

Budget & Timeline:

Budget: Open to reasonable offers (student project)

Timeline: 10–12 days preferred

Skills Required:

RF & Microwave Antenna Design

CPW-fed antenna expertise

ANSYS HFSS

PCB antenna optimization

Skills: Engineering, Electronics, Electrical Engineering, Radio Frequency Engineering, Antenna Design, Signal Processing

We are looking for a knowledgeable freelancer to assist with a university project that involves the use of Simio simulation software. The ideal candidate should have experience in modeling, simulation, and analysis using Simio, and be able to provide support in developing the project from start to finish. Strong communication skills are essential to effectively collaborate and deliver quality results. If you have a passion for simulation and educational projects, we would love to hear from you!

## Upwork Job Post: Build a Deontic x NVIDIA AV Demo (COSMOS + ALPAMAYO + DRIVE Sim)

**Budget:** Open (hourly or fixed)
**Duration:** 4–8 weeks (flexible)
**Location:** Remote (some overlap with US/EU hours preferred)

### Overview

We need a freelancer to build a polished, repeatable demo that showcases **continuous homologation / validation at scale** by integrating:

* **Deontic** (requirement → test intent → scenario generation → evaluation → evidence pack)
* **NVIDIA DRIVE Sim / Omniverse** (simulation execution + artifact capture)
* **NVIDIA COSMOS** (long-tail synthetic generation + condition variation)
* **NVIDIA ALPAMAYO** (Vision-Language-Action model; driving + rationale logging)

Demo story: a rule/ODD change arrives → Deontic detects coverage gaps → COSMOS proposes long-tail variants → Deontic compiles scenarios → DRIVE Sim runs a regression sweep → ALPAMAYO drives + explains → Deontic outputs an **auditable evidence pack**.

---

## What You’ll Build (Deliverables)

### 1) End-to-end demo pipeline (reproducible)

* Requirement/rule change input (simple UI + structured schema)
* “Test intent” object (constraints, ODD tags, metrics)
* Scenario selection + generation (baseline + expanded variants)
* Run manifest builder (scenario matrix, seeds, conditions, model versions)
* Batch sim runner (DRIVE Sim/Omniverse) + standardized outputs

### 2) COSMOS integration (long-tail generator + gating)

* Generate prompts from coverage gaps/test intent
* Retrieve COSMOS outputs (clips/scene proposals/trajectories depending on interface)
* Implement relevance gating + novelty ranking
* Convert selected candidates into simulation-ready scenario variants

### 3) ALPAMAYO integration (Reasoning-Under-Test)

* Integrate ALPAMAYO as the driving policy (or via adapter)
* Capture **actions + structured rationale snippets**
* Log rationale per run and tie it to scenario/run IDs

### 4) Evaluation + Evidence Pack

* Compute metrics & pass/fail checks (e.g., TTC, min gap, collisions, lane violations)
* Cluster failures (by tags/conditions/causal signals)
* Export **Evidence Pack** (PDF/HTML + artifacts bundle) with:

* traceability: requirement → intent → scenarios → runs → metrics
* provenance: COSMOS prompts/transforms, ALPAMAYO config/hash, sim version/build
* representative media: short clips/frames for key failures

### 5) Lightweight Demo UI (4-step flow)

* **Impact** → **Generate** → **Run** → **Evidence**
* Must be stable in live meetings

### 6) “Demo Mode” Reliability

* Deterministic seeds + version stamping
* Caching (precomputed COSMOS outputs and/or sim artifacts) while keeping the flow identical
* One-click reset and rerun
* At least one “golden failure” scenario that reliably produces an explainable outcome

---

## Required Experience

* Strong engineering in **Python** (and/or C++), clean modular services
* Hands-on AV simulation experience (ideally **NVIDIA Omniverse / DRIVE Sim**)
* Familiarity with **OpenSCENARIO / OpenDRIVE** (or similar scenario formats)
* Model integration experience (policy inference loops, logging, evaluation)
* Ability to build a simple UI (React or Streamlit)

### Nice to Have

* ROS/ROS2 or autonomy middleware experience
* Experience with synthetic data generation and domain randomization
* GPU batch orchestration (K8s, Ray, Slurm, cloud batch)

---

## What Success Looks Like

* We can run the demo end-to-end repeatedly with one command/click
* COSMOS and ALPAMAYO are visibly part of the flow (not “behind the curtain”)
* The evidence pack is credible: traceable, versioned, and easy to understand

---

## Tools/Stack (Flexible)

* Orchestrator: Python services (REST/gRPC)
* UI: Streamlit or React
* Storage: JSON manifests + SQLite/Postgres
* Reporting: HTML→PDF or direct PDF generation

---

## To Apply (Please Include)

1. 2–3 examples of similar work (AV sim, Omniverse/DRIVE, robotics demos, ML integration, or test automation)
2. Your proposed approach (architecture + how you’d handle COSMOS/ALPAMAYO integration)
3. Risks/unknowns you see and mitigation plan (especially around determinism + caching)
4. Availability + rate

---

## Screening Questions (Answer Briefly)

1. What AV simulation platforms have you used (Omniverse, CARLA, LGSVL, etc.)?
2. Have you integrated a model into a closed-loop sim before? Describe the logging/evaluation approach.
3. How would you ensure the demo is deterministic and reliable in live meetings?
4. What’s your plan for converting synthetic outputs into simulation-ready scenarios (e.g., OpenSCENARIO actors/trajectories)?
5. If COSMOS/ALPAMAYO interfaces differ from expectations, how would you adapt quickly?

---

If you want, we can share a short “demo contract” doc (scenario domain, metrics, story beats) after you apply.

We are an innovative international design company specializing in Mechanical Engineering, CFD, and FEA. We are seeking a talented Mechanical Engineer to join our dynamic team. The ideal candidate should possess strong problem-solving skills, experience with engineering design principles, and proficiency in simulation software. You will collaborate with cross-functional teams to develop high-quality engineering solutions. If you're passionate about mechanical design and eager to contribute to exciting projects, we want to hear from you!

I’m building a fully 3D printed kids’ shoe. The sole is already modeled and I need help designing a sock-like upper that can be printed in SLS TPU (knit-like stretch + recovery, smooth on skin, depowderable).