What industries does newtsim serve?

newtsim serves oil and gas (flow assurance, drilling fluids, subsea pipeline), pharmaceutical and drug discovery, energy storage (battery materials, electrolytes), semiconductor process simulation, green hydrogen (electrolyser and catalyst design), nuclear decommissioning, aerospace and composites, corrosion and materials, carbon capture, agrochemicals, mining and geotechnical, and fermentation and bioprocessing.

How is newtsim different from standard CFD or simulation software?

Standard simulation tools use empirical models at a single scale. newtsim runs the full physics chain from quantum mechanics and molecular dynamics through to plant-scale CFD and structural mechanics, GPU-native, with direct data flow between every scale and no third-party solver dependencies. Constitutive behaviour is predicted from first principles rather than empirically fitted.

What does a fixed-price simulation study include?

A newtsim fixed-price study has a defined scope, clear deliverables, fixed cost agreed upfront, and is delivered in weeks rather than quarters. Studies typically include a scoping call, physics-grounded simulation using the appropriate newtsim solvers, results analysis, and a written report with actionable recommendations.

What is physics-grounded simulation?

Physics-grounded simulation derives material properties and constitutive behaviour from lower-scale physics (molecular dynamics and quantum mechanics) rather than from empirical fitting. This gives accurate predictions for novel materials and extreme conditions outside the range of experimental data, and enables root-cause analysis at the mechanistic level.

How does newtsim approach oil and gas flow assurance simulation?

newtsim runs a continuous physics chain from molecular wax crystallisation to full pipeline transient multiphase flow, with no empirical handoffs between scales. Wax appearance temperatures, gel strength, and deposition kinetics are predicted from molecular dynamics (newtsim Bond), passed directly to mesoscale morphology (newtsim Pack), and then propagated into multiphase CFD (newtsim Stream), giving flow assurance predictions that hold outside the range of laboratory data.

Can newtsim simulate battery electrolyte behaviour and degradation?

Yes. The Bond solver runs molecular dynamics for Li-ion transport and solid-electrolyte interphase (SEI) formation; Pack handles electrode morphology at the mesoscale; these results couple upward to cell-scale electrochemical models. This gives physics-grounded predictions of electrolyte degradation, capacity fade, and thermal behaviour without relying solely on empirical ageing fits.

How does physics-grounded drug discovery simulation differ from docking software?

Molecular docking software treats the protein and ligand as rigid bodies and scores binding poses by geometry. Physics-grounded simulation uses molecular dynamics to capture conformational flexibility and explicit solvation effects that rigid-body docking misses, predicting binding free energy, ADMET properties, and metabolic activation from first principles. This is particularly valuable for targets where induced-fit binding or solvent-mediated interactions drive selectivity.

What makes GPU-native simulation faster than CPU-based CFD?

GPU parallelism maps naturally to the computational kernels underlying molecular dynamics and CFD: large numbers of independent force or flux calculations that can execute simultaneously. newtsim runs all simulation scales GPU-native, avoiding the CPU-to-GPU data transfers that bottleneck traditional solvers which run physics on CPU and only offload selected operations to the GPU. This removes the transfer overhead and allows the full multiscale chain to run without the latency penalties of heterogeneous pipelines.

Does newtsim replace existing simulation tools like ANSYS or COMSOL?

newtsim fills the multiscale gap, connecting molecular-scale physics to the engineering-scale models that tools like ANSYS operate at. It is not a replacement for FEA-only or single-scale workflows where those tools are the right fit. Where newtsim adds value is in problems where constitutive behaviour, material properties, or interfacial physics need to be derived from lower-scale simulation rather than taken from a handbook or empirical fit.

How long does a fixed-price simulation study take?

Most studies complete in 4 to 8 weeks depending on scope. The standard timeline is: scoping call and problem definition in week 1; simulation execution across the relevant physics scales in weeks 2 to 4; results analysis, validation, and report delivery in weeks 5 to 6. More complex multiscale studies with broader operating envelopes may extend to 8 weeks. Scope and timeline are agreed and fixed before work begins.

What deliverables does a simulation study produce?

A newtsim simulation study delivers: a validated multiscale model calibrated to your system; an operating envelope with physics-based safety margins; a written technical report with findings and actionable recommendations; and optionally CFD visualisations or an ML surrogate model (newtsim Neural) for fast design-space exploration by your engineering team.

Does newtsim work for green hydrogen electrolyser and catalyst design?

Yes. The Stream solver handles proton exchange membrane and alkaline electrolyser CFD: multiphase flow, bubble dynamics, and membrane transport. Bond handles catalyst surface reaction molecular dynamics, giving adsorption energies and reaction barriers from first principles rather than from empirical correlations. Neural then produces fast-to-run surrogate models for design-space exploration and stack optimisation, reducing the iteration cost for new catalyst compositions and electrode architectures.

newtsim is a physics-grounded multiscale simulation consulting firm and software platform. We deliver fixed-price simulation studies and platform licences for industrial clients, covering the full physics chain from quantum mechanics and molecular dynamics through to plant-scale CFD and structural mechanics. All solvers run GPU-native.

Does newtsim use artificial intelligence or machine learning?

Yes. newtsim Neural produces ML-trained surrogate models derived from high-fidelity physics simulations. These surrogates run in milliseconds rather than hours, enabling rapid design-space exploration and real-time control without sacrificing physical accuracy. The ML models are trained on physics simulation data, not empirical measurements, so they inherit the accuracy and extrapolation properties of the underlying physics.

How do I get started with newtsim?

Book a free technical scoping call at newtsim.com/demo. Bring a description of your engineering challenge, the physics you think are important, and any data you have. The call is 45 minutes and produces a problem definition, proposed physics chain, and outline scope that forms the basis of a fixed-price proposal.

Can newtsim simulate non-Newtonian fluids?

Yes. newtsim specialises in non-Newtonian flow: yield-stress fluids, viscoelastic materials, shear-thinning and shear-thickening systems, and complex multiphase flows. The Stream solver handles macroscale CFD for these systems; Bond provides the molecular-scale rheology predictions that replace empirical viscosity fits; and the full chain gives accurate behaviour outside the calibrated range of laboratory data.

Does newtsim work for small companies or startups?

Yes. Fixed-price studies are available to clients of any size. The fixed-price, fixed-scope model means you know the cost before committing, making it accessible for smaller organisations without large simulation teams. Many newtsim clients are startups and scale-ups who need physics-grounded analysis early in product development before investing in laboratory or pilot-scale work.

What is the difference between molecular dynamics and CFD?

Molecular dynamics (MD) simulates the motion of individual atoms and molecules to compute material properties such as viscosity, diffusivity, reaction rates, and phase behaviour, from first principles. Computational fluid dynamics (CFD) models bulk fluid flow at the engineering scale using those material properties as inputs. newtsim connects the two: MD provides accurate constitutive inputs that CFD uses for engineering-scale predictions, rather than relying on empirical correlations.

What is a surrogate model in simulation?

A surrogate model (also called a reduced-order model or emulator) is a fast mathematical approximation of a complex simulation. newtsim Neural builds ML surrogates trained on high-fidelity physics simulation data. Once trained, a surrogate evaluates in milliseconds instead of hours, enabling real-time optimisation and design-space exploration. Because surrogates are trained on physics data, they are more accurate than empirical correlations, especially outside the training range.

What deliverables does a newtsim simulation study produce?

A newtsim simulation study delivers: (1) a validated multiscale model calibrated to your system; (2) an operating envelope with physics-based safety margins; (3) a written technical report with findings and actionable recommendations; and optionally (4) CFD visualisations and an ML surrogate model for fast ongoing design-space exploration by your engineering team.

Resources

EngineeringMarch 12, 202612 min read

Why Your Reservoir Predictions Are Wrong: Empirical vs Physics-First Simulation

Viscosity errors, pressure drop failures, PVT predictions that miss by 50%. If your reservoir simulations are systematically wrong, the problem isn't your data. It's empirical correlations breaking down outside their calibration range. Here's what petroleum engineers are switching to.

newtsim engineering

Your reservoir model predicts 15,000 psi at depth. Actual well pressure: 22,000 psi. Your pipeline pressure drop calculation says 8 MPa. Field measurement: 14 MPa. Your viscosity correlation estimates 120 cP for the crude blend. Lab rheometer: 380 cP.

If these errors sound familiar, you're not dealing with bad data or operator error. You're hitting the fundamental limitation of empirical correlation-based simulation: they only work within the narrow conditions they were calibrated for. Step outside that range: different fluid composition, higher pressure, novel temperature profile. Predictions degrade catastrophically.

Why Viscosity Predictions Fail for Heavy Oil

Viscosity is where empirical correlations fail first. Equations like Pedersen or Lohrenz-Bray-Clark were fitted to light-to-medium crude data from the 1960s-1980s. They break down for heavy oil (API < 20°) with asphaltene content above 5%, gas condensate with retrograde behavior near dewpoint, crude blends with wax or paraffin precipitation, high-shear conditions in pipelines (> 1000 s⁻¹), and extreme reservoir conditions (T > 150°C, P > 50 MPa).

The error isn't random. It's systematic. Correlations underpredict viscosity for heavy crudes (making you undersize pumps) and overpredict for gas condensates (making you overdesign separators). Either way, you're leaving money on the table or risking field failures.

Pipeline Pressure Drop Calculation Errors

Pipeline hydraulics depend on accurate fluid properties across changing conditions. As crude flows 100 km through a pipeline, temperature drops, composition shifts (light ends flash), and shear rate varies by 3 orders of magnitude. Your simulator uses a single viscosity value, or a simple temperature correlation at best, and wonders why predictions miss by 30-50%.

The Peng-Robinson equation of state can't capture wax precipitation onset temperature (WPOT) with 10°C accuracy, non-Newtonian shear-thinning in waxy crudes, asphaltene flocculation triggering viscosity spikes, or emulsion formation when water cut exceeds 20%.

These aren't edge cases. These are normal operating conditions that empirical tools weren't designed to handle.

Empirical Correlations vs Physics-First Simulation: Complete Comparison

Empirical correlations workflow:

Limitations: Only accurate within calibration range. Require lab testing for every new fluid ($50k-$500k). Break down for novel compositions or extreme conditions. No predictive power for transport properties.

Physics-first simulation workflow:

graph LR
    A[Quantum mechanics] --> B[Molecular dynamics]
    B --> C[Statistical mechanics]
    C --> D[Properties: η, κ, D]
    D --> E[Minutes to hours]
    E --> F[Any P/T/composition]
    style F fill:#16a34a,stroke:#15803d,color:#fff

Advantages: Universal accuracy across all conditions. Zero lab testing or calibration required. Handles any composition, any P/T automatically. Predicts viscosity, thermal conductivity, diffusion automatically.

The speed difference isn't what matters. It's iteration. With empirical correlations, testing 100 operating scenarios means 100 lab samples and 6 months. With physics-first simulation, it's 100 GPU runs and 2 days.

How Molecular Dynamics Eliminates Calibration

Here's why molecular simulation doesn't need calibration: it computes interactions from first principles. Our physics-first approach starts with quantum mechanics to determine how electrons arrange around nuclei, giving you the molecular charge distribution. Molecular dynamics uses those charges to simulate millions of atoms interacting under realistic conditions (your actual reservoir P/T). Statistical mechanics extracts macroscopic properties from atomic motions: viscosity from momentum flux, thermal conductivity from energy flux.

No fitting. No tuning. No lab samples. Just physics.

This works because the Schrödinger equation doesn't care if your fluid is methane at STP or hydrogen sulfide at 15,000 psi and 400°F. The physics is universal. You're not extrapolating from limited data. You're simulating the actual molecular behavior.

Multi-Scale Physics Coupling

Industrial processes span 10 orders of magnitude in scale: from quantum-scale reactions on catalyst surfaces (Å) to molecular diffusion through nanopores (nm) to pore-scale multiphase flow (μm-mm) to continuum reservoir flow (m-km).

graph TB
    QM[Quantum Mechanics
Ångström scale] --> MD[Molecular Dynamics
Nanometer scale]
    MD --> CFD[Continuum CFD
Meter scale]
    CFD -.->|Local T, P| MD
    MD -.->|Updated properties| CFD
    
    QM --> Props1[Reaction barriers
Molecular charges]
    MD --> Props2[Transport properties
Phase behavior]
    CFD --> Props3[Field-scale flow
Pressure/temperature]
    
    style QM fill:#1e40af,stroke:#3b82f6,color:#fff
    style MD fill:#ea580c,stroke:#f97316,color:#fff
    style CFD fill:#7c3aed,stroke:#a78bfa,color:#fff

Legacy simulators handle one scale, usually continuum, and use empirical correlations for everything smaller. When those correlations break, the entire model fails. Physics-first platforms run concurrent simulations at multiple scales with bidirectional coupling. The entire system stays synchronized with reality, not frozen to calibration data from 1975.

Choosing the Right Physics-Based Simulation Platform

If you're evaluating physics-based simulation platforms, here's what separates production-ready tools from research projects:

End-to-end workflow integration. Quantum mechanics, molecular dynamics, and continuum CFD in a single platform. No copy-pasting viscosity values between tools. No manual data transfer. The simulation handles coupling automatically.

Pre-validated material libraries. Crude oil fractions (C₆-C₄₀ hydrocarbons), natural gas components (CH₄, C₂H₆, C₃H₈, CO₂, H₂S, N₂), brine compositions (NaCl, CaCl₂, MgCl₂), and EOR chemicals (surfactants, polymers, solvents). Libraries should expose SMILES strings or molecular structures (not just names), so you can modify on the fly.

Experimental validation. Benchmarks against NIST databases for PVT and transport properties, validation studies on real reservoir fluids with quantified error bars, and direct comparisons to lab rheometry and PVT testing.

Deployment options. Cloud platforms offer pay-as-you-go pricing and no infrastructure management. On-premise makes sense if you're running simulations continuously or have data export restrictions.

Implementation: Transitioning from Legacy Tools

Start with a pilot project: Pick a well-characterized reservoir (you have validation data), a problem legacy tools struggle with (heavy oil, high-pressure gas, EOR), and a team that's technically capable and curious. Run new platform in parallel with existing workflow for 2-3 months. Compare predictions to field data. Quantify accuracy improvement and time savings.

Integration with existing tools: Your engineers won't abandon Petrel, PIPESIM, or ECLIPSE overnight. Make sure the new platform exports CSV, JSON, and industry-standard formats, provides Python/MATLAB bindings for scripting, and offers visualization that matches your current workflow. Goal: augmentation, not replacement.

Training and adoption: Molecular simulation is conceptually different from tuning correlation parameters. Invest in 2-day onboarding (how molecular simulation works, when to trust it), hands-on tutorials (run 5-10 example cases, interpret results), and ongoing support (weekly office hours for 3 months). Learning curve is weeks, not months. Don't skip it.

Economics of transition:

Traditional approach per reservoir study:

Lab testing: $50k-$200k
Simulation licenses: $150k-$500k/year
Engineering time: 3-4 months
Total: $300k-$800k and 3-4 months

Physics-first approach:

Simulation platform: $50k-$150k/year (cloud) or $100k capex + $20k/year (on-premise)
No lab testing required
Engineering time: 1-2 weeks
Total: $80k-$180k and 1-2 weeks

Payback time: First reservoir study. ROI scales with number of studies: 10 studies/year saves $2M+ annually.

The Bottom Line

Oil and gas is a low-margin, capital-intensive industry. A 2% improvement in drilling efficiency or 5% increase in recovery factor translates to tens of millions per field. Physics-first simulation delivers faster decisions (explore 100x more scenarios in same time), lower technical risk (accurate predictions reduce field surprises), zero lab spend (no PVT testing for every new well), and better asset management (re-optimize existing fields as conditions change).

Early adopters are already seeing ROI measured in months. The companies that wait will be explaining to shareholders why their competitors drill faster, produce more, and spend less. Explore PetroSim for reservoir and process modeling, or see how physics-first simulation works for your specific challenges.