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.

newtsim — Physics-Grounded Industrial Simulation

Resources

Technical deep-dives into solver architecture, validation methodology, and production simulation workflows. Written by the engineers building the platform.

blogMar 11, 2026

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

blogMar 4, 2026

Rheology Prediction: The Hidden Variable That Makes or Breaks Process Simulations

Pipeline pressure drops, coating surface finishes, polymer extrusion rates: all fundamentally governed by rheology. Yet it's one of the most poorly characterized properties in process simulation.

newtsim engineering

blogFeb 25, 2026

Predictive Maintenance with Physics-Based Digital Twins

Equipment failures cost industrial plants millions in downtime. Physics-based digital twins detect anomalies before alarms trigger, predicting failures days or weeks in advance with root cause attribution from first principles.

newtsim engineering

blogFeb 18, 2026

Why Your Drug Delivery Formulations Fail at Scale: Dissolution, Diffusion, and the Physics You're Ignoring

A pharmaceutical company spends 18 months optimizing an oral solid dosage form. Dissolution testing shows 85% release in 45 minutes. The formulation passes every in vitro specification, then fails bioequivalence in Phase 1.

newtsim engineering

blogFeb 11, 2026

Battery Electrolyte Design Is a Molecular Problem: Why Conductivity Predictions from Correlations Miss by 40%

Your electrochemistry team has a promising cathode material. The half-cell performance is excellent. Now you need an electrolyte that delivers high ionic conductivity, wide electrochemical stability window, and operates below -20°C.

newtsim engineering

blogFeb 4, 2026

Catalyst Screening Without a Lab: How Molecular Simulation Predicts Activity, Selectivity, and Deactivation

A chemicals company needs a more selective catalyst for propylene oxide production. The current catalyst achieves 88% selectivity, but the 12% byproduct stream costs $15M/year in separation and waste treatment.

newtsim engineering

blogJan 28, 2026

Chemical Reactor Mixing Problems: When CFD Alone Gets the Chemistry Wrong

Your CSTR was designed for 92% yield. After commissioning, it delivers 78%. The reaction kinetics are well-characterized: lab-scale calorimetry confirmed the rate constants, and the thermodynamics are solid. So why is the plant-scale reactor underperforming?

newtsim engineering

blogJan 21, 2026

Multiphase Flow in Oil and Gas Separators: Why Empirical Sizing Correlations Leave 20% on the Table

A production facility in the Permian Basin processes 30,000 barrels per day through a three-phase separator. The separator was sized using API 12J guidelines with a 10-minute oil retention time and 5-minute gas retention time. Liquid carryover in the gas stream exceeds 0.1 gal/MMscf twice a week.

newtsim engineering

blogJan 14, 2026

Heat Exchanger Fouling Prediction: Moving from Fixed Schedules to Physics-Based Condition Monitoring

A refinery operates 200+ shell-and-tube heat exchangers in its crude preheat train. Every exchanger fouls. It is thermodynamically inevitable when hot crude contacts metal surfaces. The question is how fast and when to clean.

newtsim engineering

blogJan 7, 2026

Polymer Processing and Crystallization: Predicting Microstructure from Molecular Simulation Instead of Trial Runs

An injection molder receives a new lot of polypropylene from the resin supplier. Same grade number, same datasheet properties: MFI 12 g/10min, density 0.905 g/cm3, tensile strength 34 MPa. The operator runs the normal molding cycle, and the parts warp.

newtsim engineering

blogDec 31, 2025

Supercritical Fluid Extraction: Why Solubility Predictions from Equations of State Miss by Orders of Magnitude

A botanical extraction company is designing a supercritical CO2 process to extract cannabinoids from hemp biomass. The target: 90%+ extraction efficiency for CBD and CBG while minimizing co-extraction of chlorophyll and waxes. Peng-Robinson EOS predictions are off by 10-100x.

newtsim engineering

blogDec 24, 2025

Nanoparticle Formulation Stability: Predicting Aggregation, Ostwald Ripening, and Shelf Life from Molecular Physics

A pharmaceutical company develops a nanoparticle suspension for injectable drug delivery. Particle size: 120 nm by DLS, polydispersity index 0.08, zeta potential -35 mV. By every standard stability metric, this formulation looks excellent. Six months later, it has aggregated into micron-scale clusters.

newtsim engineering

blogDec 17, 2025

Membrane Separation Process Design: Why Permeability-Selectivity Tradeoffs from Solution-Diffusion Models Are Not Enough

A gas processing company needs to upgrade a natural gas stream from 8% CO2 to pipeline spec (< 2% CO2) at a remote wellhead where amine treating is not practical. Membrane separation is the obvious choice: lower capex, no chemicals, minimal operator attention.

newtsim engineering