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

EngineeringJanuary 22, 202611 min read

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

Separator sizing based on API 12J and retention time rules produces oversized vessels that still let oil through the water outlet. The physics of droplet coalescence and breakup requires molecular-level simulation.

newtsim engineering

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 water retention time. It works, most of the time. But when the water cut increases above 40%, oil carryover into the water outlet spikes to 500 ppm, exceeding the 200 ppm spec for disposal. When the gas-oil ratio shifts during well cycling, liquid carryover into the gas outlet causes compressor fouling. The separator is doing exactly what the sizing correlations predicted. The correlations just do not account for the actual physics of multiphase separation.

The fundamental assumption behind retention time sizing is that droplets settle by Stokes law in a quiescent fluid. Give the droplets enough time and they settle to the correct phase. But a real separator is not quiescent. The inlet device creates turbulence that breaks droplets. Internal baffles redirect flow but create eddies. Temperature gradients drive natural convection. And the fluid properties (interfacial tension, viscosity, density difference) change with composition, temperature, and pressure in ways that Stokes law alone cannot capture.

What Actually Happens Inside a Separator

Multiphase separation involves three coupled physical processes: momentum separation (bulk phase splitting by density difference), coalescence (small droplets merging into larger ones that settle faster), and breakup (turbulent shear fragmenting large droplets into smaller ones that settle slower). The net separation efficiency depends on the balance between coalescence and breakup, which depends on local turbulence intensity, interfacial tension, and dispersed phase viscosity.

Coalescence is the rate-limiting step for most separator designs. Two oil droplets suspended in water must approach each other, drain the thin water film between them, and merge. The film drainage rate depends on the interfacial mobility (which depends on surfactant concentration at the interface), the van der Waals attraction (which depends on the Hamaker constant of the oil-water-oil system), and the electrostatic repulsion (which depends on the zeta potential, itself a function of brine chemistry and pH).

Every one of these parameters is a molecular-level property. The interfacial tension between crude oil and produced water depends on the asphaltene and naphthenic acid content of the oil and the ionic composition of the brine. These molecules adsorb at the interface, lower the interfacial tension, and change the film drainage dynamics. Empirical correlations for interfacial tension in crude-brine systems have errors of 50-200% because the interfacial properties are controlled by trace components (ppm-level asphaltenes and acids) that correlations cannot resolve.

Molecular Simulation of Interfacial Properties

Molecular dynamics simulation computes interfacial properties from the molecular composition of both phases. Build a simulation box with crude oil components (alkanes, aromatics, resins, asphaltenes) on one side and brine (water + NaCl + CaCl2 + dissolved CO2) on the other. Equilibrate the interface and measure the interfacial tension from the pressure tensor anisotropy. The simulation automatically captures the effect of asphaltene adsorption at the interface. You see the asphaltene molecules migrate to the interface, orient flat against it, and reduce the interfacial tension from 25 mN/m (clean alkane-water) to 5-15 mN/m depending on asphaltene type and concentration.

The simulation also computes the dynamic interfacial properties that govern film drainage. When two droplets approach, fresh interface is created, and surfactant molecules must diffuse to cover it. The Marangoni stress from the surfactant concentration gradient opposes film thinning and can slow coalescence by orders of magnitude. This molecular-level physics is invisible to empirical correlations but critical for predicting separation performance.

From Molecular Properties to Separator Performance

The molecular simulation provides the interfacial and transport properties that a continuum-scale multiphase CFD model needs as inputs. The CFD model solves the Navier-Stokes equations for the continuous phases (oil, water, gas) and tracks the dispersed phase (droplets) using a population balance equation that accounts for coalescence, breakup, and settling.

The coalescence kernel in the population balance equation uses the film drainage rate computed from molecular simulation, not from the typical constant-efficiency assumption. The breakup kernel uses the interfacial tension (also from molecular simulation) to determine the critical Weber number for droplet fragmentation. The settling velocity uses the actual phase densities and viscosities at the local temperature and pressure.

This multi-scale approach predicts separator performance from molecular composition rather than from empirical retention time rules. You can evaluate how a change in water cut affects droplet size distribution and carryover, how a change in crude composition (higher asphaltene content from a new well) changes coalescence rates, how temperature affects separation (cold crude is more viscous, slower coalescence, larger droplets carry over), and how chemical demulsifier dosing affects interfacial properties and separation efficiency.

Separator Internals Design

The multi-scale simulation enables optimization of separator internals (inlet devices, coalescing plates, weir configurations, and outlet structures) based on the actual multiphase flow behaviour rather than rules of thumb.

Inlet devices are critical. A simple inlet deflector creates a high-turbulence zone that breaks droplets and homogenizes the flow. A cyclonic inlet device uses centrifugal force for preliminary gas-liquid separation but can emulsify the liquid phases if the shear is too high. Simulation predicts the droplet size distribution downstream of each inlet device design, accounting for both the beneficial degassing effect and the detrimental emulsification.

Coalescing plate packs accelerate oil-water separation by reducing the settling distance. The plate spacing, angle, and surface treatment all affect performance. Simulation predicts the optimal plate geometry for a given oil-water system. Heavy crude with high asphaltene content requires wider plate spacing (more room for the slowly-coalescing droplets) than light crude.

Weir height and position control the oil-water interface level and the flow pattern in the water section. Too high and oil accumulates in the water section. Too low and water overflows into the oil outlet. The optimal weir height depends on the flow rates, fluid properties, and desired separation quality, all of which the simulation predicts.

Chemical Treatment Optimization

Demulsifier chemicals are routinely added to improve oil-water separation. The global demulsifier market is $2.5B/year. But demulsifier selection is largely trial-and-error: the production chemist tests 5-10 products at various dosages in bottle tests and selects the one that gives the fastest water drop. This works, but it optimizes for bottle test conditions (static, warm, small scale) rather than separator conditions (turbulent, variable temperature, large scale).

Molecular simulation predicts demulsifier performance from the molecular structure of the demulsifier and the oil-water system. The simulation shows how demulsifier molecules compete with natural surfactants (asphaltenes) at the interface, how they disrupt the interfacial film to accelerate drainage, and what concentration is needed for optimal effect. Overdosing actually worsens separation in some systems because excess demulsifier re-stabilises the emulsion, a phenomenon that molecular simulation captures but bottle tests often miss because of scale effects.

A Permian Basin operator used simulation-guided demulsifier selection to reduce chemical costs by 35% while improving water quality from 350 ppm oil-in-water to 120 ppm. The simulation identified that the existing demulsifier was partially re-stabilizing the emulsion at the dosage being used, and that a lower dose of a different chemistry (one that displaced asphaltenes more effectively) gave better results at lower cost.

Produced Water Treatment

Produced water disposal regulations are tightening. Onshore disposal typically requires <500 ppm oil-in-water, but some jurisdictions are moving to <100 ppm. Offshore discharge limits are already at 29 ppm (OSPAR convention) and 42 ppm (US EPA). Meeting these limits requires understanding the droplet size distribution leaving the primary separator and designing downstream treatment (hydrocyclones, flotation, filtration) accordingly.

Multi-scale simulation predicts the full droplet size distribution (not just the median) at the separator water outlet. This is critical because the small droplets (< 10 um) that evade the separator are the hardest to remove downstream. Hydrocyclones have a cut point around 10-15 um; droplets smaller than this pass through. Flotation is effective down to 5 um but requires gas injection. Membrane filtration handles sub-micron droplets but fouls if the larger droplets are not removed first.

By predicting the separator outlet droplet size distribution as a function of operating conditions, the simulation enables design of the complete water treatment train. You size the hydrocyclone for the right throughput and cut point. You specify the flotation unit based on the sub-10 um fraction. And you predict the conditions under which the treatment train will exceed the discharge limit, before it happens in the field.

Economics of Simulation-Guided Separator Design

Traditional separator design approach:

Sizing: API 12J retention time rules
Internals: vendor standard designs
Chemical treatment: bottle test screening
Result: vessels oversized 20-40%, carryover exceeds spec 15% of operating time
Cost: $2M-10M per separator (oversized), $500K-2M/year in chemical treatment

Simulation-guided approach:

Sizing: multi-scale physics with actual fluid properties
Internals: optimized for specific crude-brine system
Chemical treatment: molecular simulation of demulsifier-crude interaction
Result: right-sized vessels, carryover within spec across operating envelope
Cost: 15-25% smaller vessel (capex savings $300K-2.5M), 30-50% chemical savings

For a production facility with 5-10 separators, the capex savings from right-sizing alone pay for the simulation many times over. The ongoing savings from optimized chemical treatment and reduced produced water violations compound year over year. Explore PetroSim for separator and multiphase flow simulation, or discuss your separation challenges with our team.