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

EngineeringDecember 18, 202512 min read

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

Membrane gas separation performance depends on polymer free volume, chain mobility, and gas-polymer interactions at the molecular level. Solution-diffusion models cannot predict these for novel membrane materials.

newtsim engineering

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: compact, no chemicals, low maintenance. The engineer selects a commercial polyimide membrane with published CO2/CH4 selectivity of 30 and CO2 permeability of 10 Barrer. The process model predicts a 2-stage system with 500 m2 of membrane area will meet spec at 90% methane recovery.

After installation, the system achieves only 82% methane recovery. The CO2 permeation rate is 30% lower than the published value because the feed gas contains 200 ppm H2S (which competes for sorption sites in the membrane), the operating temperature is 45C (not the 35C at which the published data was measured), and plasticization by CO2 at the high partial pressure in the feed has reduced the effective selectivity from 30 to 18.

These are not installation errors or manufacturing defects. They are predictable consequences of molecular-level phenomena in the membrane material that the solution-diffusion model, with its constant permeability and selectivity coefficients, cannot capture. Every membrane process operates in conditions that differ from the conditions where membrane properties were measured. And the deviations are not small. They routinely cause 20-40% discrepancies between design predictions and field performance.

How Membranes Separate Gases

Dense polymer membranes separate gases through the solution-diffusion mechanism. Gas molecules dissolve into the upstream face of the membrane (sorption), diffuse through the polymer matrix driven by the concentration gradient, and desorb from the downstream face. The permeability P is the product of the solubility coefficient S (how much gas the polymer absorbs) and the diffusion coefficient D (how fast the gas moves through the polymer).

Selectivity between two gases (say CO2 and CH4) depends on both the solubility selectivity (S_CO2/S_CH4) and the diffusivity selectivity (D_CO2/D_CH4). CO2 is more soluble than CH4 in most polymers due to its quadrupole moment and condensability. CO2 is also more mobile than CH4 in glassy polymers because its kinetic diameter is smaller (3.3 A vs 3.8 A). Both factors favor CO2 permeation, giving the useful CO2/CH4 selectivity that makes membrane gas processing possible.

But the standard solution-diffusion model treats S and D as constants, properties of the gas-polymer pair measured at one condition and applied everywhere. In reality, both S and D depend on temperature, pressure, gas composition, and the physical aging history of the membrane. These dependencies are governed by molecular-level interactions between gas molecules and the polymer matrix.

Plasticization: The Performance Killer

Plasticization is the most economically significant deviation from ideal membrane behavior. When CO2 dissolves into a glassy polymer at high partial pressure, it increases the polymer chain mobility, effectively reducing the glass transition temperature. This swells the polymer, increases the free volume, and dramatically increases the permeability of all gas species. The effect on selectivity is devastating: the membrane becomes more permeable but less selective, because the larger free volume allows CH4 (the larger molecule) to permeate faster.

Plasticization onset occurs at a critical CO2 partial pressure that depends on the polymer chemistry, crosslink density, and thermal history. For standard polyimide membranes, onset is typically 8-15 bar CO2 partial pressure. At 8% CO2 and 70 bar total feed pressure, the CO2 partial pressure is 5.6 bar, close to the onset threshold. At 15% CO2 (some wells), the partial pressure exceeds onset and selectivity degrades rapidly.

The standard solution-diffusion model with constant coefficients does not predict plasticization. You either need experimental data at the actual operating conditions (which requires fabricating and testing the membrane at high pressure, a non-trivial experiment) or molecular simulation that computes the CO2-polymer interaction from first principles.

Molecular Simulation of Gas Transport in Polymers

Molecular dynamics simulation of gas permeation starts with building a realistic model of the polymer membrane. An amorphous polymer packing (typically 5,000-50,000 atoms) is generated by placing polymer chains in a simulation box and equilibrating at the target temperature. The resulting structure contains the free volume elements (transient voids between polymer chains) through which gas molecules hop.

Our physics-first simulation approach directly computes gas transport by inserting gas molecules (CO2, CH4, N2, H2S) into the polymer matrix and running molecular dynamics. The simulation captures gas dissolution, with CO2 molecules occupying free volume elements and interacting with polar groups in the polymer (carbonyl, imide, ether). It captures gas diffusion, with molecules hopping between free volume elements as polymer chains undergo segmental motion. And it captures the composition dependence: as more CO2 dissolves, it swells the matrix, creating larger free volume elements and faster diffusion for all species.

The simulation predicts the sorption isotherm (solubility vs. pressure, including the concave-to-convex transition that signals plasticization onset), the diffusion coefficient (as a function of gas concentration in the membrane), and the permeability and selectivity at any operating condition (pressure, temperature, gas composition). These predictions emerge from the molecular interactions without fitted parameters.

Mixed-Gas Effects and Competitive Sorption

Membrane properties measured with pure gases do not predict mixed-gas performance. When CO2 and CH4 are present simultaneously, they compete for sorption sites in the polymer. CO2 preferentially occupies the Langmuir sites (microvoids in the glassy polymer), reducing the available sorption capacity for CH4. The result: CH4 permeability in the mixture is lower than the pure-gas value, while CO2 permeability may be similar or slightly lower. The net effect on selectivity can go either way.

H2S presents an even more complex scenario. H2S is highly condensable and strongly interacting with polar polymer groups. Even at 200 ppm in the feed, H2S can occupy a disproportionate fraction of sorption sites, reducing CO2 sorption and permeation. At higher concentrations (1-5%), H2S can plasticize the membrane more aggressively than CO2, causing catastrophic selectivity loss.

Molecular simulation captures mixed-gas effects naturally. The simulation includes all gas species at their actual partial pressures. Competition for sorption sites, plasticization from multiple condensable gases, and the effect of gas-gas interactions within the polymer matrix are all computed from the molecular forces. The result is a mixed-gas permeability that can differ from the pure-gas value by 20-50%, a deviation that changes the required membrane area and the achievable recovery.

Physical Aging: Why Membrane Performance Drifts

Glassy polymer membranes are not in thermodynamic equilibrium. The polymer chains are frozen in a non-equilibrium packing with excess free volume compared to the equilibrium liquid state. Over time (months to years) the polymer slowly relaxes toward equilibrium, reducing free volume, decreasing permeability, and increasing selectivity. This physical aging causes membrane performance to drift over the operational lifetime.

The aging rate depends on the polymer chemistry (how far below Tg the operating temperature is), the membrane thickness (thin-film composite membranes age faster because the surface-to-volume ratio is larger), and the gas environment (condensable gases like CO2 can accelerate or retard aging depending on concentration). Predicting the performance trajectory over 3-5 years of operation requires understanding the molecular mechanism of aging.

Molecular simulation models physical aging by running long-duration simulations of the polymer packing and tracking the free volume evolution. Accelerated molecular dynamics techniques (temperature-accelerated dynamics, metadynamics) extend the accessible timescales from nanoseconds to the microsecond-millisecond range, capturing the slow relaxation processes that drive aging. The simulation predicts the aging rate and its dependence on conditions, enabling projection of membrane performance over the operational lifetime.

Designing New Membrane Materials

The Robeson upper bound is the empirical tradeoff between permeability and selectivity for polymer membranes: more permeable polymers tend to be less selective, and vice versa. Exceeding the upper bound (achieving both high permeability and high selectivity) requires designing polymer structures with molecular-level precision.

Polymers of intrinsic microporosity (PIMs), thermally rearranged (TR) polymers, and mixed-matrix membranes (MMMs) with metal-organic framework (MOF) fillers are current approaches to exceeding the upper bound. Each creates a specific free volume architecture that enables selective gas transport. PIMs have rigid, contorted backbones that prevent efficient packing, creating permanent microporosity. TR polymers have ladder-like structures with well-defined pore sizes. MOF fillers add crystalline micropores with apertures tuned to discriminate between gas molecules by size.

Molecular simulation guides the design of these advanced membranes by predicting the free volume distribution from the polymer or MOF structure, the gas solubility and diffusivity in each material, the plasticization resistance (how much CO2 the material can absorb before swelling), and the aging rate (how quickly the excess free volume relaxes). For MOF-based MMMs, the simulation also predicts the MOF-polymer interfacial compatibility, since poor compatibility creates non-selective voids at the interface that destroy selectivity.

The design loop is rapid: propose a new polymer structure (backbone chemistry, side groups, crosslink density), simulate the gas transport properties, evaluate against target specifications, and iterate. Each candidate takes 2-5 days of computation. Synthesizing and testing the same candidate experimentally takes 2-6 months. The simulation enables exploration of hundreds of candidates in the time it takes to test five experimentally.

Process Simulation with Realistic Membrane Properties

Membrane process design requires solving the mass balance equations across the membrane module, accounting for the pressure profile along the module length, the changing gas composition (and therefore driving force) from feed to retentate, the temperature profile (permeation is exothermic for condensable gases), and the concentration dependence of permeability (plasticization, competitive sorption).

Standard process simulators use constant permeabilities. This is acceptable for low-pressure, low-concentration applications where the membrane operates well below plasticization onset. For high-pressure natural gas processing, landfill gas upgrading, or biogas purification (where CO2 partial pressures routinely exceed plasticization onset), constant permeabilities give wrong answers.

Multi-scale simulation couples the molecular-level transport model (which provides permeability as a function of local conditions) with the continuum-scale process model (which solves the module mass balance). The permeability is updated at each point along the module length based on the local gas composition and pressure. This captures the fact that the membrane operates in different regimes along the module: near the feed end, high CO2 partial pressure may cause plasticization; near the retentate end, low CO2 partial pressure gives ideal separation. A constant-permeability model averages over these regimes and gets the overall performance wrong.

Membrane Module and Process Optimization

With accurate, condition-dependent membrane properties, the process model can optimize the complete membrane system. Key design variables include membrane area and configuration (single-stage, two-stage with recycle, two-stage with different membranes), operating pressure (higher pressure increases driving force but worsens plasticization), temperature (affects both permeability and selectivity), permeate pressure (lower improves recovery but increases recompression cost), and stage cut (fraction of feed that permeates, which determines product purity and recovery tradeoff).

The optimization finds the minimum-cost system that meets the product specification. For natural gas processing, the cost function includes membrane capital cost ($50-200/m2 installed), compressor capital and operating cost, methane loss (methane that permeates with CO2 is lost revenue), and membrane replacement cost (typically every 3-5 years).

A gas processing company used simulation-guided membrane process design for a 50 MMscfd natural gas sweetening application. The simulation predicted that a 2-stage system with interstage heating (to reverse plasticization between stages) would achieve 95% methane recovery at 2% CO2, compared to 88% recovery with the original isothermal 2-stage design. The 7% improvement in methane recovery at this flow rate is worth $2.4M/year. The interstage heater cost $180K installed.

Economics of simulation-guided membrane process design:

Pilot testing for membrane selection (5 membranes x 10 conditions): $150K-400K, 3-6 months
Simulation-guided design (50 virtual + 3 pilot conditions): $30K-100K, 4-6 weeks
Oversized membrane system (20-40% excess area from constant-permeability model): $200K-2M unnecessary capex
Methane recovery improvement from accurate process model: 3-7%, worth $500K-5M/year
Membrane lifetime prediction: avoids premature replacement ($100K-500K per replacement event)

For companies deploying membrane systems across multiple sites (where each site has different gas composition, pressure, and temperature), simulation reduces the per-site engineering cost from $200K+ (custom pilot testing) to $30K-50K (simulation with minimal validation). The physics does not change between sites; only the conditions change, and the simulation handles any conditions from first principles. Explore MolSim for membrane and separation process simulation, or discuss your membrane process design challenges with our engineering team.