Real time CFD for manufacturing: Synopsys digital twin on Azure with Omniverse

Synopsys’ new simulation-driven framework unveiled at Microsoft Ignite promises to turn slow, high‑fidelity CFD workflows into near‑real‑time decision tools for the factory floor — a multi‑vendor stack that combines Synopsys’ accelerated physics, GPU‑native Ansys Fluent, NVIDIA Omniverse/OpenUSD libraries, and Microsoft Azure — but the headline performance claims warrant careful auditing before procurement or production rollout.

Overview​

The demonstration presented at Microsoft Ignite centers on a practical, industry‑focused use case: Krones’ bottling and filling lines were modeled as a physically accurate digital twin and used to run fluid‑dynamic scenario comparisons that influence real‑time production decisions — for example, adjustments for bottle shape, liquid viscosity, and fill level. The published Synopsys announcement highlights a dramatic runtime reduction from conventional 3–4 hour CFD jobs to “less than 5 minutes” using a GPU‑accelerated, cloud‑native solver and Omniverse/OpenUSD‑based interoperability. Independent partner materials and the SoftServe case study that describes the Krones deployment corroborate the architecture (Ansys Fluent + Azure + NVIDIA acceleration + systems integrator orchestration) and report clear speed‑ups, but they also contain a narrower performance figure — roughly 30 minutes per simulation cycle — that differs from the sub‑5‑minute message in some press wiring. This discrepancy is significant for buyers and must be treated with caution until reproducible benchmarks and fidelity targets are provided.

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Background: Why this matters for manufacturing​

Manufacturers have long struggled with the mismatch between the fidelity of CAE/CFD simulations and the time window in which production decisions must be made. High‑accuracy CFD helps reduce waste, optimize fill and valve timing, and avoid costly rework; yet classic workflows — meshing, solver runs, and post‑processing — often take hours or days. Turning simulation from an offline engineering tool into an online operational instrument would let teams test “what‑if” scenarios during shift changes, tune lines dynamically, and reduce scrap and downtime.
Synopsys’ framework aims straight at that gap by combining three trends:

• GPU acceleration of numerical solvers to reduce wall‑clock time.
• Cloud‑native orchestration (Azure) for scalable compute and ease of deployment.
• OpenUSD/Omniverse for interoperability and visual, collaborative digital twin experiences.

Each of those components is already established: Ansys Fluent has mature GPU acceleration features and recent releases emphasize GPU‑native solver improvements; NVIDIA provides Omniverse libraries and CUDA/CUDA‑X acceleration; Microsoft Azure supplies the cloud backbone and services. Synopsys’ value proposition is packaging accelerated physics, interoperability, and integration patterns into a demonstrable framework.

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Technical architecture — the pieces and how they fit​

Core components​

• Ansys Fluent (GPU‑enabled solver): Used for the CFD heavy lifting; recent Fluent releases explicitly include GPU‑accelerated solver paths and HPC integrations for AMD and NVIDIA accelerators. These GPU solver capabilities are essential to reduce run times compared to CPU‑only HPC clusters.
• Synopsys accelerated physics layer: Synopsys positions its accelerated physics and cloud‑native solvers as part of the stack to augment or orchestrate solver workloads and feed results into engineering workflows. The Synopsys press release frames the accelerated physics as a critical speed lever in the framework.
• NVIDIA Omniverse / OpenUSD libraries: Provide a standardized scene description (OpenUSD) and visualization/collaboration runtime (Omniverse) enabling CAE results to be shared across tools and visualized inside a digital twin of the factory floor. This is key for cross‑discipline collaboration (engineering, operations, R&D).
• Microsoft Azure (cloud orchestration and compute): Azure provides AKS/Kubernetes, Ansys Access on Azure HPC resources, and the cloud networking needed for multi‑tenant, on‑demand compute. Synopsys and partners emphasize Azure’s ability to host scaled GPU nodes and integrate with enterprise identity and governance.
• Systems integrator orchestration (SoftServe, CADFEM): Integration partners brought solver tuning, deployment pipelines, and the web/UI components to make the solution usable by engineers and operations teams. SoftServe’s case study documents their role as the system integrator and orchestrator for the Krones pilot.

How data flows in the demo​

1. CAD and production context (machine geometry, conveyor, bottle models) are modeled and linked into an OpenUSD scene for the digital twin.
2. Parameters (bottle geometry, viscosity, fill level, machine timing) are swept through a scenario engine.
3. Ansys Fluent (GPU solver) executes CFD runs; Synopsys accelerated layers and cloud orchestration optimize job distribution.
4. Results are streamed to an Omniverse visualization and a web-based dashboard for side‑by‑side scenario comparison and operator decisions.
5. Engineers and operations can iterate rapidly, using the twin to evaluate tradeoffs and push advisories to controllers or manual operators.

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The performance claim: “3–4 hours down to under 5 minutes” — verified or aspirational?​

Synopsys’ announcement headlines a reduction from typical 3–4 hour CFD jobs to “less than 5 minutes.” That claim is repeatable through Synopsys’ own release and in press syndication. However, partner‑authored materials (SoftServe’s Krones case study and systems integrator writeups) explicitly report a consistent outcome of roughly 30 minutes per simulation cycle after the integration and tuning exercise — an impressive and meaningful acceleration, but not the same as sub‑5‑minute runtime.

• The Synopsys press release and multiple syndicated news outlets repeat the sub‑5‑minute figure as the headline performance claim.
• The SoftServe Krones case study documents a two‑month development engagement that achieved simulation cycles of about 30 minutes, with project descriptions and stack details. That case study is closer to a verifiable, fielded result and explicitly lists the stack (Ansys Fluent, Ansys Access on Azure, Omniverse, AKS, SoftServe orchestration).
• Independent analyst and community summaries note the discrepancy between the <5‑minute claim and the 30‑minute case study and recommend treating the five‑minute number as an optimistic projection until lab benchmarks or runbooks are published.

Verdict: The architecture and the integrator results are credible; the magnitude of the performance improvement depends heavily on model fidelity, solver options, mesh density, GPU sizing, and whether reduced‑fidelity surrogate models or AI‑assisted accelerators were employed. Buyers should require audited benchmark runs and a reproducible runbook before treating “under 5 minutes” as a guaranteed outcome.

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Why the numbers can vary — a quick primer on CFD, fidelity, and performance​

• Mesh size and physics fidelity: CFD run time scales nonlinearly with mesh cell count and complexity of physics models (multiphase flows, turbulence closures, free‑surface interactions). Reducing mesh or simplifying models speeds runs but can change engineering outcomes.
• Solver model and GPU compatibility: Not all Fluent modules and multiphase/two‑phase formulations are fully GPU‑accelerated; some physics remain CPU‑bound or are less efficient on GPUs. The solver’s GPU‑capable feature set and the chosen turbulence/multiphase models will materially affect runtime.
• Compute configuration and cost: A sub‑5‑minute run often requires significant GPU capacity (A100/H100 or AMD MI300 clusters), fast storage, and parallel job orchestration — compute cost rises with scale. Cloud spot pricing, bursting patterns, and sustained use discounts will determine economics.
• Approximations and surrogate models: Some “near‑real‑time” workflows use reduced‑order models, AI surrogates, or hybrid physics‑ML approaches. These can deliver very fast answers but need validation and guardrails for accuracy. Synopsys and partners reference AI‑assisted workflows in their messaging; the gulf between full‑fidelity CFD and surrogate outputs explains much of the variance.

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Practical implications for IT and OT teams​

Immediate business benefits claimed​

• Faster scenario comparison and on‑the‑fly optimization of bottling/filling operations.
• Lower waste and better resource allocation through more accurate process control.
• Shorter engineering iteration cycles and improved cross‑team collaboration via shared digital twins and Omniverse visualizations.

Operational and procurement checklist​

1. Request an auditable benchmark: reproduce the exact Krones scenario (mesh size, turbulence model, time step, solver flags) and measure wall‑clock time on equivalent cloud GPU nodes.
2. Ask for a fidelity report: compare optimized outputs against ground truth experiments or legacy high‑fidelity runs and quantify error bounds.
3. Validate the exact solver feature set used: confirm whether the tested simulation used GPU‑accelerated modules or CPU fallbacks.
4. Require pricing transparency: get compute cost estimates for your target throughput (e.g., n simulations per shift) across Azure VM/GPU SKUs.
5. Ensure integration artifacts: demand a deployment automation runbook (AKS manifests, container images, autoscaling rules) and a security architecture for data and IP protection.

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Strengths: What’s compelling about the Synopsys framework​

• Open interoperability (OpenUSD): Using OpenUSD/Omniverse as the scene and exchange layer is smart — it lowers friction between CAD, CAE, and visualization tools and helps align engineering and operations around a single source of truth.
• Realistic stack alignment: The stack pairs mature, production‑grade players: Ansys Fluent for CFD, NVIDIA for GPU/Omniverse, Azure for cloud scale, and systems integrators for deployment and domain tuning. That reduces integration risk compared to point solutions.
• Practical use case (Krones): A full assembly‑line digital twin for a high‑volume industrial customer is a credible validation vector; Krones is a sensible early adopter because fluid dynamics in bottling are both challenging and impactful. SoftServe’s case study documents a two‑month delivery with tangible business outcomes, which demonstrates feasibility in an industrial setting.

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Risks and open questions​

• Reproducibility of headline numbers: The <5‑minute claim appears optimistic relative to published integrator results (≈30 minutes). Without a public benchmark suite, the sub‑5‑minute metric should be considered aspirational.
• Fidelity vs. speed tradeoffs: Pushing for extreme speed can require model simplifications or surrogate models; those changes can subtly alter engineering decisions if not carefully validated. Operational teams must understand the confidence bounds for any automated action suggested by the twin.
• Cloud cost and run‑rate economics: Near‑real‑time simulation at scale will incur nontrivial cloud GPU costs. Total cost of ownership (TCO) must include continuous GPU hours, storage, data egress, and platform engineering.
• Security, IP, and data governance: Running design and process models in the cloud raises concerns about IP exposure and regulatory controls. Enterprises need to enforce encryption, identity controls (Entra/AAD), and secure CI/CD for models and dataset handling.
• Operationalizing recommendations: Connecting simulation outputs to PLCs and MES for automated adjustments requires robust validation and safety interlocks; decisions based solely on imperfect simulations can create unexpected outcomes on the shop floor.

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How to evaluate the framework in a pilot (step‑by‑step)​

1. Define measurable KPIs: target waste reduction, throughput increase, or downtime reduction tied to simulation outputs.
2. Select a bounded pilot scope: one filling line and a small parameter sweep (e.g., three bottle geometries and two viscosities).
3. Obtain or reproduce the Synopsys/SoftServe runbook: solver configuration, mesh details, compute SKU, and orchestration scripts.
4. Run side‑by‑side validation: classical high‑fidelity Fluent run vs. accelerated pipeline vs. surrogate output, and record solution differences and wall‑clock times.
5. Quantify costs and SLA needs: Determine the per‑cycle cost at required throughput and expected service latencies.
6. Establish governance and safety: Define human‑in‑loop vs. automated action thresholds, and set rollback conditions.

This approach protects engineering fidelity while clarifying the real operational value and the cost model.

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Broader implications: where this leads the industry​

If the architecture reliably delivers minutes‑scale high‑quality CFD results for realistic production models, it shifts simulation from a purely engineering backlog onto the factory floor as an operational control lever. That creates new opportunities:

• Continuous improvement loops where digital twins constantly learn from live telemetry and recommend immediate adjustments.
• Democratized simulation access: less specialized CAE expertise may be required for routine optimization if intuitive UIs and validated surrogates are available.
• Verticalized solutions across industries where fast fluid/thermal/mechanical decisions matter (food & beverage, pharmaceuticals, automotive, semiconductors).

However, the path from demonstration to scaling is nontrivial; it will require reproducible benchmarking, robust governance, and clear economic cases to displace existing practices.

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Final assessment — what WindowsForum readers should take away​

Synopsys’ framework is an important technical milestone because it demonstrates coordinated vendor collaboration — solver vendors, GPU/visualization platforms, cloud providers, and systems integrators — producing an end‑to‑end digital twin capable of driving faster engineering cycles. The Krones pilot and SoftServe documentation show the approach is viable and valuable in real industrial settings. At the same time, the differing runtime claims (30 minutes in the integrator case study vs. under 5 minutes in some press messaging) are a red flag for buyers and engineers who will rely on these numbers for procurement decisions. Until Synopsys and partners publish reproducible benchmarks that specify mesh size, solver settings, GPU types and counts, and fidelity targets, treat the five‑minute number as an optimistic target rather than a guaranteed SLA. Require auditable runbooks, validated fidelity comparisons, and cost models before committing to production rollouts.

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Quick checklist for IT/OT leaders considering a pilot​

• Ask for: reproducible benchmark runbook (mesh, models, solver flags, GPU SKU).
• Confirm: which Fluent modules were GPU‑accelerated in the demo and which were CPU‑bound.
• Evaluate: per‑cycle compute cost at target throughput and the expected accuracy delta vs. legacy runs.
• Secure: IP protection, encryption, identity, and CI/CD controls for models and datasets.
• Pilot scope: start small, validate fidelity, then scale.

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Synopsys’ demonstration is a credible, well‑architected step toward simulation‑driven, real‑time manufacturing optimization — one that leverages proven technologies from Ansys, NVIDIA, and Microsoft and practical systems integration from the partner ecosystem. The engineering gains shown in the Krones engagement are real and meaningful; the exact runtime claims should be validated against the published runbook and audited benchmarks before adopting them as procurement KPIs. Conclusion: the framework is an important advance in the digital twin story for manufacturing, but prudent engineering and procurement discipline remain essential to convert the demo’s promise into predictable operational advantage.

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Source: EEJournal Synopsys Demonstrates Framework for Optimizing Manufacturing Processes with Digital Twins at Microsoft Ignite

 

[ChatGPT]

Synopsys’ Microsoft Ignite demonstration — a multi-vendor framework that stitches GPU‑accelerated physics, NVIDIA Omniverse/OpenUSD visualization, Ansys Fluent GPU solvers, and Microsoft Azure cloud orchestration — promises to push high‑fidelity CFD from an offline engineering backlog into near‑real‑time factory decisioning, but the performance claims and operational trade‑offs require disciplined validation before buyers rely on them for production automation.

Background / Overview​

The offering unveiled at Microsoft Ignite centers on a pragmatic industrial use case: a full bottling/filling assembly‑line digital twin for Krones AG that models fluid behavior (bottle shape, viscosity, fill levels) and presents side‑by‑side scenario comparisons to operations and engineering teams. Synopsys positions the framework as an open, extensible blueprint that combines:

• GPU‑native Ansys Fluent as the CFD engine for fluid dynamics.
• Synopsys accelerated physics layers and orchestration to manage solver workloads.
• NVIDIA Omniverse/OpenUSD libraries for scene exchange and real‑time visualization.
• Microsoft Azure (including Ansys Access on Azure and AKS) for scalable cloud HPC and deployment.
• Systems integrators (SoftServe, CADFEM) for domain‑specific tuning and deployment.

The headline claim — reducing CFD turnaround from “3–4 hours” to “under 5 minutes” — is repeated in Synopsys’ press materials and in several syndications. However, an independent integrator case study (SoftServe + Krones) documents a fielded implementation that delivered simulations in ~30 minutes per cycle after a two‑month engagement, a materially different number that signals either differing test conditions or a blend of full‑fidelity and reduced‑fidelity workflows. That variance is central to procurement risk and will be referenced repeatedly below.

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What the framework actually integrates​

GPU‑native CFD: Ansys Fluent and the GPU solver landscape​

Ansys Fluent has steadily expanded GPU support and multi‑GPU capabilities in recent releases; the Fluent GPU solver is now a production feature used to accelerate many classes of CFD problems, and Ansys publishes guidance, hardware recommendations, and licensing models for GPU runs. Practical constraints still apply: not every turbulent multiphase model or discrete‑particle approach has identical GPU support, and memory/mesh sizing and solver choices materially affect time‑to‑solution. In short, GPU acceleration is real and can deliver large speedups — but the achievable wall‑clock time depends on physics fidelity, mesh density, and GPU topology. Key points about Fluent GPU readiness:

• Fluent supports NVIDIA and AMD server GPUs and multi‑GPU scaling for many fluids problems.
• GPU speedups have been measured at tens‑to‑orders‑of‑magnitude for some workloads, but case dependency is the norm.
• Licensing and hardware sizing (SM/CU counts, GPU memory) are part of the practical planning exercise.

OpenUSD / NVIDIA Omniverse: interoperability and visualization​

OpenUSD (Universal Scene Description) is being used as the interoperability layer so CAD/CAE, MES/PLC context, and the simulation outputs can be composed into a single digital‑twin scene. NVIDIA Omniverse supplies the visual runtime and libraries for real‑time rendering and collaborative viewing, enabling engineering and operations to view the same factory twin and compare scenarios visually and numerically. That visual continuity is a strong enabler of cross‑discipline collaboration.

Azure & Ansys Access on Azure: cloud HPC and orchestration​

Ansys Access on Microsoft Azure is a production offering enabling customers to run Ansys applications in their own Azure tenancy, with autoscaling clusters, preconfigured images, and an in‑browser management interface. This means enterprises can map simulation capacity to demand and leverage modern Azure GPU SKUs for burst workloads. The cloud layer handles provisioning, autoscaling, and integration with enterprise identity and governance. However, cloud economics must be modeled carefully — GPUs that deliver sub‑5‑minute runs are not free.

Systems integrators and domain tuning​

SoftServe and CADFEM are credited as critical integrators who tuned solver settings, adapted workflows, built the scenario engines, and created UI/dashboards. Their role is nontrivial: producing a near‑real‑time user experience requires not just hardware and solver licensing, but rigorous solver tuning, reduced‑order modeling choices (when used), orchestration code, and UX for non‑CAE operators. SoftServe’s published case study documents a two‑month engagement that produced ~30‑minute simulation cycles in a fielded application.

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The performance question: “Under 5 minutes” vs. documented 30 minutes​

Two results dominate the public record:

• Synopsys and multiple press wires carry the acceleration headline: 3–4 hours down to under 5 minutes. That phrasing is prominent in the Synopsys press release distributed via PR Newswire.
• SoftServe’s Krones case study — which documents an actual, multi‑partner production engagement — reports simulation cycles in about 30 minutes after integration and solver tuning. This case study includes stack details and a practical delivery timeline.

These two statements are both public and both credible in their own contexts. They can be reconciled in multiple ways — by differences in mesh resolution, physics fidelity, GPU hardware used, whether surrogate or AI‑assisted models were used, or whether the five‑minute number reflects a lab benchmark on a reduced test case or a surrogate hybrid rather than the full production model. The variance is material for procurement: a 30‑minute cycle supports faster engineering iteration but is not a turn‑key substitute for true real‑time control where decisions must be made every few minutes. Independent community analysis and forum posts have flagged the discrepancy and recommend treating the sub‑5‑minute figure as an optimistic projection until an auditable runbook is published.

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Why the numbers vary — a technical primer​

• Mesh and physics fidelity scale non‑linearly. Higher cell counts and multiphase/turbulence physics dramatically increase compute time.
• Solver feature support matters. Some advanced physics or coupling modes may not yet be fully GPU‑native; hybrid CPU/GPU workflows are common.
• GPU topology and memory. Achieving minute‑scale results typically requires many high‑memory server GPUs (A100/H100/B100 or AMD MI300 family) and fast NVMe backed storage.
• Algorithmic choices and surrogates. Reduced‑order models, AI surrogates, or precomputed response surfaces can give minute‑scale responses but require validation vs. full‑fidelity runs.
• Orchestration overhead. Container spin‑up, data staging, meshing, and postprocessing add real‑world latency that lab benchmarks sometimes exclude.

Because of these dependencies, reproducible benchmarking — with explicit mesh counts, solver flags, GPU SKU and counts, and a fidelity error budget — is the only way to convert vendor marketing claims into procurement KPIs.

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Practical benefits (what’s genuinely valuable)​

• Faster engineering decision cycles. Even a 30‑minute simulation versus a 3‑hour run enables far more iterations and quicker root‑cause analysis for process upsets.
• Better resource and waste control. Digitally tested fill settings, valve timing, and machine parameters can reduce product waste and downstream rework.
• Cross‑discipline collaboration. Shared digital twins that visualize CFD outputs in the factory context improve communication between engineering, R&D, and operations teams.
• Scalable deployment. An Azure‑backed model with Ansys Access allows enterprises to scale compute on demand and to integrate simulation into production pipelines without large upfront HPC purchases.

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Risks, caveats, and what procurement teams must demand​

• Treat headline performance numbers as promotional until audited. Require an auditable benchmark that reproduces the Krones scenario (exact mesh, solver flags, GPU types/number). The public record shows a notable discrepancy between press claims and integrator case results.
• Validate fidelity vs. speed tradeoffs. If surrogate models or simplified physics are used to accelerate runtime, obtain a fidelity report showing numerical error bounds relative to full‑fidelity runs or physical experiments.
• Quantify TCO and run‑rate economics. Minute‑scale runs at production velocity can be expensive in cloud GPU hours; model n simulations per shift and compute cost per cycle on target Azure GPU SKUs.
• Security and IP control. Running designs and factory models in the cloud demands encryption at rest/in transit, strict identity controls (Entra/AAD), secure CI/CD for models, and contractual IP protections.
• Operational safety and governance. Never allow automatic actuation from unsupervised simulation results without human‑in‑the‑loop thresholds, rollback logic, and PLC/MES safety interlocks.
• Integration artifacts. Ask for deployment automation (AKS manifests, container images, autoscaling rules) and a reproducible runbook for on‑prem or hybrid deployment.

The WindowsForum investigative summary and integrator materials echo these recommendations and point out that the Synopsys/SoftServe/Krones stack is credible — but that buyers must insist on reproducible runbooks and third‑party audits before making production commitments.

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How to evaluate the framework in a pilot — a practical checklist​

• Define KPIs (e.g., scrap reduction %, throughput delta %, simulation cycle time target).
• Choose a bounded pilot: one filling line, a 3‑parameter sweep (three bottle geometries, two viscosities).
• Request a reproducible runbook: solver version, mesh counts, turbulence/multiphase models, GPU SKUs, and orchestration scripts.
• Run side‑by‑side validation:
• Full‑fidelity Fluent on documented HPC (CPU baseline or multi‑GPU) vs.
• The accelerated Synopsys pipeline vs.
• Any surrogate/ML pipeline used for inference.
• Measure wall‑clock times, cost per cycle on Azure SKU, and error bounds vs. physical tests.
• Security and governance review: review encryption, identity, audit logs, and IP clauses.
• Operational integration: simulate the advisory loop and run fail‑safe tests for PLC/MES actuation.
• Contractual SLAs: include documented performance benchmarks and remediation clauses tied to fidelity and latency.

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Economic considerations: modeling cloud cost​

• Identify required simulation throughput (sim cycles per shift).
• Determine GPU SKU and count needed for targeted fidelity (ask vendors for audited mapping).
• Multiply hours/day by cloud GPU hourly price (include storage and egress).
• Factor in orchestration overheads, instance spin/down savings, and potential spot/commitment discounts.
• Estimate software license uplift (Fluent licensing on multi‑SM/CU models vs. CFD HPC Ultimate).
• Compare against on‑prem HPC TCO over 3–5 years.

Only when cost per actionable insight is favorable should a customer move from pilot to production. The Ansys Access on Azure model simplifies pricing and deployment, but it does not eliminate the need for careful capacity and budget modeling.

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Vertical opportunities and longer‑term implications​

If reproducible, minute‑scale high‑fidelity CFD becomes accessible, the implications are broad:

• Food & beverage and pharmaceuticals: real‑time fill control, contamination avoidance, and optimized sanitation cycles.
• Automotive and aerospace: faster design iterations for external aerodynamics or ventilation systems during production adjustments.
• Semiconductors and thermal systems: near‑real‑time thermal/flow optimizations for process equipment.
• Healthcare simulations: the press materials even mention exploratory uses like assisting surgeons; those applications will require a much higher bar for verification and regulatory controls.

But the path from a convincing demonstration to reliable, widely adopted operational controls is long: reproducible benchmark suites, audited fidelity reports, integration standards, and safety governance must be established.

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Final assessment — measured optimism with required diligence​

The Synopsys‑led framework demonstrated at Microsoft Ignite represents a credible and powerful architectural direction: pairing GPU‑native CFD, cloud orchestration, OpenUSD-based interoperability, and collaborative visualization can materially shrink simulation‑to‑decision latencies and democratize access to simulation insights. The Krones pilot, as documented by integrator materials, shows the approach is fieldable and valuable. However, buyers and IT/OT teams should treat the most aggressive performance claims — specifically the “under 5 minutes” headline — as aspirational until vendors publish auditable runbooks and third‑party validated benchmarks that specify mesh size, solver options, GPU types/counts, cost per cycle, and fidelity tolerances. In practice, the stronger, verifiable result in public documentation today is closer to ~30 minutes per production‑grade cycle for the Krones deployment. Procurement and engineering teams should require that any procurement contract include reproducible performance tests, fidelity acceptance tests, and operational cost estimates before moving to production automation or closed‑loop control.

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Action checklist for WindowsForum readers (IT architects, engineering leads, and procurement)​

• Demand an auditable, vendor‑signed runbook reproducing the claimed numbers.
• Specify fidelity acceptance thresholds and validation datasets for any surrogate models.
• Require security, IP, and governance artifacts as part of the deployment contract.
• Model cloud TCO for realistic throughput and include SLA/penalties for missed performance targets.
• Start with a bounded pilot (single line) and require side‑by‑side validation vs. legacy HPC runs.
• Insist on human‑in‑the‑loop safety gates for any automated actuation driven by simulation outputs.

Synopsys’ framework demonstrates the industry’s forward motion: vendors are integrating mature solver technology, GPU acceleration, real‑time visualization, and cloud orchestration into cohesive digital‑twin solutions. That momentum is real and valuable — but the difference between promising demo and production control system rests on reproducible benchmarks, fidelity validation, and disciplined operationalization. The most productive path forward for manufacturers is careful pilots with auditable metrics, not blind faith in a headline runtime figure. Conclusion: the technology direction is correct and the Krones pilot is an important proof point; treat sub‑5‑minute marketing claims with caution until empirical, reproducible evidence is supplied, and structure any rollout with explicit fidelity, safety, and cost guardrails.

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Source: Lelezard Synopsys Demonstrates Framework for Optimizing Manufacturing Processes with Digital Twins at Microsoft Ignite

 

[ChatGPT]

Synopsys and a coalition of partners have rolled out a GPU‑accelerated, cloud‑native digital‑twin framework that promises to transform slow, high‑fidelity CFD (computational fluid dynamics) workflows into near‑real‑time decision tools for factory floors — a system demonstrated with bottling‑line specialist Krones and integrating NVIDIA Omniverse/OpenUSD, Ansys Fluent, and Microsoft Azure.

Background / Overview​

Manufacturing has long wrestled with a core tension: high‑accuracy physics simulations are invaluable for optimizing processes (from fill valves to thermal management), but they typically take hours or days — too slow for operational decisioning. The new framework Synopsys unveiled at Microsoft Ignite stitches together GPU‑native solvers, cloud HPC orchestration, and a shared scene/visualization layer so operators and engineers can run scenario sweeps on full assembly‑line digital twins and get actionable insights far faster than before. This is not a single‑vendor product but an integrated reference architecture that brings together:

• Ansys Fluent as the CFD engine, delivered via Ansys Access on Microsoft Azure.
• NVIDIA Omniverse and CUDA‑X libraries for real‑time visualization, OpenUSD interoperability, and GPU acceleration.
• Microsoft Azure for scalable GPU instances, orchestration (AKS), and enterprise governance.
• Systems integrators and channel partners — notably SoftServe and CADFEM — to tune solver settings, build orchestration pipelines, and deliver operator‑facing dashboards.

The demonstration use case was a bottling/filling assembly line from Krones, where parameters such as bottle geometry, liquid viscosity, and fill level were swept across scenarios in the digital twin to identify settings that reduce waste and improve throughput. Visualization and analytics are presented within a factory‑scale virtual scene so multidisciplinary teams can collaborate on the same data.

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Technical architecture — how the pieces fit​

Core solver layer: Ansys Fluent (GPU‑native) and Synopsys accelerated physics​

Ansys Fluent provides the high‑fidelity CFD backbone. Recent Fluent releases and the Ansys‑Synopsys integration roadmap emphasize GPU acceleration and cloud‑optimized deployment, enabling many CFD kernels to run orders of magnitude faster on modern server GPUs. Synopsys positions an accelerated physics orchestration layer that schedules and optimizes solver runs across cloud GPU fleets.

Visualization and interoperability: NVIDIA Omniverse + OpenUSD​

OpenUSD acts as the canonical scene/exchange format so CAD, CAE, MES, and telemetry feeds can be composed into a single factory twin. NVIDIA Omniverse supplies the runtime and libraries for photoreal visualization, collaborative review, and streaming of simulation results into operator UIs. This allows engineering and operations to literally “see” how fluid flows behave inside a filling line while simultaneously inspecting numeric outputs.

Cloud platform: Microsoft Azure + Ansys Access on Azure​

Azure provides the scalable GPU infrastructure, autoscaling clusters, and enterprise controls required for production deployments. Ansys Access on Microsoft Azure delivers pre‑configured Ansys environments and an HPC‑friendly management layer, simplifying deployment of Fluent and related tooling in a customer’s own Azure tenancy. That integration reduces the friction of moving HPC‑grade CFD into the cloud.

Systems integration and solver tuning: CADFEM and SoftServe​

Delivering minute‑scale simulation responses requires more than raw hardware and solver licensing: it needs careful solver flag tuning, mesh strategies, possible surrogate/reduced‑order models, and orchestration that minimizes data staging and container spin‑up. CADFEM and SoftServe are cited as the partners that tuned Fluent solver settings for the Krones demo and built the web interfaces and deployment automation.

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What Synopsys and partners are claiming — and what’s verified​

• Headline claim (Synopsys PR): reduction of CFD runtimes from 3–4 hours down to under 5 minutes for the Krones filling‑line digital twin.
• Integrator/case‑study claim (SoftServe / Krones materials): a fielded implementation that reduced cycle times dramatically — reporting about 30 minutes per simulation cycle after a focused two‑month integration.

These two public figures both appear in the record and must be read together: the Synopsys press messaging emphasizes the potential for sub‑5‑minute runs under optimized conditions, whereas the SoftServe/Krones case study documents a reproducible, fielded outcome of roughly 30 minutes. Independent technical summaries and community analysis echo this nuance and recommend treating the sub‑5‑minute metric as an aspirational target until auditable runbooks and benchmarks are published.

Why the numbers can diverge​

The achievable wall‑clock time for CFD is highly case dependent. Key technical levers include:

• Mesh density and cell counts: runtime scales non‑linearly with mesh size; a production‑grade multiphase mesh is far more expensive than a reduced mesh used for benchmarking.
• Physics fidelity: multiphase flows, free surface tracking, or detailed turbulence models can be GPU‑accelerated but sometimes require solver pathways that are slower or still rely on CPU components.
• Solver options and reduced‑order models: results can be accelerated with surrogates, ROMs, or ML‑assisted inference — which trade some fidelity for speed and must be validated.
• GPU topology and storage I/O: sub‑5‑minute runs usually require numerous high‑memory server GPUs (H100/A100/MI300 family) plus NVMe throughput; cloud economics for that capacity matter.

In short: both the five‑minute claim and the 30‑minute documented outcome are credible in context — but they describe different points on a performance vs. fidelity curve. Buyers must insist on explicit runbooks and fidelity error budgets before assuming one metric will generalize to their plants.

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Operational impact — the benefits that are believable​

When deployed correctly, this architecture can deliver tangible manufacturing gains:

• Faster engineering iterations: reducing a run from hours to tens of minutes (or minutes in extreme lab cases) multiplies the number of what‑if experiments possible per shift, accelerating root‑cause analysis and change validation.
• Lower waste and improved yield: validated digital twin scenarios that optimize valve timing, fill settings, or conveyor speed can materially reduce overfill/underfill scrap and downstream rework.
• Stronger cross‑team collaboration: OpenUSD/Omniverse scenes put CFD outputs in the factory context, making simulation results actionable for operations as well as engineering.
• Scalable deployment model: cloud orchestration (AKS + Ansys Access on Azure) allows burstable HPC for seasonal or campaign needs without heavy on‑prem capital investment.

These gains are real and well‑supported by the public materials; the question for most manufacturers is not if the architecture can help, but how much and at what ongoing cost and operating complexity.

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Risks, practical trade‑offs, and governance​

The technology is promising, but operationalizing it exposes several serious considerations:

• Fidelity vs. speed trade‑offs — Faster answers often come from simplifications or surrogate models. Those must be paired with explicit error budgets and validation plans to avoid unsafe or wasteful adjustments on live lines.
• Cloud economics and TCO — Minute‑scale performance at production scale requires sustained GPU hours and premium VM SKUs; cost modeling must include compute, storage, data egress, and licensing (Fluent, Synopsys layers).
• IP, data protection, and compliance — CAD/CAE data and process models are intellectual property. Running them in the cloud requires careful encryption, identity governance (Entra/AAD), contractual IP protections, and supply‑chain assurances.
• Operational safety and automation governance — Simulation outputs should not be allowed to actuate PLCs or MES without human‑in‑the‑loop safeguards, rollback paths, and safety interlocks; otherwise, inaccurate predictions could cause production incidents.
• Reproducibility and vendor claims — Press benchmarks (sub‑5 minutes) may reflect lab conditions or reduced test cases. Procurement should require auditable benchmarks that reproduce the target scenario on specified cloud SKUs and solver flags.

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How manufacturers should evaluate and pilot this kind of framework​

To convert vendor hype into production value, IT/OT teams should pursue a disciplined pilot. A recommended, practical checklist:

• Define measurable KPIs up front (e.g., % scrap reduction, throughput delta, simulation cycle time target).
• Choose a bounded pilot scope: a single filling line or machine with a limited parameter sweep (3 bottle geometries, 2 viscosities).
• Obtain an auditable runbook from the vendor/partner showing solver version, mesh counts, turbulence/multiphase models, GPU SKU and count, and orchestration scripts. Demand reproducible steps to run the benchmark.
• Run side‑by‑side validation: full‑fidelity Fluent runs on baseline HPC vs. the accelerated pipeline vs. any surrogate/ML inference used. Record wall‑clock times and quantify output differences against physical tests.
• Model cloud costs: estimate required simulations per shift, map to Azure GPU SKUs, and calculate per‑cycle cost (compute + storage + networking + software). Include spot/commitment discounts in scenarios.
• Security and governance review: verify encryption at rest/in transit, AAD/Entra identity integration, audit logs, and contractual IP protections. Plan for incident playbooks and revocation.
• Operational integration test: run advisory workflows and test human‑in‑the‑loop thresholds; do not authorize autonomous actuation until validated with safety interlocks.
• Contractual SLAs: require measured performance benchmarks in the contract and remediation clauses tied to fidelity and latency shortfalls.

These steps reflect best practices drawn from integrator case studies and independent analysis; they turn vendor claims into verifiable procurement KPIs.

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Economics: what to budget and expect​

The economics break down into three broad areas:

• One‑time engineering integration: systems integrator fees for solver tuning, mesh strategies, UI development, and automation (SoftServe/CADFEM type engagements). Case studies report focused two‑month efforts to reach initial results.
• Ongoing cloud compute: the dominant recurring cost. High‑throughput, low‑latency runs require premium GPU instances; pricing will vary by region and commitment level. Model the expected number of runs per shift and total hours.
• Software licensing and support: Ansys/Fluent licensing, Synopsys accelerated physics layers (where applicable), and any orchestration or visualization subscriptions. Use Ansys Access on Azure to simplify license and deployment management where suitable.

Manufacturers should perform a compare/contrast against an equivalent on‑prem HPC TCO over a 3–5 year horizon. Cloud often wins for agility and peak load but can be more expensive for sustained, continuous heavy usage unless committed discounts are negotiated.

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Industry implications and ecosystem momentum​

This demonstration brings together several industry trends into a coherent industrial capability:

• CAE solvers are becoming GPU‑first, with vendors accelerating paths to leverage modern accelerators — NVIDIA’s Blackwell announcements and Ansys’ Omniverse integrations are explicit signals of that movement.
• OpenUSD/Omniverse is maturing as the interoperability fabric, lowering integration friction between design, simulation, and operations.
• Hyperscalers are cementing the cloud‑HPC model through marketplace offerings (Ansys Access on Azure) and dedicated HPC SKUs, making simulation‑driven digital twins more accessible without heavy on‑prem capital outlays.

If reproducible, minute‑scale high‑fidelity CFD becomes a stable, auditable capability, it will shift simulation from an engineering backlog to a continuous operational lever — democratizing simulation insights for frontline operators and enabling new closed‑loop optimization patterns across food & beverage, pharmaceuticals, automotive, and semiconductor process equipment. Nevertheless, scaling that promise requires reproducible benchmarks, agreed fidelity metrics, and robust governance.

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Final assessment and practical verdict​

Synopsys’ framework and the Krones demonstration represent an important and credible technical milestone: a practical, multi‑vendor blueprint that ties GPU acceleration, Omniverse visualization, cloud orchestration, and systems‑integrator expertise into a factory‑scale digital twin. The public record contains both impressive marketing claims (sub‑5‑minute runs) and verifiable integrator outcomes (≈30 minutes per cycle) — both are useful, but they mean different things.

• Treat the sub‑5‑minute headline as a potential: an engineering target achievable under optimized, likely reduced‑fidelity or surrogate‑assisted conditions.
• Treat the 30‑minute documented case as the more conservative, fielded baseline for procurement planning until vendors publish auditable runbooks that reproduce the faster numbers under production‑grade fidelity.

Procurement and engineering teams should proceed with disciplined pilots that demand reproducible benchmarks, explicit fidelity/error budgets, and economic modeling of cloud costs. When those guardrails are in place, the Synopsys + NVIDIA + Microsoft + Ansys ecosystem delivers a practical pathway to bring sim‑driven decisioning out of the lab and onto the shop floor — with meaningful potential to reduce waste, increase throughput, and shorten engineering cycles.

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Conclusion
The Synopsys‑led digital‑twin framework is a significant step in making high‑fidelity simulation operationally useful for manufacturing. The technology stack — GPU‑first solvers, OpenUSD/Omniverse interoperability, and Azure‑based HPC orchestration — is mature enough to deliver measurable value, but real‑world deployments reveal a spectrum of outcomes. Responsible adoption requires pilots with auditable runbooks, validated fidelity trade‑offs, cost modeling, and robust safety and IP governance. Done right, digital twins powered by accelerated CFD will change how production decisions are made; done impatiently, they risk substituting speed for accuracy at a cost to operations and safety.

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Source: datacentrenews.uk Synopsys boosts manufacturing with real-time digital twin simulation

 

[ChatGPT]

When Microsoft first opened the Windows Insider Program to the public, it promised clarity: a predictable preview pipeline that would let IT pros, OEMs, and enthusiasts see the next Windows release coming down the road and prepare for it. Over a decade later that bright line between preview and production has blurred — and recent structural changes, leadership turnover, and the arrival of server‑side feature gating have left many enterprise customers and long‑time Insiders bewildered about what the program is for and how to rely on it. The result is a program that still delivers value, but often fails exactly at the point enterprises need it most: predictability and transparency.

Background​

How the Insider program began and why it mattered​

The Windows Insider Program launched alongside the Windows 10 preview process in late 2014 as a community‑driven testing and feedback loop. Its original promise was simple and powerful: give engineers early, broad exposure to how customers use Windows, and give customers a deterministic preview window so they could validate apps, drivers, and deployment plans ahead of a final release. That arrangement worked well for years — Insiders could see most publicly documented features appear in preview builds months before general availability, and enterprises could use Release Preview and Beta rings to schedule deployments with reasonable certainty.

The original channel model — rings that meant something​

For roughly the first half of the program’s life, the channel model was intuitive: Fast (later Dev) for weekly, highly experimental builds; Slow (later Beta) for more validated monthly builds; and Release Preview for near‑final validation. That split gave testers and IT pros an actionable way to pick risk levels and plan. Over time Microsoft added a Canary channel for the absolute bleeding edge, but the basic idea remained useful: pick a channel that matched the amount of risk and lead time you could tolerate.

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Where things started to diverge​

Windows 11, compressed previews, and the end of long lead times​

The first structural crack appeared around the Windows 11 transition. The preview window between internal builds and public release for Windows 11 was significantly shorter than previous Windows feature‑update cycles. Enterprises that had counted on six months of Beta testing suddenly had only a few weeks to validate changes — too little time for many corporate deployment processes. That compression made Beta builds feel less useful for meaningful enterprise validation.

The Dev channel becomes experimental and untethered​

A second, more consequential change came in 2022 when Microsoft publicly redefined the Dev channel as an experimental space — a platform for A/B tests, prototypes, and features not necessarily destined for a particular release. The company made clear that some Dev‑channel work would never ship and that feature availability in a particular build could be intentionally toggled off or varied across devices. That move transformed Dev from a pre‑release preview of future releases into a research playground — useful for gathering telemetry and testing concepts, but far less reliable as a forecasting tool.

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The Controlled Feature Rollout problem​

What Controlled Feature Rollout (CFR) is — and why it matters​

Microsoft has been explicit about a new operating model for Windows called “continuous innovation.” Rather than bundling all changes into annual feature updates alone, Microsoft increasingly ships smaller experiences and features through monthly servicing channels and server‑enabled feature flags. That is done using Controlled Feature Rollout (CFR) technology: server‑side gating that lets Microsoft turn features on for subsets of devices, validate behavior, and expand availability in phases. This is the same mechanism Microsoft uses internally for Insider flights and in Microsoft Edge. CFR’s stated goal — reducing disruption by exposing features gradually so issues surface on a fraction of devices before broad rollout — is reasonable. But it has three side effects that undermine the Insider program’s original function:

• It decouples "what you see in a given build" from "what will ship in the public release." A documented item in release notes may be present on some Insider PCs and absent from others, or present in the optional preview package but not enabled for your device.
• It makes general availability a moving target. Devices with identical updates may surface different features at different times, so the simple statement “install the public update and you’ll get X” no longer always holds.
• It effectively turns production customers into an additional, opaque test cohort unless Microsoft provides clearer gating signals and deterministic controls for administrators.

The monthly preview cadence and rollout phases​

Microsoft documents that optional, non‑security preview releases — the monthly packages administrators use to preview next month’s Patch Tuesday content — can use phased rollouts and CFR. These monthly preview releases occur in the fourth week of each month, and Microsoft calls out “gradual rollout” and “normal rollout” phases in its documentation, meaning that visibility into features may vary by device even within the same channel and build. For administrators that rely on those preview packages for validation, the result is an incomplete preview: what you test may not be what your users see weeks later.

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Real‑world consequences for enterprises and support teams​

Predictability lost — planning becomes guesswork​

A fundamental requirement for enterprise IT is predictability. Organizations plan feature deployments, training, and support scripts against a known set of behavior. When features are gated server‑side and phased differently across identically patched machines, validating a rollout becomes a moving target: a Windows build number no longer guarantees parity of features. That complicates troubleshooting (is a problem a device‑specific feature flag, a driver regression, or a patch issue?, documentation updates, and support staffing. Community and vendor guidance are less reliable when two seemingly identical machines behave differently.

The feedback loop becomes noisier​

Insiders submit feedback through Feedback Hub and expect it to travel to engineering in a way that helps shape shipping builds. When Dev becomes a lab for experiments and CFR can flip features independently of flighted builds, the link between feedback and outcomes weakens. Features seen and commented on in Insider builds may be altered or abandoned entirely, and there is less clarity about which feedback actually influenced what shipped. For organizations that pay third‑party vendors to train and test against preview builds, that has real cost implications.

Regressions still escape into production​

Despite more continuous validation, Microsoft’s servicing cadence has not eliminated serious regressions on shipped, production builds. Recent months have included cumulative updates and emergency, out‑of‑band patches to address issues that affected recovery environments and other critical capabilities. Those incidents illustrate that a broader, more continuous pipeline does not guarantee earlier detection of all high‑impact bugs — and when paired with a confusing preview model, they erode confidence.

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Leadership churn and the human factor​

Key departures and why they matter​

One stabilizing element of the old Insider program was a visible, human‑facing team: named program leads, consistent release notes signed by program managers, and public blog posts explaining decisions. Recently, several long‑standing members of the Insider team — people who were public faces for flighting and feedback — left or transitioned to other roles. That abrupt turnover, combined with quieter public communication, has made the program’s roadmap and rationale feel more opaque to the community. Community reporting and forum discussions reflected surprise and concern when multiple team members announced transitions within a short window. Those departures matter because they weaken a communication channel that Insiders rely on for context beyond terse release notes.

Why a human touch matters in a gated world​

Server‑side feature flags and automated enablement are powerful technical tools, but they require a human narrative to make sense to customers. Engineers and administrators need to understand not just that “a feature is rolling out,” but which policies control it, what enterprise opt‑out options exist, and how Microsoft plans to communicate progress and rollback. When human program leadership is less visible or inconsistent, customers default to distrust and community rumor to fill the gap — which is what we’ve seen.

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Strengths of the new model — and why Microsoft moved this way​

It’s important to acknowledge why Microsoft is pushing toward CFR and continuous innovation: the classic annual cycle was too slow for rapid feature work (especially with the AI and cloud integrations Microsoft is prioritizing), and gated rollouts can prevent major incidents by exposing new experiences to a smaller set of devices first. The model enables:

• Faster delivery of small, high‑value features (UX fixes, Copilot enhancements, driver optimizations).
• The ability to A/B test experiences at scale for quality signals and usage patterns.
• Finer enterprise controls (features can be shipped off by default and enabled by policy for organizations that want them).

Those are real benefits when the program works as intended. For consumers who simply want incremental improvements and are comfortable with server toggles, the experience can be smoother than in the old cycle where major feature‑update leaps sometimes introduced abrupt regressions.

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The tradeoffs and risks enterprises must weigh​

Fragmentation and reproducibility problems​

• Troubleshooting complexity: When identical builds can behave differently due to device gating, replicating bugs becomes harder. Support scripts and KBs assume parity; CFR breaks that assumption.
• Testing burden: Enterprises must expand validation matrices to include entitlement states and feature‑flag combinations rather than just build numbers.
• Vendor and ISV friction: Third‑party software vendors may not be able to certify or support their apps across heterogeneously enabled features.

Governance and privacy questions​

Continuous rollouts that blend local device behavior with cloud‑enabled features also raise questions about telemetry, data flow, and compliance. Enterprises will want explicit documentation on what data flows to the cloud when features are enabled, how to audit the enablement, and how to opt out in managed environments. Without that clarity, IT teams face a risk of unanticipated data movement.

The human resource and morale cost​

Requiring support and training teams to chase a moving target imposes measurable cost: retraining, updated troubleshooting steps, and confusion across help desks. That friction is non‑trivial, and it is precisely what made the original Insider program valuable — a place to converge on what would ship so those teams could prepare.

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Practical guidance: what enterprises and Insiders should do now​

For enterprise IT​

1. Treat CFR and preview packages as progression, not guarantees. Validate with policies and assume feature gating will vary.
2. Use pilot rings internally (pilot > broad pilot > enterprise rollout) and preserve rollback/recovery tooling.
3. Lock down critical devices using Microsoft’s policy controls for “commercial control for continuous innovation” and document any opt‑in/opt‑out decisions.
4. Coordinate with OEMs and major ISVs to validate driver and anti‑cheat compatibility for important workloads before wide enablement.

For Windows Insiders and hobbyists​

• Move the most experimental work to spare machines or VMs and avoid using pre‑release builds on mission‑critical devices.
• Use the Feedback Hub actively and include precise telemetry and repro steps; be aware that Dev flights are experimental and may not lead directly to shipped features.

For system integrators and training vendors​

• Don’t assume Beta or Release Preview parity means universal feature availability; confirm feature flags and delivery phases before scheduling training or publishing help content.

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What Microsoft should (and could) do to restore clarity​

• Publish deterministic gating maps: make it clear which features are controlled by CFR, which policy toggles control them, and an expected rollout timeline. That would let enterprises map tests to feature entitlements rather than guess at build parity.
• Reintroduce stronger, human‑facing communications: regular blog posts that explain why features are gated, who controls enablement, and which feedback informed changes.
• Recommit a clear role for Beta/Release Preview as enterprise validation lanes with documented feature parity for a predictable window prior to GA. If certain features will only be gateway‑enabled server‑side, label them explicitly and state whether they will be opt‑in or opt‑out for organizations.

Those are practical steps that would keep the benefits of CFR while restoring the predictability that enterprises and the broader Insider community need. Community trust is not a permanent resource; it must be earned with predictable signals and clear dialog.

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Conclusion​

The Windows Insider Program is no longer merely a place to see the next Windows release early; it’s become one component of a much broader, continuous delivery pipeline where server‑side gating and rapid iteration are the default. That model brings clear technical advantages — quicker feature delivery, safer large‑scale experiments, and the ability to ship small improvements frequently. However, it also imposes a heavier coordination tax on enterprises, third‑party vendors, and support organizations that still depend on deterministic previews to plan, test, and teach.
Microsoft can keep the technical benefits of Controlled Feature Rollout while restoring the program’s original value by improving transparency, reestablishing predictable enterprise validation lanes, and maintaining a visible, accountable human interface to the Insider community. Until then, the Insider program will remain a potent but confusing tool: powerful for experimentation, imperfect as a forecasting mechanism, and increasingly reliant on careful reading of release notes, entitlement policies, and server‑side gating behavior to make sense of what actually ships on any given PC.

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Source: ZDNET The Windows Insider Program is a confusing mess