pSeven Enterprise
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Web-native low-code platform for automation of engineering processes at scale:
- Automate simulation workflows and find optimal designs right in your browser.
- Collaborate easily with teammates within your organization and partners.
- Scale even resource-intensive trade-off simulation studies.
- Create and share workflow-powered web apps.
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All you need to automate engineering processes at scale
- Multi-user environment - Workflow co-authoring and sharing in the browser.
- Low-code automation - Web-native architecture and documented open API.
- Workflows as web apps - Workflow-powered web apps in few clicks.
- Workflow engine - Visual and executable representation of processes.
- Machine learning - Build and deploy fast and reliable ML models.
- Resource management - Execution in distributed heterogeneous environment.
- Optimization - Explore, optimize and calibrate your models.
Capture and transfer knowledge
- Formalize complex multidisciplinary engineering processes at scale.
- Share, reuse and maintain knowledge with low-code approach.
Automate repetitive tasks
- Accelerate product development cycle through productivity gain.
- Reduce time-to-market with standardization and democratization.
Enable Digital Twin strategy
- Bridge the gap between engineering and operations.
- Make better and faster operational decisions.
Customer success stories
- Subsea field design automation (project ARENA)
- 3x time reduction of the project design justification
- Enabling digital oilfield applications (Digital Field Solver)
- Many oil & gas customers worldwide
Explore industrial use cases
Streamlining NVH analysis of loudspeaker performance
Find out how predictive modelling techniques and engineering solutions are shaping the future of automotive acoustics. Explore a web-based application that automates the evaluation of loudspeaker positioning within vehicle interiors. This application enables quick design iterations, reduces manual effort and assists non-experts in making informed audio design decisions.
Fully automated car suspension simulation process in pSeven Enterprise
pSeven Enterprise transforms complex simulation business processes into automated multidisciplinary workflows. This transformation formalizes processes, eliminates data transfer errors and accelerates simulation. Furthermore, automated simulation workflows enable parametric, multidisciplinary design optimization.
Collaborative work on aircraft wing design in pSeven Enterprise
This article describes how the features presented above can be applied to real-world examples of wing design.
Capture complex engineering processes
with low-code workflow automation
pSeven Enterprise allows to capture even the most complex engineering processes, define its logic and collect, analyze and reuse engineering data thanks to powerful workflow engine and low-code approach.
Collaborative engineering
and workflow co-authoring in real-time
pSeven Enterprise is a multi-user platform with simultaneous access and co-authoring of the workflows in the browser. Shared workspaces are designed for departments or teams to share and edit workflows, results and files depending on the user roles - all in your browser in real-time.
Share simulation methodologies
as workflow-powered web apps
Workflows in pSeven Enterprise can be easily published and shared in a form of web apps with either automatically generated or custom developed GUI. These workflow-powered web apps serve as easy-to-use engineering calculators that hide unnecessary complexity and democratize the use of best practices for inexperienced users.
Some examples of possible integrations
Boost PLM / SPDM systems
with enhanced simulation workflow execution
As a collaborative engineering platform, pSeven Enterprise is designed for automating complex simulation workflows and executing them server-side. All workflows are executed server-side in various predefined runtime environments with scalable resource management. All of that makes pSeven Enterprise a reliable backbone for PLM / SPDM systems in terms of handling simulation workflows.
Explore design alternatives
and find optimal designs
pSeven Enterprise is equipped with a set of tools to efficiently explore model behavior using a wide range of Design of Experiments (DoE) techniques, solve multi-objective optimization problems and perform Uncertainty Quantification (UQ) studies with both fast-to-evaluate analytical models and computationally-expensive simulations.
By creating predictive models*, existing test, experimental, and/or simulation data can be used to predict response values for new designs, accelerate complex simulations by many orders of magnitude and capture essential knowledge.
* - Also often called machine learning models, response surface models (RSM), reduced order models (ROM), approximation models, surrogate models, metamodels etc.
Design Exploration
Explore various design alternatives and easily find optimal solutions with Design of Experiments and Design Optimization strategies.
Uncertainty Quantification
Improve quality and reliability of designed products and manage potential risks at early design, manufacturing and operating stages.
Predictive Modeling
Predict response values for new designs, accelerate complex simulations and capture knowledge by creating fast models from data.
Automatic choosing of algorithms in all tools thanks to SmartSelection technology
What's new in pSeven Enterprise?
Block publishing process
Development of custom blocks is a powerful and popular way to extend and tailor pSeven Enterprise to address and streamline everyday usage efficiency for each customer scenario. With recent updates, we significantly simplified the process of block publishing. Now the author of the block does not need any assistance from the deployment administrator to publish the block to Common library to make it assessable by all users on the deployment – only proper permissions. And the process of publication is now reduced to simple drag-and-drop of the block from personal library to common one in the user interface, yet preserving all advantages of block versioning, tagging and grouping.
User setup
Since the recent version of pSeven Enterprise, deployment administrators can tune the user account even before the first login happens – user is initialized upon creation. This improvement saves time and effort and ensures no problems with an unexpected increase in license count, wrong initial permissions, etc.
Moreover, user account creation and modification are now fully covered by public API – which enables even more tight integration of pSeven Enterprise into existing ecosystems.
Workflow export \ import API
In production deployments there is often a need to automatically backup some workflows to external storages, re-import them from such storages or even transfer the workflows from one deployment to another in an automated way. All of those are now possible with the recent API extension.
Go to page navigate_nextWhen executing a workflow in pSeven Enterprise, each block should ideally run immediately once its input data becomes available - meaning all prerequisite blocks linked to it have completed and produced their outputs. However, block startup often involves initialization delays due to resource allocation for containers, network latencies and other overhead.
pSeven Enterprise offers multiple block execution strategies, each designed to optimize workflow runtime and cluster resource consumption differently. Let's explore how these strategies work using a simple workflow with three sequential blocks.
1. All-at-once: All blocks start simultaneously with the workflow run. Most blocks become ready to process input data by the time it's available, which minimizes inter-block delays. Total workflow runtime then approaches the sum of individual block execution times. Conversely, this leads to excessive cluster load as all blocks reserve required resources upfront.
2. On-demand: Blocks start only when their input data becomes available. Due to initialization delays, blocks cannot immediately start processing input data during the workflow run, which can significantly increase inter-block delays and total workflow runtime. However, this approach minimizes the load by allocating resources only when needed and is recommended for overloaded clusters.
3. On-demand with predictive initialization: The new optimized scheduling strategy (now enabled by default) builds on the on-demand strategy. Using workflow run statistics, it predicts and compensates for block initialization delays. Given accurate timing estimates from previous runs, this strategy ensures each block is ready to execute precisely when its input data (outputs from all prerequisite blocks) becomes available. The updated run strategy maintains low computational overhead comparable to the original on-demand strategy, while achieving runtimes that nearly match the all-at-once strategy. Performance improvements are most significant in workflows with long chains of sequential blocks, while a little less noticeable in highly parallel workflows.
The Model builder block enables predictive modeling in pSeven Enterprise. Data collected from simulations and physical tests can be transformed into an executable model that quickly computes results for any new inputs, accelerating complex simulations by orders of magnitude. For example, such models can be used to run full-factorial experiments and select candidate optimal design parameters, which are then verified using more precise methods.
The process of creating a model is called training, while using a model to compute results for new inputs is called prediction. The overall approach is known as predictive modeling, also referred to as metamodeling, surrogate modeling, response surface modeling (RSM), reduced-order modeling (ROM), machine learning (ML), etc.
Creating a model with sufficiently accurate predictions (good predictive power) requires selecting the most suitable modeling technique for the specific problem and dataset, plus extensive parameter tuning - often through trial and error. In pSeven products, a special technique called SmartSelection automates this entire process without requiring additional configuration or expertise from the user. It automatically searches for an optimal technique and parameter settings to balance computational efficiency and model accuracy based on the training data characteristics.
The Model builder block primarily serves as a SmartSelection launcher within pSeven Enterprise:
- Provide a CSV file path or Table value to the Training sample input.
- Optionally pass an existing model to the Initial model input as a baseline.
- Control the process by specifying high-level settings for training and validation, known sample properties and additional model requirements - needing only general methodological knowledge, no deep expertise in mathematics, modeling algorithms or specialized domains.
- Save trained models in native format or export them to source formats, including C source code and MATLAB-compatible - models are not locked into pSeven Enterprise.
Model builder offers quick setup through direct access to all settings in the Block properties pane, requiring no separate UI configuration. The block also features seamless integration with the Design space exploration block, allowing generated DoE to pass directly from the Result designs output to the Training sample input. This integration enables automatic detection of inputs/outputs, names, and properties without requiring additional setup.
For more experienced users, Model builder provides access to the most granular training settings through pSeven Core options. This enables full control over technique selection and parameter configuration. Experts can use Model builder for conventional training workflows while maintaining complete control over the process.
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Download an eBook by CIMdata
The eBook highlights the challenges industrial companies of all sizes face in deploying simulation across domains and how pSeven Enterprise addresses those challenges.