Siemens Aktiengesellschaft

Siemens Corporation

Siemens Industry Software Gmbh

Siemens Industry, Inc.

Florida State University Research Foundation, Inc.

Siemens Industry Software Nv

Sanjeev Srivastava

Zhen Song

Lucia Mirabella

Arquimedes Martinez Canedo

Suraj Ravi Musuvathy

Erhan Arisoy

Livio Dalloro

Xianjun Sam Zheng

Justinian Rosca

Lingyun Wang

Leandro Pfleger De Aguiar

Dong Wei

Lukas Graber

Chris S Edrington

Xiaofan Wu

David Madeley

Harald Held

Gustavo Arturo Quiros Araya

Sindhu Suresh

Edward Slavin, Iii

Paul J Bruschi

Guannan Ren

Yaosuo Xue

Yubo Wang

Dmitriy Okunev

Yi Xu

Horatio Rodrigo

Gil Chen

Sudipta Pathak

Parag Patre

Yayun Zhou

Denis Smirnov

Wei Xia

Yaw D Nyanteh

Krzysztof Chalupka

Shaili Nepal

David S Cartes

Eduard Finaro

Janani Venugopalan

Marcin Staniszewski

Frederic Villeneuve

Edward Slavin Iii

Edward Iii Slavin

A computer-implemented method for manufacturing or controlling a technical system includes the method steps: (a) inputting a sample of parameter sets suitable for manufacturing or controlling the technical system, with a feasibility identifier assigned to each of the parameter sets, wherein the feasibility identifier marks each parameter set either as technically feasible or technically non-feasible or erroneous in terms of manufacturing or controlling the technical system, (b) generating a computerized surrogate model for the technical system based on the respective parameter sets of the sample, which are marked as technically feasible, by means of a regression method, (c) determining an optimized parameter set based on the surrogate model by means of a computerized optimization method, and (d) outputting the optimized parameter set for manufacturing or controlling the technical system.

Manufacturing or controlling a technical system using an optimized parameter set

A system and method for accelerated simulation setup includes receiving a description of a new problem for simulation, extracting input data and output data of previous simulation results, generating a representation of data based on the extracted input data and output data, and quantifying similarities between the new problem and the extracted input data and output data to identify a candidate simulation for the new problem. A machine learning component infers a solution output for the new problem based on extrapolation or interpolation of outputs of the candidate simulation, thereby conserving resources by eliminating a simulation generation and execution. Alternatively, an efficient simulation setup can be generated using the queried knowledge, input variables, and input parameters corresponding to the candidate simulation.

Accelerated simulation setup process using prior knowledge extraction for problem matching

Systems and methods are provided for generating a flow control plan for a plurality of components in a flow control system. A decentralized multi-agent control framework is used to plan and schedule for each agent independently without a central processor. Each agent of the multi-agent control framework separately optimizes a local portion of the system as a function of values for one or more parameters. Agents communicate with other connected agents, sharing values for parameters. The communication provides a negotiation and consensus for values of the shared parameters that are used by the agent to recalculate optimized parameters values for the local portion of the system.

Decentralized planning, scheduling and control of multi-agent flow control system

A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

System for automated generative design synthesis using data from design tools and knowledge from a digital twin

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

Method and apparatus for robust reduction of shape error in laser powder deposition based additive manufacturing process due to uncertainty

A computer-implemented method for quantifying assurance of a software system includes collecting artifacts of the software system generated during phases of the software system's engineering lifecycle. A graph of graphs (GoG) is constructed encoding the artifacts. Each subgraph in the GoG is a semantic network corresponding to a distinct assurance requirement. The GoG is used to calculate a component assurance value for each software component for each distinct assurance requirement. A system assurance value is calculated based on the component assurance values. An architectural view of the software system is presented showing at least one of the component assurance values and the system assurance values.

Ace: assurance, composed and explained

A computer-implemented method for detecting cyber-attacks affecting a computing device includes retrieving a plurality of sensor datasets from a plurality of sensors, each sensor dataset corresponding to involuntary emissions from the computing device in a particular modality and extracting a plurality of features from the plurality of sensor datasets. One or more statistical models are applied to the plurality of features to identify one or more events related to the computing device. Additionally, a domain-specific ontology is applied to designate each of the one or more events as benign, failure, or a cyber-attack.

Critical infrastructure forensics

A system and method is provided that facilitates threat impact characterization. The system may include a replica programmable logic controller (PLC) that corresponds to a production PLC in a production system and that may be configured to operate at an accelerated processing speed that is at least two times faster than a processing speed of the production PLC. The system may also include a data processing system configured to communicate with the replica PLC when executing malware infected PLC firmware and generate a simulation of the production system based on a virtual model of the production system operating at an accelerated processing speed that is at least two times faster than a processing speed of the physical production system. The simulation may include accelerated simulation of the production PLC based on communication with the replica PLC using the malware infected PLC firmware. The data processing system may also monitor: outputs from the replica PLC and the simulation of the production system to determine a possible threat to a hardware component of the production system caused by the production PLC executing the infected PLC firmware rather than a non-infected PLC firmware; and output data indicative of the possible threat thorough a display device.

System and method for threat impact characterization

A computing system may include a data access engine and a toolpath adjustment engine. The data access engine may be configured to access a computer-aided design (CAD) model of a part design and a computer-aided manufacturing (CAM) setup for the part design. The CAM setup may include a nominal toolpath specified through the CAD model for performing a finishing operation for the part design. The data access engine may also be configured to obtain 3-dimensional (3D) scan data for a physical part manufactured from the part design. The toolpath adjustment engine may be configured to extract, from the 3D scan data, a manufactured geometry of the physical part manufactured from the part design and generate an adjusted toolpath for the physical part to account for the manufactured geometry extracted from the 3D scan data.

Toolpath adjustments based on 3-dimensional scan data of physically manufactured parts

A computer-implemented method of splitting whole building energy consumption includes collecting sensor data from a plurality Variable Air Volume (VAV) boxes in a building, an electric meter in the building, and a gas meter in the building. The total heating energy consumption is split across a plurality of zones within the building using the sensor data, thereby yielding heating energy consumption for each zone. The total cooling energy consumption is split across the plurality of zones using the sensor data, thereby yielding cooling energy consumption for each zone and the total ventilation energy consumption is split across the plurality of zones using the sensor data, thereby yielding ventilation energy consumption for each zone. Zone energy consumption by aggregating the heating energy consumption for the zone, the cooling energy consumption for the zone, and the ventilation energy consumption for the zone.

Building energy split calculation and visualization

A system for calculating total lifecycle costs for a building energy management system includes lifecycle costs for a grid-scale battery system. The system includes a battery lifecycle optimizer configured to provide an optimal battery configuration for a building and a building energy system controller. The building energy system controller receives optimum battery configuration information, information relating to the heat ventilation air conditioning system of the building, intrinsic thermodynamic properties of the building, energy tariff schedules and weather forecast data to forecast a monthly peak demand profile. A simulation is run using a battery model, system model along with tariff and weather information to produce an hourly building energy management plan for minimizing overall energy costs with consideration of system lifecycle costs.

Adaptive demand response method using batteries with commercial buildings for grid stability and sustainable growth

A method of partitioning a model to facilitate printing of the model on a 3D printer includes identifying partition sensitive locations on the model and creating a binary tree with a root note representative of the model. An iterative partitioning process is applied to divide the model into objects by selecting a node of the binary tree without any children nodes, identifying a portion of the model corresponding to the node, and determining candidate cutting planes on the portion of the model based on the partition sensitive locations. During the process, analytic hierarchical processing (AHP) is applied to select an optimal cutting plane from the candidate cutting planes based on partitioning criteria. The optimal cutting plane is used to segment the portion of the model into sub-portions, and two children nodes representative of these sub-portions are created on the node of the binary tree.

Optimized partitioning of solid 3D models for additive manufacturing

A method for operating an air handling unit of an HVAC system. The method includes opening an outside air flow control device to enable breathing air flow during a high outdoor air time period to a first zone having a first number of occupants and a second zone having a second number of occupants that is less than the first number of occupants. The outside air flow control device is then closed to enable conditioning air flow during a low outdoor air time period to the first and second zones. Further, a variable air volume (VAV) air flow control device provides desired amounts of breathing air to the first and second zones suitable for the first and second number of occupants, respectively. A VAV air temperature control device then provides conditioning air to the first and second zones having a suitable temperature for the first and second number of occupants.

Zonal demand control ventilation for a building

A system and method for electrical tree simulation based on a modification of a discharge avalanche model with an application of a charge simulation method to determine partial discharge data during the growth of electrical trees in an insulation system and a method of using the model to determine the remaining useful life of an insulation system.

System and method for assessing the remaining useful life of an insulation system

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Princeton Junction, NJ, United States of America

Sanjeev Srivastava