2022 SUMMER UNDERGRADUATE RESEARCH FELLOWSHIP (SURF) PROJECTS
673.1 Qt-based GUI for ns-3
The Wireless Networks Division is developing a simulation platform to model next-generation wireless networks for public safety communication. To complement our platform we have been developing Graphical User Interface (GUI) tools to visualize the network topology and the data collected during our simulations (https://github.com/usnistgov/NetSimulyzer). This summer, you will develop new capabilities for the Graphical User Interface (GUI) based on Qt (www.qt.io), an open-source widget toolkit for creating graphical user interfaces. Your work will include the following: the development of the Qt interface, OpenGL rendering components, development of statistical models, and/or the creation of 3D models.
674.1 Interoperability Assessment Analytics of Smart Sensors
Smart sensors can provide real-time data and status of electrical power grids for real-time monitoring, protection, and control of grid operations to improve reliability and resilience of smart grids. Smart sensor data exchange and interoperability are major challenges for SGs. Interoperability measurement and assessment methods for smart sensors are keys to achieving and assuring the interoperability of smart sensors in smart grids. This project will study interoperability assessment methodology based on standard communication protocols, process model of interoperability developed in our current smart sensor project, design and develop open-source software tool to assess interoperability of phasor-measurement unit (PMU)-based smart sensors.
This project includes the following tasks:
674.2 Classification and inference of operating and market conditions contributing to resilience and failures on the electric power system
Researchers with the NIST Smart Grid Program are developing open-source tools using public data resources to model and evaluate the benefits to system performance from enhancements to interoperability and the new operating and control strategies made possible by such investments. Efforts to quantify the value propositions and performance outcomes of interoperability improvements are complicated by the complexity of interactions between devices, systems, and stakeholders; the opacity of competitively sensitive business operations; the diversity of market structures and operating conditions; and the limited opportunity to conduct experiments on live infrastructure systems. The conditions confronting the electric power system are as varied as the communities served by this critical infrastructure. Researchers seeking to value returns on infrastructure investment need flexible tools for evaluating the efficacy of operating strategies and the propensity for operating and market conditions to contribute to system resilience or failure. As a result, quantitative research has sought to build tools for the construction of plausible counterfactual scenarios through which economic analysis of alternative strategies may be conducted. One such tool currently under development at the NIST Smart Grid Program is the Generator Fleet Characteristics Model (GFCM), which consists of a set of Matlab functions for the wrangling and analysis of public data on the balancing authorities that comprise the electric power system. In the interest of improving the counterfactual analyses to be produced using the GFCM, the Smart Grid Program has an opportunity for a SURF researcher to develop a Matlab module for classification and inference with respect to GFCM model outputs, which detail the operating and market conditions present on the electric power system. A successful work effort will be incorporated into ongoing GFCM development and applications, and may therefore contribute to future modeling efforts and publications on the economics of emerging technology and operating strategies. The classification and inference module to be produced may involve methods and tools from computer science, engineering, economics, or the social sciences, including machine learning and/or automated algorithms for detection and classification of patterns within multivariate data structures. Consequently, students with strong computer skills, interest in these subject areas, and experience working with Matlab are encouraged to apply.
674.3 Impact of Transactive Energy on Residential Communities
Transactive Energy (TE) integrates controllable loads driven by real-time market conditions into the electric power grid. TE research requires complex models that combine multiple systems such as residential homes, commercial buildings, grid infrastructure, communication technologies, and economic markets. NIST developed a software tool called the Universal Cyber-Physical Systems Environment for Federation (UCEF) that enables researchers to run these complex experiments. An open research question is on the fairness of proposed TE implementations to residential customers including those in low-income neighborhoods. This project is focused on the development of a co-simulation that models the houses in a community, which will explore the potential effects of TE on residential homes with different income levels.
674.4 Co-Simulation of Thermal Energy Storage in Smart Buildings
The modernization of the electric grid, the Smart Grid, integrates information and communications technology with the grid to provide enhanced system control and to enable the bi-directional flow of power. These new capabilities require different approaches to grid management such as transactive energy (TE) which are techniques for managing the generation and consumption of electric power using controllable loads and economic or market-based constructs. With TE, buildings should use load shifting to move their energy consumption to periods of low prices. This project focuses on one method of load shifting that uses Heating, Ventilation, and Air Conditioning (HVAC) systems to pre-heat (or pre-cool) buildings before energy prices spike. A co-simulation of smart buildings in a transactive energy system will be developed to explore the potential impact and limitations of the approach.
674.5 Data tracing web platform
The recent and significant digitalization of industries has been driven by numerous potential benefits, from better physical goods to faster services. In this digital world, digital data becomes the key driver to many important decisions, processes, and flows within and across organizations. Unfortunately, this move into the digital word has exposed organizations to numerous new cyber threats that need to be addressed before they are exploited. One major threat, known as data tampering, discreetly modifies data to corrupt the processes and decisions that rely on it, and can quickly propagate itself within and across organizations. Because this tampering cannot always be prevented, understanding the exposure of an organization to such a threat is key to properly responding to it. This project focuses on improving our next-gen cybersecurity tool, to model, trace, and analyze data flows, to understand, control, and reduce exposure to data tampering in complex environments. This project will sharpen your programming skills and expand your cybersecurity knowledge.
674.6 CAD Interoperability Assessment Software Development
The NIST STEP File Analyzer (SFA) is free (and popular!) NIST software that evaluates how well software implements the STEP standard (ISO 10303: STandard for the Exchange of Product model data). STEP files are widely used for data exchange and interoperability between Computer-Aided Design (CAD), Manufacturing (CAM), Analysis (CAE), and Inspection (CMM) software. Even if you’ve never heard of STEP, saving a model as a STEP file is an option in all major CAD systems! Ensuring that all STEP translators write the same entities the same way is critical to interoperability – and the ability of lifecycle systems to communicate with each other. You will work with NIST researchers to identify priorities for redesigning and modernizing the existing SFA code base to optimize performance and reliability.
This project will include the following tasks:
1) Study existing SFA code, logic, and toolkit dependencies
2) Evaluate freely available SDKs for processing STEP data
3) Contribute to software redesign decisions
4) Rewrite portions of the SFA code
5) Test and document your code
6) Prepare and present your work in the SURF Colloquium
674.7 Integrated standards publication environment
The STEP standard (ISO 10303: STandard for the Exchange of Product model data) is developed and published in what was modern, cutting-edge document XSL/XSLT publication system … 20 years ago. In that time, STEP continues to be the most widely used format for data exchange and interoperability between Computer-Aided Design (CAD), Manufacturing (CAM), Analysis (CAE), and Inspection (CMM) software. The standard is regularly updated with new features, but the brittle publication tool chain is increasingly causing delays. The ISO Subcommittee responsible for this standard is embarking on an ambitious redesign of the publication environment that will automate the complex workflow and build in extensive error checking. We are seeking a student to participate on that team and contribute to the development and implementation of a modern, state-of-the-art, open-source publication system based on software engineering best practices, that is easier to install, use and maintain. This project is an opportunity to gain experience working on a sophisticated software engineering project alongside professional software developers, to build a turnkey solution for standards developers.
Depending on project status, your skills and interests, this project offers a variety of tasks such as:
1) CVS-to-Git migration support
2) Management of Git repo contents, tagging
3) Migrating image maps to SVGs
4) Migrating legacy documents to Annotated EXPRESS format (similar to JavaDocs)
5) Extract prior editions of standards from CVS to enable auto generation of document Change History
6) Build tools to generate the STEP Resource Library and STEP Application Protocols
7) Iterate and refine publication workflow
674.8 Simulation for AI Tool Evaluation
NIST is developing guidelines and tools to help US manufacturers cut through the marketing hype often associated with Artificial Intelligence (AI) systems, or ‘black box’ tools. These tools have significant potential for helping companies and workers, but there is no standard way for industry practitioners, especially small-to-medium sized manufacturers (SMMs) who lack expertise in these systems, to know whether a particular tool will result in significant benefit to their operation. To address this issue, we are developing standard methods for qualitative and quantitative measurement of AI driven tools. We seek to provide useful, practical, and impactful guidance to help companies make sound decisions to adopt or invest in decision support tools. One of the key tools we are building is a simulation engine that has the capability to replicate key aspects of a manufacturing factory process. This simulation is designed to help verify the suitability of digital monitoring and routing control systems to diagnose and respond to faults or failures in a multi stage manufacturing process. Many such systems are driven by various levels of AI technologies and this simulation environment provides an intuitive way to evaluate their impact on specific factory configurations prior to actual installation. Under mentorship of NIST researchers, the student will
1) Study project background documentation, related research, and code base
2) Assist NIST researchers in implementing AI algorithms for fault isolation
3) Collect and evaluate simulation engine data sets
4) Build or modify event simulation codes