Results for “open-source platform”

DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗

DOE OSTI2026

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗

DOE OSTI2026

Analysis of Power Electronic Solutions for Wide-Range Wave Energy Input

Wave energy is a largely untapped energy source with the potential to provide 290 TWh per year to the continental United States. As a new technology, wave energy converters are less efficient and reliable compared to established energy sources, leaving the vast energy of the sea largely unutilized. Wave energy is uniquely inconsistent, with large fluctuations. Paired with harsh operating environments and difficulty in repair, the power electronics designed to harvest wave energy need to be reliable, cost-effective, and able to work in a wide operating band. The literature on existing power electronics in wave energy converters and lessons learned across other industries provide a framework of topologies to simulate. WEC-Sim, an open-source hydrodynamic platform in MATLAB Simulink developed by the National Laboratory of the Rockies, was used to simulate a wide range of power electronics under a variety of wave conditions. This paper provides a comprehensive investigation into existing wave energy power electronics converters, state-of-the-art topologies that can be applied to wave energy, and novel solutions to wave conversion.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗

DOE OSTI2026

2021–2022 Can Do Colorado E-Bike Full-Scale Pilot Program Study

In 2021–2022, the Colorado Energy Office conducted a full-scale pilot program study on e-bike usage as part of the Can Do Colorado initiative, providing e-bikes to low-income participants across the state. A [2020 mini pilot program study](https://www.nlr.gov/transportation/secure-transportation-data/tsdc-2020-can-do-colorado-e-bike-pilot-program.html) informed the full-scale study. Both studies used pedal-assist e-bikes, which feature an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the study in partnership with local organizations in Adams and Broomfield counties (Smart Commute Metro North), Boulder (Community Cycles), Durango (Four Corners Office for Resource Efficiency), Fort Collins (City of Fort Collins), Pueblo (Pueblo County), and Vail (Town of Vail). #### Survey Methodology Program participants received an e-bike and accessories at no cost and manually submitted travel data and feedback via the CanBikeCO smartphone app. Developed in partnership with NLR, the app used a customized version of the open-source [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include 170 participants. The six datasets contain up to 18 months of partially automated travel diaries, combining sensed and surveyed travel behavior data—patterns of multimodal, end-to-end, individual human mobility—as well as demographic information from participants. The number of e-bike trips and e-bike miles traveled per location are 1,560 and 4,179 for Adams and Broomfield counties; 8,481 and 27,000 for Boulder; 2,815 and 6,307 for Durango; 3,483 and 7,080 for Fort Collins; 4,022 and 14,887 for Pueblo, and 1,206 and 3,3361 for Vail.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Graphical User Interface for a Biasing Board for the PSEC6

The PSEC6 is an application-specific integrated circuit (ASIC) designed for a readout system for a large area picosecond photodetector (LAPPD). The PSEC6 is currently in fabrication and pending testing. The testing system for the PSEC5, the previous iteration of the ASIC, required expensive and non-portable equipment, because the ASIC needs twelve adjustable reference voltages. The new testing system consists of an low-cost, open-source, cross-platform graphical user interface (GUI), a digital system, and a biasing board. The digital system is the interface between the GUI and biasing board, and can be implemented on a microcontroller or field-programmable gate array (FPGA). The biasing board contains twelve digital-to-analog converters (DACs) that are configurable via the GUI, which gives users the ability to write voltage values to all or specific DACs. The GUI was developed in C on Linux using the widget library GTK4 and cross-compiled for Windows compatibility. I2C and SPI protocols were implemented on an Adafruit Feather ESP32-S3 microcontroller to write commands to the DACs and PSEC6. A hardware implementation of the I2C protocol is in development on an FPGA. Since LAPPDs will be used by the Accelerator Neutrino Neutron Interaction Experiment (ANNIE) at Fermilab, the PSEC6 testing system in this internship project can potentially benefit future neutrino research. The project is relevant to the Department of Energy’s microelectronics mission, because the PSEC6 is an ASIC that will handle fast time signals arriving from the detector for readout. It also provided experience with building a cross-platform user interface, practicing digital design and implementation in hardware description language (HDL), and using simulations to inform new design iterations.

Guerrero, Sasha Camila [North Central Coll.]↗

DOE OSTI2026

Graphical User Interface for a Biasing Board for the PSEC6

The PSEC6 is an application-specific integrated circuit (ASIC) designed for a readout system for a large area picosecond photodetector (LAPPD). The PSEC6 is currently in fabrication and pending testing. The testing system for the PSEC5, the previous iteration of the ASIC, required expensive and non-portable equipment, because the ASIC needs twelve adjustable reference voltages. The new testing system consists of an low-cost, open-source, cross-platform graphical user interface (GUI), a digital system, and a biasing board. The digital system is the interface between the GUI and biasing board, and can be implemented on a microcontroller or field-programmable gate array (FPGA). The biasing board contains twelve digital-to-analog converters (DACs) that are configurable via the GUI, which gives users the ability to write voltage values to all or specific DACs. The GUI was developed in C on Linux using the widget library GTK4 and cross-compiled for Windows compatibility. I2C and SPI protocols were implemented on an Adafruit Feather ESP32-S3 microcontroller to write commands to the DACs and PSEC6. A hardware implementation of the I2C protocol is in development on an FPGA. Since LAPPDs will be used by the Accelerator Neutrino Neutron Interaction Experiment (ANNIE) at Fermilab, the PSEC6 testing system in this internship project can potentially benefit future neutrino research. The project is relevant to the Department of Energy’s microelectronics mission, because the PSEC6 is an ASIC that will handle fast time signals arriving from the detector for readout. It also provided experience with building a cross-platform user interface, practicing digital design and implementation in hardware description language (HDL), and using simulations to inform new design iterations.

Guerrero, Sasha Camila [North Central Coll.]↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

DOE OSTI2026

ENPAX (ENergy Platform — Automated eXpenditures) [SWR-26-053]

ENPAX (ENergy Platform — Automated eXpenditures) is an open-source Python library that provides modular, bottom-up capital and operating expenditure (CAPEX/OPEX) models for energy technologies. Cost estimates are derived using system design characteristics, site specific details, power system considerations and infrastructure assumptions.

Mulas Hernando, Daniel [National Laboratory of the↗

DOE OSTI2026

Lead Isotope Fingerprinting of Nanoscale Mineral Particles via Single Particle Inductively Coupled Plasma Mass Spectrometry

Determining lead (Pb) isotopic ratios is of broad interest across chemical disciplines for source tracing and age determinations, but the technique is inherently limited when multiple sources of Pb are present in a single sample. Single particle methods offer a solution to directly resolve multiple distinct isotopic ratios within individual samples. In this study, single particle inductively coupled plasma-mass spectrometry (spICP-MS) was performed using time-of-flight (TOF) and multicollector (MC)-based platforms to measure the Pb isotopic composition of individual nanoscale particles. Distinct Pb isotope ratios were measured in particles from four powdered galena samples of different origins (average single-particle 206Pb/207Pb ratios ranging from 0.91 to 1.28) using both the TOF and the MC-based platforms. Differentiation of galena particles in a mixture (liberated grains and those hosted in a silicate matrix) was possible due to detecting their unique multielemental fingerprints. The differing behavior of the two galena samples during digestion was investigated; undigested galena particles trapped within silicate minerals were detected using spICP-MS, which has implications for Pb recovery in bulk-scale isotopic analysis. The multinuclide detection capability of the ICP-TOF-MS instrument allowed for the simultaneous detection of secondary constituent elements within the nanoparticles and the identification of multiple populations of isotopically distinct Pb-bearing particles in a copper ore sample, whereas the increased sensitivity of the MC instrument enabled quantification of 204Pb, allowing differentiation using multiple isotopic ratios. A single particle isotope ratio analysis module was developed within an open-source spICP-TOF-MS data processing platform, enabling its adoption across chemical disciplines.

Goodman, Aaron J. [University of Montreal, Quebec,↗

DOE OSTI2026

Vision and Development of a Design, Implementation, and Verification Automation (DIVA) Software Platform for DNA Construction

Abstract DNA construction, while a prerequisite to many biological endeavors, is often a time-consuming distraction from an individual’s primary research objectives. We envisioned that with the right software infrastructure and cultural mindset, a single person could execute in parallel the batched DNA construction tasks of an entire research institute, at scales realizing efficiency gains through process and laboratory automation. In pursuit of this vision, we developed the Design, Implementation, and Verification Automation (DIVA) software platform. DIVA’s web interface enables researchers to design DNA constructs (using visual biological computer-aided design tools and biological parts repositories), submit designs for construction to dedicated staff, and track DNA construction as it progresses. DIVA supports the dedicated staff through the DNA construction process and records both successful and unsuccessful attempts toward improving the overall process. The platform is publicly available at public-diva.jbei.org and its open-source code through github.com/JBEI/DIVA.

Plahar, Hector [DOE Agile BioFoundry , , ,; DOE Jo↗

DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗

DOE OSTI2026

A Full-Induction Magnetohydrodynamics Solver for Liquid Metal Fusion Blankets in Vertex-CFD

Multiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy production. While traditional blanket modeling of liquid metal flows during normal steady operating conditions commonly employs the inductionless approximation of the magnetohydrodynamics (MHD) equations, transient scenarios, when the plasma-confining magnetic field varies on millisecond time scales, require a full-induction MHD approach that dynamically evolves the magnetic field via the time-dependent induction equation. This paper presents the formulation, implementation, and initial verification of a full-induction MHD solver integrated within the open-source Vertex-CFD framework, which aims to achieve tight multiphysics coupling, a flexible software design enabling easy extension and addition of physics models, and performance portability across computing platforms. The solver utilizes finite element spatial discretization, implicit Runge–Kutta time integration, and an inexact Newton method to solve the resulting discrete nonlinear system, leveraging Trilinos packages for efficient computation. Verification against selected benchmark problems demonstrates accuracy and robustness of the solver. Furthermore, when the solver is applied to an idealized blanket model in 2.5D and full 3D, results obtained with Vertex-CFD are in good agreement with recently published quasi-2D simulations. These findings establish a computational foundation for future simulations of transient MHD phenomena in liquid metal blankets with Vertex-CFD, and open avenues for future extensions and performance optimizations.

Endeve, Eirik [ORNL] (ORCID:0000000312519507)↗

DOE OSTI2026

Integrated Hydrogen Energy System Testing/Validation: Cooperative Research and Development Final Report, CRADA Number CRD-21-18272

Through the ARIES platform, this proposed project aims to bridge a gap and utilize the power hardware-in-the-loop (PHIL) and controller hardware-in-the-loop (CHIL) experiments and create open-source interconnection and interoperability information that can aid in faster and cheaper installation and operation of grid forming fuel cell inverters.

08 HYDROGEN↗

DOE OSTI2026

FOCAL Campaign IV: Integrated System Control: Turbine + Hull

Campaign IV of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of a floating offshore wind turbine with turbine and hull controls. The turbine considered in this campaign is the scaled IEA 15MW Reference Wind Turbine similar to the setup considered in FOCAL Campaign I. This turbine is mounted on the VolturnUS platform with integrated hull controls similar to the hull considered in FOCAL Campaign II/III. This system is deployed for a fully-coupled wind and wave testing campaign at the University of Maine's Harold Alfond Wind and Wave (W2) facility. System dynamics and turbine characteristics are measured to determine global performance and assess the effect of the control strategies employed. NREL's reference open-source controller (ROSCO) is used for active blade pitch control and hull structural control is examined through the use of tuned mass dampers (TMD's) tuned to the systems pitch and tower-bending natural frequency. The naming convention of the load cases considered are described in the data details section. Detailed properties on the modeled system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaign IV: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Platform. UMaine ASCC Report Number 23-57-1183.

17 WIND ENERGY↗

DOE OSTI2026

Lessons learned from the development and implementation of a workforce training curriculum for advanced controls for high performance HVAC systems

Over the past decade, academic research on advanced controls has slowly transitioned into new software platforms, giving rise to various companies developing and deploying these innovative products, including solutions for light commercial HVAC systems. However, the current workforce remains widely unprepared to install, maintain and operate these systems, particularly complex software-based control platforms, as most workforce training programs still focus on traditional building automation for large commercial buildings. This paper presents the development and piloting of curriculum for three key types of professionals: ● Technicians (trade-level): installing and maintaining modern high-performance HVAC systems and controls ● Programmers (undergrad-level): developing and implementing advanced controls ● Engineers and energy professionals (undergrad/grad-level): managing and evaluating system performance We share details of the material developed including training videos, open-source software, instruction manuals. We also present the results of a pilot implementation of the training materials with real students.

Casillas, Armando↗

DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗