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DOE OSTI2025

Open-source microreactor design models for technoeconomic assessments

Technoeconomic analyses for advanced nuclear technologies are essential for identifying cost drivers which allow for optimizing the technology to achieve better economic performance. Microreactors are emerging as a promising and reliable-energy solution, offering inherent safety, low capital investment, and rapid deployment capabilities. Past studies have conducted technoeconomic analyses for microreactors, but there remains a need for open-source technoeconomic models that can enhance collaboration, transparency, and consistency when performing this type of analysis. The present article introduces an open-source technoeconomic model for microreactors based on bottom-up cost estimation. Since no real cost data for microreactors exists, this model leverages cost data and insights gleaned from the Microreactor Applications, Research, Validation and Evaluation (MARVEL) project. It estimates the first-of-a-kind cost of two microreactor technologies and can also calculate the N th of a kind (NOAK) cost via accounting for learning and mass factory production. The two technologies considered in this paper are the liquid–metal thermal reactor (LTMR) and the gas-cooled microreactor (GCMR). In conclusion, the goal of this study is to demonstrate bottom-up cost estimation of these microreactor technologies and provide the cost estimation models that other users can leverage for various applications such as design optimization and financial planning.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

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

An Open Source Perovskite Solar Cell Curve Tracer

When developing systems for perovskite solar cells (PSCs), it is important to have a testing platform that closely resembles your deployed systems. While developing a 1 U CubeSat, the Big Red Sat-1 at the University of Nebraska-Lincoln, we found a lack of passive curve tracing instrumentation, which we needed for our mission. To that end, we developed a benchtop passive curve tracer that leverages the Ossila Solar Simulator as an AM1.5 light source. This device utilizes a ladder of switched resistors and a precision voltage and current measurement to achieve the curve tracing. It also integrates a common-anode multiplexer to switch through multipixel PSCs. Finally, the solar cell holder is separate from the main measurement system, so various substrates sizes and pixel counts can be supported. This system is tested using various PSCs. It has been open sourced to enable reuse in future systems that may benefit from the lower power consumption and flight heritage.

14 SOLAR ENERGY↗

DOE OSTI2025

Customized open source renewable energy models validated through PHIL lab experiments

Energy models for power systems require ongoing updates to reflect advancements in equipment technology and the increasing complexity of power electronic devices. This study utilizes a Power Hardware-in-the-Loop (PHIL) experimental setup to validate custom photovoltaic (PV) inverter models, aiming to enhance and expedite the development of advanced renewable energy models. The research compares the performance of a physical inverter with generic Renewable Energy Source (RES) models recommended by the Western Electricity Coordinating Council (WECC). As inverter-based renewable energy sources become more prevalent in modern electrical grids, it is crucial that dynamic models accurately represent their real-world behavior. Accurate models improve our understanding of these energy resources and their interactions with the grid. The proposed model enhancements are designed to better reflect real inverter performance, based on insights from PHIL experiments. These models are developed using the open source Modelica language and the OpenIPSL Modelica Library, allowing integration across various simulation tools without re-implementation. The paper concludes with a thorough assessment, comparing the enhanced models with PHIL experiments on a real PV inverter in a controlled laboratory setting. As a result, the study provides the enhanced WECC RES models and validation data as open source resources, facilitating further research and development.

Modelica↗

DOE OSTI2025

CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis

Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance. We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of Eulerian/stationary datasets external to the tracking data for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Catalight─An Open-Source Automated Photocatalytic Reactor Package Illustrated through Plasmonic Acetylene Hydrogenation

An open-source and modular Python package, Catalight, is developed and demonstrated to automate (photo)catalysis measurements. (Photo)catalysis experiments require studying several parameters to evaluate performance, including the temperature, gas flow rate and composition, illumination power, and spectral profile. Catalight orchestrates measurements over this complicated parameter space and systematically stores, analyzes, and visualizes the results. To showcase the capabilities of Catalight, we perform an automated apparent activation barrier measurement of acetylene hydrogenation over a plasmonic AuPd catalyst on an Al 2 O 3 support, simultaneously varying laser power, wavelength, and temperature in a multiday experiment controlled by a simple Python script. Our chemical results unexpectedly show an increased activation barrier upon light excitation, contrary to previous findings for other plasmonic reactions and catalysts. We show that the reaction rate order with respect to both acetylene and hydrogen remains unchanged upon illumination, suggesting that molecular surface coverage is not changed by light. By analyzing the inhomogeneity of the laser-induced heating, we attribute these results to a partial photothermal effect combined with a photochemical/hot electron-driven mechanism. In conclusion, our findings highlight the capabilities of a new experiment automation tool; explore the photocatalytic mechanism for an industrially relevant reaction; and identify systematic sources of error in canonical photocatalysis experimental procedures.

Catalysts↗

DOE OSTI2025

An Open-Source Framework for Rapid Validation of Scientific ASICs

Spacely is an open-source framework for the post-silicon validation of analog, digital, and mixed-signal ASICs (Application-Specific Integrated Circuits) which maximizes the reuse of hardware and software, reducing the time taken to achieve meaningful test results. Spacely specifically addresses the needs of small, flexible ASIC design teams commonly found in academia or research institutions which benefit most from sharing the overhead of test stand creation between many unique ASIC designs. Spacely is a set of software, firmware, and design practices. It targets two primary hardware platforms (NI-PXI and Caribou) as well as offering extensible support for bench instruments. Spacely provides a high-level Python interface to all test hardware for accessibility, while also giving more sophisticated teams the opportunity to integrate custom test firmware. The design principles of Spacely are presented in brief. Current documentation is available at https://github.com/SpacelyProject/spacely-docs.

Quinn, Adam [Fermilab]↗

DOE OSTI2026

NREL OpenPATH: An Open-Source, Extensible Platform for Instrumenting Travel Behavior Data

NREL OpenPATH is an open-source, extensible platform that allows communities to instrument their own travel behavior data. The platform consists of a smartphone app, server and analysis pipeline, and enables collection of opt-in, multi-modal, end-to-end travel diaries. It makes the aggregate statistics available via a public dashboard, and allows deployers to download and visualize trip and trajectory data through the admin dashboard. It also allows for customization of the initial demographic survey and the trip-level qualitative information collected. Our goal is to provide an easy-to-use tool that can democratize travel behavior data collection by empower communities of all sizes to recruit participants and obtain a holistic picture of their travel patterns. The platform has been used by close to 40 partners, to collect data from thousands of participants. Upon signing a simple MOU, it is currently available for free to universities, non-profits and public agencies in the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Intrusive Uncertainty Quantification and Optimal Experiment Design in the Open-Source Pyomo Ecosystem

This contribution describes ParmEst and Pyomo.DoE, two pillars of the open-source Python-based Pyomo ecosystem for computational optimization with (partial differential) algebraic equation mathematical models. Specifically, ParmEst facilitates intrusive frequentist parameter estimation (PE) and uncertainty quantification (UQ) through built-in features, such as covariance matrix estimation, bootstrapping, and likelihood ratio tests. Complementary, Pyomo.DoE enables optimal experiment design by maximizing various metrics of the Fisher information matrix, such as A-optimality (trace), D-optimality (determinant), E-optimality (minimum eigenvalue), and ME-optimality (condition number). ParmEst and Pyomo.DoE can solve high-dimensional optimization problems by leveraging the model structure and exact derivative information. Finally, we will discuss future opportunities to integrate PE and UQ capabilities with optimization under uncertainty, including robust optimization with non-convex models via PyROS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

PyHydroGeophysX: An extensible open-source platform for integrating hydrological models with geophysical measurements

Hydrological models and geophysical measurements are widely used tools for understanding subsurface hydrological processes relevant to water resource management, yet they typically remain disconnected due to technical barriers. We present PyHydroGeophysX, an open-source Python platform bridging this gap by providing standardized interfaces between hydrological modeling software (MODFLOW, ParFlow) and geophysical simulation tools (PyGIMLi, SimPEG). The platform implements bidirectional workflows: translating hydrological outputs into simulated geophysical responses through petrophysical models, and extracting hydrological information from geophysical inversions. Key features include bidirectional workflow modules, configurable petrophysical models, time-lapse inversion with temporal regularization, parallel computing, and mesh utilities for property transfer between geophysical and hydrological grids. The modular architecture of PyHydroGeophysX enables researchers to incorporate additional models and methods, fostering broader adoption of integrated hydrogeophysical approaches. The software is freely available on GitHub and is intended for researchers and practitioners working at the intersection of hydrology and geophysics.

Hydrogeophysics↗

DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

FOS: Computer and information sciences↗

DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

Schulte, Jan-Frederik [Purdue U.] (ORCID:000000034↗

DOE OSTI2025

PSCF+: An Extended and Improved Open-Source Software Package for Polymer Self-Consistent Field Calculations

This work introduces PSCF+, a freely available open‑source software tool for modeling how block copolymers self‑assemble into ordered nanoscale structures. The software allows researchers to study a wide range of polymer models and interaction types, making it possible to directly compare theoretical predictions with molecular and simulation‑based studies in a consistent way. PSCF+ incorporates several recently developed numerical methods that significantly reduce computer memory requirements and speed up calculations, enabling efficient use of modern GPU hardware. Here, it also provides automated tools to calculate free‑energy landscapes and phase boundaries, which are essential for understanding and predicting material behavior. Together, these advances make PSCF+ a powerful and flexible platform for studying polymer self‑assembly and support the design of new polymeric materials for scientific and technological applications.

Chemical calculations↗

DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗

DOE OSTI2025

Developing Open-Source Tools for Increasing the Efficiency of Synthetic Aviation Turbine Fuel Certification Process

FuelLib is an open-source Python-based fuel library, developed by NREL, that leverages the group contribution method (GCM) of [1] to systematically estimate the thermodynamic and transport properties of hydrocarbon fuels. FuelLib predicts these properties based on the molecular structure of individual compounds or compound families, using weight percentages of a fuel's composition, typically measured using techniques such as gas chromatography (GC). FuelLib enables property estimation over a wide range of temperatures and pressures of multi-component fuels in the absence of detailed molecular composition data, making it particularly valuable for complex fuel mixtures where detailed experimental characterization of fuel composition is unavailable. These capabilities contribute directly to synthetic aviation turbine fuels (SATF) development, supporting the short-term American Society for Testing and Materials (ASTM) qualification of drop-in fuels while potentially expanding ASTM boundaries to certify a broader range of fuels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Introducing Kynema, an Open-Source Performance-Portable Flexible-Multibody-Dynamics Solver

In this talk we introduce Kynema, an open-source general flexible-multibody-dynamics solver that is well suited for simulating wind turbine structural dynamics. Kynema uses a Lie-group time integrator for constrained systems and runs on both CPUs and GPUs. Timing results for simulations are presented for the IEA 15-MW turbine with and without aerodynamic forces.

17 WIND ENERGY↗

DOE OSTI2025

OpenEdge: A collaborative, open-source, multi-purpose direct simulation Monte Carlo for plasma simulation in magnetic fusion environments

OpenEdge is a collaborative, open-source, object-oriented Direct Simulation Monte Carlo (DSMC) code, designed specifically for plasma simulations in magnetic fusion environments. Here, the code features include advanced structures, robust capabilities, and an effective parallelization strategy, all of which significantly enhance performance. It includes specialized modules for managing complex particle interactions, including collisions, ionization/recombination, and reflection/sputtering. Benchmarks and performance analyses have confirmed its efficiency and scalability. Versatile and adaptable, OpenEdge is applied across a broad spectrum of plasma-material interaction studies and charged particle transport in various fusion research settings.

Boundary plasma↗

DOE OSTI2024

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗