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DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

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↗

NASA NTRS1993

Systems aspects of COBE science data compression

A general approach to compression of diverse data from large scientific projects has been developed and this paper addresses the appropriate system and scientific constraints together with the algorithm development and test strategy. This framework has been implemented for the COsmic Background Explorer spacecraft (COBE) by retrofitting the existing VAS-based data management system with high-performance compression software permitting random access to the data. Algorithms which incorporate scientific knowledge and consume relatively few system resources are preferred over ad hoc methods. COBE exceeded its planned storage by a large and growing factor and the retrieval of data significantly affects the processing, delaying the availability of data for scientific usage and software test. Embedded compression software is planned to make the project tractable by reducing the data storage volume to an acceptable level during normal processing.

Freedman, I.↗

NASA NTRS1994

Micromechanics Analysis Code (MAC) User Guide: Version 1.0

The ability to accurately predict the thermomechanical deformation response of advanced composite materials continues to play an important role in the development of these strategic materials. Analytical models that predict the effective behavior of composites are used not only by engineers performing structural analysis of large-scale composite components but also by material scientists in developing new material systems. For an analytical model to fulfill these two distinct functions it must be based on a micromechanics approach which utilizes physically based deformation and life constitutive models and allows one to generate the average (macro) response of a composite material given the properties of the individual constituents and their geometric arrangement. Here the user guide for the recently developed, computationally efficient and comprehensive micromechanics analysis code, MAC, who's predictive capability rests entirely upon the fully analytical generalized method of cells, GMC, micromechanics model is described. MAC is a versatile form of research software that 'drives' the double or triple ply periodic micromechanics constitutive models based upon GMC. MAC enhances the basic capabilities of GMC by providing a modular framework wherein (1) various thermal, mechanical (stress or strain control), and thermomechanical load histories can be imposed; (2) different integration algorithms may be selected; (3) a variety of constituent constitutive models may be utilized and/or implemented; and (4) a variety of fiber architectures may be easily accessed through their corresponding representative volume elements.

Wilt, T. E.↗

NASA NTRS2008

Intelligent Integrated Health Management for a System of Systems

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

Smith, Harvey↗

NASA NTRS2011

Object-Oriented Multi-Disciplinary Design, Analysis, and Optimization Tool

An Object-Oriented Optimization (O3) tool was developed that leverages existing tools and practices, and allows the easy integration and adoption of new state-of-the-art software. At the heart of the O3 tool is the Central Executive Module (CEM), which can integrate disparate software packages in a cross platform network environment so as to quickly perform optimization and design tasks in a cohesive, streamlined manner. This object-oriented framework can integrate the analysis codes for multiple disciplines instead of relying on one code to perform the analysis for all disciplines. The CEM was written in FORTRAN and the script commands for each performance index were submitted through the use of the FORTRAN Call System command. In this CEM, the user chooses an optimization methodology, defines objective and constraint functions from performance indices, and provides starting and side constraints for continuous as well as discrete design variables. The structural analysis modules such as computations of the structural weight, stress, deflection, buckling, and flutter and divergence speeds have been developed and incorporated into the O3 tool to build an object-oriented Multidisciplinary Design, Analysis, and Optimization (MDAO) tool.

Pak, Chan-gi↗

NASA NTRS2014

Supersonic Wing Optimization Using SpaRibs

This research investigates the advantages of using curvilinear spars and ribs, termed SpaRibs, to design a supersonic aircraft wing-box in comparison to the use of classic design concepts that employ straight spars and ribs. The objective is to achieve a more efficient load-bearing mechanism and to passively control the deformation of the structure under the flight loads. Moreover, the use of SpaRibs broadens the design space and allows for natural frequencies and natural mode shape tailoring. The SpaRibs concept is implemented in a new optimization MATLAB-based framework referred to as EBF3SSWingOpt. This optimization scheme performs both the sizing and the shaping of the internal structural elements, connecting the optimizer with the analysis software. The shape of the SpaRibs is parametrically defined using the so called Linked Shape method. Each set of SpaRibs is placed in a one by one square domain of the natural space. The set of curves is subsequently transformed in the physical space for creating the wing structure geometry layout. The shape of each curve of each set is unique; however, mathematical relations link the curvature in an effort to reduce the number of design variables. The internal structure of a High Speed Commercial Transport aircraft concept developed by Boeing is optimized subjected to stress, subsonic flutter and supersonic flutter constraints. The results show that the use of the SpaRibs allows for the reduction of the aircraft's primary structure weight without violating the constraints. A weight reduction of about 15 percent is observed.

Locatelli, David↗

DOE OSTI2025

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

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

NASA NTRS1992

The experience factory: Can it make you a 5? or what is its relationship to other quality and improvement concepts?

The concepts of quality improvements have permeated many businesses. It is clear that the nineties will be the quality era for software and there is a growing need to develop or adapt quality improvement approaches to the software business. Thus we must understand software as an artifact and software as a business. Since the business we are dealing with is software, we must understand the nature of software and software development. The software discipline is evolutionary and experimental; it is a laboratory science. Software is development not production. The technologies of the discipline are human based. There is a lack of models that allow us to reason about the process and the product. All software is not the same; process is a variable, goals are variable, etc. Packaged, reusable, experiences require additional resources in the form of organization, processes, people, etc. There have been a variety of organizational frameworks proposed to improve quality for various businesses. The ones discussed in this presentation include: Plan-Do-Check-Act, a quality improvement process based upon a feedback cycle for optimizing a single process model/production line; the Experience Factory/Quality Improvement Paradigm, continuous improvements through the experimentation, packaging, and reuse of experiences based upon a business's needs; Total Quality Management, a management approach to long term success through customer satisfaction based on the participation of all members of an organization; the SEI capability maturity model, a staged process improvement based upon assessment with regard to a set of key process areas until you reach a level 5 which represents a continuous process improvement; and Lean (software) Development, a principle supporting the concentration of the production on 'value added' activities and the elimination of reduction of 'not value added' activities.

Basili, Victor R.↗

DOE OSTI2025

Framework for Assessing Impact of Wave-Powered Desalination on Resilience of Coastal Communities

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

16 TIDAL AND WAVE POWER↗

NASA NTRS2018

Increasing Flight Software Reuse with OpenSatKit

In January 2015 the NASA Goddard Space Flight Center (GSFC) released the Core Flight System (cFS) as open source under the NASA Open Source Agreement (NOSA) license. The cFS is based on flight software (FSW) developed for 12 spacecraft spanning nearly two decades of effort and it can provide about a third of the FSW functionality for a low-earth orbiting scientific spacecraft. The cFS is a FSW framework that is portable, configurable, and extendable using a product line deployment model. However, the components are maintained separately so the user must configure, integrate, and deploy them as a cohesive functional system. This can be very challenging especially for organizations such as universities building cubesats that have minimal experience developing FSW. Supporting universities was one of the primary motivators for releasing the cFS under NOSA. This paper describes the OpenSatKit that was developed to address the cFS deployment challenges and to serve as a cFS training platform for new users. It provides a fully functional out-of-the box software system that includes NASA's cFS, Ball Aerospaceâ€"TM"s command and control system COSMOS, and a NASA dynamic simulator called 42. The kit is freely available since all of the components have been released as open source. The kit runs on a Linux platform, includes 8 cFS applications, several kit-specific applications, and built in demos illustrating how to use key application features. It also includes the software necessary to port the cFS to a Raspberry Pi and instructions for configuring COSMOS to communicate with the target. All of the demos and test scripts can be rerun unchanged with the cFS running on the Raspberry Pi. The cFS uses a 3-tiered layered architecture including a platform abstraction layer, a Core Flight Executive (cFE) middle layer, and an application layer. Similar to smart phones, the cFS application layer is the key architectural feature for userâ€"TM"s to extend the FSW functionality to meet their mission-specific requirements. The platform abstraction layer and the cFE layers go a step further than smart phones by providing a platform-agnostic Application Programmer Interface (API) that allows applications to run unchanged on different platforms. OpenSatKit can serve two significant architectural roles that will further help the adoption of the cFS and help create a community of users that can share assets. First, the kit is being enhanced to automate the integration of applications with the goal of creating a virtual cFS 'App Store'. Second, a platform certification test suite can be developed that would allow users to verify the port of the cFS to a new platform. This paper will describe the current state of these efforts and future plans.

McComas, David↗

NASA NTRS2018

Increasing Flight Software Reuse with OpenSatKit

In January 2015 the NASA Goddard Space Flight Center (GSFC) released the Core Flight System (cFS) as open source under the NASA Open Source Agreement (NOSA) license. The cFS is based on flight software (FSW) developed for 12 spacecraft spanning nearly two decades of effort and it can provide about a third of the FSW functionality for a low-earth orbiting scientific spacecraft. The cFS is a FSW framework that is portable, configurable, and extendable using a product line deployment model. However, the components are maintained separately so the user must configure, integrate, and deploy them as a cohesive functional system. This can be very challenging especially for organizations such as universities building cubesats that have minimal experience developing FSW. Supporting universities was one of the primary motivators for releasing the cFS under NOSA. This paper describes the OpenSatKit that was developed to address the cFS deployment challenges and to serve as a cFS training platform for new users. It provides a fully functional out-of-the box software system that includes NASA's cFS, Ball Aerospace's command and control system COSMOS, and a NASA dynamic simulator called 42. The kit is freely available since all of the components have been released as open source. The kit runs on a Linux platform, includes 8 cFS applications, several kit-specific applications, and built in demos illustrating how to use key application features. It also includes the software necessary to port the cFS to a Raspberry Pi and instructions for configuring COSMOS to communicate with the target. All of the demos and test scripts can be rerun unchanged with the cFS running on the Raspberry Pi. The cFS uses a 3-tiered layered architecture including a platform abstraction layer, a Core Flight Executive (cFE) middle layer, and an application layer. Similar to smart phones, the cFS application layer is the key architectural feature for users to extend the FSW functionality to meet their mission-specific requirements. The platform abstraction layer and the cFE layers go a step further than smart phones by providing a platform-agnostic Application Programmer Interface (API) that allows applications to run unchanged on different platforms. OpenSatKit can serve two significant architectural roles that will further help the adoption of the cFS and help create a community of users that can share assets. First, the kit is being enhanced to automate the integration of applications with the goal of creating a virtual cFS "App Store".. Second, a platform certification test suite can be developed that would allow users to verify the port of the cFS to a new platform. This paper will describe the current state of these efforts and future plans.

Computer Programming and Software↗

DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗

NASA NTRS2014

Test Driven Development of Scientific Models

Test-Driven Development (TDD), a software development process that promises many advantages for developer productivity and software reliability, has become widely accepted among professional software engineers. As the name suggests, TDD practitioners alternate between writing short automated tests and producing code that passes those tests. Although this overly simplified description will undoubtedly sound prohibitively burdensome to many uninitiated developers, the advent of powerful unit-testing frameworks greatly reduces the effort required to produce and routinely execute suites of tests. By testimony, many developers find TDD to be addicting after only a few days of exposure, and find it unthinkable to return to previous practices.After a brief overview of the TDD process and my experience in applying the methodology for development activities at Goddard, I will delve more deeply into some of the challenges that are posed by numerical and scientific software as well as tools and implementation approaches that should address those challenges.

Overview of TDD Process↗

DOE OSTI2025

SpacelyProject/spacely-docs

Quinn, Adam [Fermi National Accelerator Laboratory↗

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

Bioreactor Optimization through Multi-Phase Flow Models (CRADA Final Report)

Chemical manufacturing uses 29% of energy in the United States and produces 925 million metric tons of CO2 annually. Biomanufacturing offers the potential to leverage America’s rich agricultural resources to produce critical chemicals such as lubricants, pharmaceutical precursors, and components of energetic materials that today are sourced extensively from overseas. The bioreactors used in biomanufacturing applications, such as one developed by Capra Biosciences, involve multiphase flow of biofilm-coated solid support particles that are continuously circulated in a fluidized state within the reactor along with a constant supply of oxygen via an aeration mechanism. In this project, Capra Biosciences and LBNL developed a multiscale modeling framework to simulate the multiphase flows of solid particles in a liquid-gas bubble mixture that occurs in the bioreactor using the current MFIX-Exa software, an opensource multiphase flow solver developed and maintained at LBNL and NETL. By leveraging HPC capabilities, this high-fidelity multiscale model was used to inform design decisions for bioreactor architecture to make them operationally efficient.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗