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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A meshing framework for digital twins for extrusion based additive manufacturing

Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.

Additive manufacturing↗
DOE OSTI2021

Analysis of basic airflow configurations for separate sensible and latent cooling systems with indoor air recirculation

Separate sensible and latent cooling (SSLC) is a technology with efficiency and comfort advantages over conventional cooling systems used for space conditioning of buildings. Using multiple cooling processes at different temperatures allows SSLC to save energy by raising the evaporation temperature of the sensible cooling process. In this paper, all possible airflow configurations of SSLC systems are enumerated under the following constraints: exactly two heat exchangers are used, and air is recirculated to the conditioned space (no exhaust or outdoor air treatment). Seven designs are identified, with varying free operating variables, and each is modeled. Analysis reveals that several configurations are equivalent, and there is only one unique basic airflow SSLC configuration: the one with the sensible and latent heat exchangers placed in series. The efficiency of the SSLC system is compared against that of the conventional system. Under standard conditions, an SSLC system can improve the coefficient of performance by 14.8%. In addition to the numerical simulation, the optimal operating condition of the basic air configuration of the SSLC system is derived analytically. The basic SSLC system is shown to offer the highest performance improvement when the outdoor temperature is relatively cool and the space sensible heat ratio is high.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗
DOE OSTI2023

All-temperature zinc batteries with high-entropy aqueous electrolyte

Electrification of transportation and rising demand for grid energy storage continue to build momentum around batteries across the globe. However, the supply chain of Li-ion batteries is exposed to the increasing challenges of resourcing essential and scarce materials. Therefore, incentives to develop more sustainable battery chemistries are growing. Here, in this paper, we show an aqueous ZnCl 2 electrolyte with introduced LiCl as supporting salt. Once the electrolyte is optimized to Li 2 ZnCl 4 ∙9H 2 O, the assembled Zn–air battery can sustain stable cycling over the course of 800 hours at a current density of 0.4 mA cm -2 between -60 °C and +80 °C, with 100% Coulombic efficiency for Zn stripping/plating. Even at -60 °C, >80% of room-temperature power density can be retained. Advanced characterization and theoretical calculations reveal a high-entropy solvation structure that is responsible for the excellent performance. The strong acidity allows ZnCl 2 to accept donated Cl - ions to form ZnCl 4 2- anions, while water molecules remain within the free solvent network at low salt concentration or coordinate with Li ions. Our work suggests an effective strategy for the rational design of electrolytes that could enable next-generation Zn batteries.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Lessons Learned in Systems Engineering Availability and Recommendations for Mission Technical Leaders

In spaceflight missions at Goddard Space Flight Center (GSFC), the Mission System Engineer (MSE) is the technical leader of the overall engineering team and also is the Independent Engineering Technical Authority. The responsibility of this role includes the definition of the mission design architecture, concept of operations, and mission requirements, management of risk throughout the development, and verification and validation of the final system performance and function amongst other duties. This responsibility inherently requires time management, enabling focus on a balanced development with appropriate risk. Time is the most valuable resource of the MSE. The system engineer’s availability to interact with the development team (often product or component design leads and technicians, often in different worksites) to discover and mitigate mission risks during development is key to mission success. This paper presents examples from Lunar Reconnaissance Orbiter, Landsat 9, and Neutron star Interior Composition ExploreR (NICER) which represent in-house and out-of-house hardware builds. These examples demonstrate how interactions between the Mission Systems Engineers and other project and partner engineers result in discovery of critical risks, leading to early mitigation with significant cost and performance savings. These three missions would have suffered test failures or on-orbit failures had their MSEs not set aside time to visit engineers and technicians that were working on key pieces of space flight hardware. Availability is more than just time; it is openness to listen to concerns and questions. It begins by building a level of trust in the team that it is safe to ask questions or share concerns without the fear of blame or additional workload. It also requires enabling informal conversations (over lunch, coffee, in the clean room, or at the team members desk, etc.) where key information can be exchanged, and team members may even provide an easy-to-implement mitigation idea for another subsystem. Availability is a highly valuable commodity and completely non-obvious to protect and optimize. The natural tendency of engineers is to keep themselves busy with solving problems that they know about. This paper is encouraging MSEs to resist this tendency to try and solve all the complex problems themselves and actively devote daily time to learning and solving problems that are found with informal communications with other team members.

Lessons Learned↗
DOE OSTI2026

Challenges and Opportunities for HVAC + Phase Change Material Thermal Energy Storage in Buildings

Phase-change material (PCM) thermal-energy storage (TES) integrated with HVAC and domestic hot water (DHW) can shift a large share of building thermal loads. By flattening and shifting loads, PCM TES reduces peak electricity use, eases stress on local and grid infrastructure, and lowers costs. It can also defer costly upgrades to service panels, distribution, and transmission. Higher energy density relative to chilled or hot water storage makes PCM TES practical for small, space limited, and retrofit projects, while packaged HVAC-integrated systems expand cost-effective load shifting to commercial buildings that previously lacked options. Despite this promise, deployment faces barriers. This paper presents challenges, opportunities, and lessons learned from lab and field integrations of PCM TES with packaged HVAC systems. Key challenges include misalignment between default heat pump controls tuned for direct-to-load operation and TES charge/discharge objectives, PCM properties that diverge from manufacturer claims and evolve with cycling, and high integration and deployment costs with off-the-shelf products. These studies underscore the need for factory built, integrated systems that arrive as prepackaged modules with proven controls, reducing design and installation effort and delivering predictable performance across projects. The paper outlines steps to enable viable and scalable PCM TES HVAC and DHW systems, including 1) validated methods that can be used to characterize PCM properties and TES system performance, 2) supervisory controls that optimize charge/discharge scheduling, and 3) factory integrated packaging that eliminates bespoke field engineering.

Dutton, Spencer↗
DOE OSTI2021

Smart Labs Final Report Summer 2021

The Smart Labs Project at Los Alamos National Laboratory (LANL) is an initiative derived from The University of California, Irvine and is part of the Department of Energy’s (DOE) Better Buildings Challenge. These carbon abatement strategies aim to reduce energy consumption of laboratories while also maintaining health and safety requirements. Smart Labs designs incorporate seven key principles which are: digital control systems, demand-based ventilation, low power-density demand-based lighting, exhaust fan discharge velocity optimization, pressure drop optimization, fume hood flow optimization, and commissioning with automated cross-platform fault detection. As the ALDCP Smart Labs team for the summer of 2021, the scope of the project is to determine the energy savings within building 03-1698 (Material Science Laboratory - MSL). Over the past couple of years, the Sustainability Group has been adding Smart Labs upgrades into the MSL building and the summer team would like to understand the impact made for the overall energy consumption/demand and safety for the building, determine the overall return on investment (ROI), and recommend more Smart Labs upgrades that can be added to the MSL building. The goal is to enable the UI FOD (Utilities and Infrastructure Facility Operation Division) to promote more Smart Labs projects in the future and further the reputation LANL and DOE facilities have of being leading examples of developers of high performing buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Additive Manufacture of Multi-Functional, Large-Scale Components

The poster summarizes the main results for a LDRD focused on developing a arc-based additive manufacturing (AM) system capable of fabricating large components with a composition gradient otherwise known as functionally graded materials (FGMs). FGMs are capable of addressing local extreme conditions in an optimal and cost-effective manner. The AM system (referred to as multi-Wire Arc Additive Manufacturing or m-WAAM) consisted of modifying a standard automated gas tungsten arc welding system to deliver multiple welding wires to the weld pool at controlled rates to deposit desired alloy compositions at critical locations. Numerous shapes and composition gradients were fabricated with the system and the effect of various deposition parameters on the nature of the composition profile were evaluated. Additionally, heat treatment of the FGM was performed to develop graded microstructures which should enhance dynamic impact performance of inexpensive steels. The project highlights the potential of m-WAAM to efficiently fabricate FGMs. However, this project only explored the science behind the technique and additional development work will be required to develop a fully-functional system that can build more complex components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microencapsulation of bio-based phase change materials with silica coated inorganic shell for thermal energy storage

This study investigates the use of cenospheres to encapsulate a low-cost, bio-based phase change material (PCM) derived from refined edible vegetable oil. Chemical etching was applied on the cenospheres to create holes through which melted PCM was loaded to produce a PCM microcapsule. Subsequently, a silica-based coating was applied to seal the perforations and prevent leakage of PCM from the microcapsule. Microstructural, chemical compatibility, thermal, and leakage properties of the produced silica coated PCM microcapsule (SCPCM) were assessed. No leakage was found for this new microcapsule, and higher thermal stability and conductivity were noted. Thermal decomposition of the PCM in SCPCM was delayed by about 16 degrees C attributable to the silica coating. Additionally, an enhanced compressive strength performance was achieved at 10 vol% of sand replacement with SCPCM. 20-30 vol% replacement of sand with cenosphere microcapsules seems to be the optimal range for reasonable compressive strength. The workability of the mortar incorporated with SCPCM decreased when the replacement level increased to more than 30 vol% due to the hydrophilic nature of the silica deposit. This study has demonstrated the potential of cenospheres as inorganic shell for PCMs and the potential for incorporation of SCPCM into construction materials for thermal energy storage (TES).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of Integrated Mechanical Pods

This presentation highlights early wins, updated progress, and upcoming developments on ‘national-scale shared development platform’ for rapid prototyping, testing and validation of various integrated Mechanical Pod solutions and form factors. Such pod solutions consist of a set of all-electric heat pump mechanical equipment that have integrated functionalities through built-in controls, with heating, cooling, hot water, ventilation (including energy recovery), electrical management, and battery storage within a single package. The presentation draws inspiration from the success of bathroom pods in the US modular construction industry, UK’s efforts with unitizing mechanical systems as ‘utility cupboards’, and VEIC’s early wins in design-build of all-electric Mechanical Pod solutions in Vermont. The presentation includes researchers and partners involved with NREL in Design for Manufacturing and Assembly (DfMA), Virtual Design and Construction (VDC), and digital twin based process optimization modeling of integrated Mechanical Pod solutions. The presentation aims to highlight early wins from such a platform and how various physical and virtual tools are currently being employed as part of NREL’s ongoing multi-year project funded by US DOE. Streamlined procurement, coordination, installation, and O&M of Mechanical Pods such that the majority of work is delegated to the off-site modular factory implies monetary savings. Such a seemingly basic shift in location of the construction process leads to great reduction in complexity, first cost, lead time, and waste, and greater opportunities for innovative compartmentalization and integration of mechanical systems appropriately sized for each apartment or hotel guest room. However, past studies on unitized combination systems show that high installation costs, maintenance issues, challenges with system integration, limitations in existing electrical infrastructure, and lack of architecturally appealing solutions are key barriers. NREL and partners aim to address key barriers through DfMA approach, rapid prototyping and testing, and digital twin process optimization modeling. The presentation is also a call for interested entities to partner with NREL as part of the national-scale development platform, help drive both product and process innovation, and encourage open source sharing of learnings. Learning objectives include (1) learn about the vision of national-scale shared development platform for process-product innovation on integrated mechanical pod solutions and how to get involved, (2) gain an understanding of the components of an all-electric, high performance home, design characteristics and equipment included in an all-electric mechanical pod, integration of mechanical systems within a modular factories’ assembly line, and the system’s commissioning, operation and maintenance. The pre-planning and coordination with the factory and sub-contractors are also highlighted, (3) gain an understanding of using process modeling tools to quantify resource-constrained performance of operations (such as integration of energy efficiency strategies) to manufacture modules of varying design, (4) gain insights on virtual design, rapid prototyping, and emulated testing of various form factors across different climatic conditions. The need for such preliminary testing with open source sharing of learnings will also be highlighted.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Proliferated Resilient Economical Half-Meter Aperture Space Telescopes (PREEMPT)

The PREEMPT LDRD was motivated by a major national need for better space-based imaging systems that are both high performing and affordable for the US Government. Current optical payloads for intelligence, surveillance and reconnaissance (ISR) and space domain awareness (SDA) missions can cost hundreds of millions of dollars and take years to develop, which makes it difficult to build the large constellations required for persistent, world-wide coverage. To address this, the team aimed to advance a different kind of large-aperture (>25 cm) telescope, called a monolithic telescope, in which key optical surfaces are built into a single piece of fused silica. This design greatly reduces payload and spacecraft complexity, the need for precision focus actuators, improves mechanical and thermal robustness, and lowers cost when compared with traditional Cassegrain telescopes that rely on many precisely aligned components. The project focused on five primary thrusts. The first thrust was to advance the concept of a V10, 25 cm, monolithic telescope forward from optical design to flight-ready stage. This was accomplished in partnership with Optimax, who delivered the first test unit in the early stages of the LDRD. The team developed several technologies necessary for this optic to be integrated into a flight demonstration. These include carbon fiber housings, highly detailed structural and thermal models and stress-reducing elastic averaging Hirth groove designs. These technologies resulted in a successful maturation of the optic, which is now slated to fly in late 2026/early 2027 for a demonstration mission. The second thrust was to advance the manufacturability of these optics. In collaboration with NIF’s optical manufacturing shop, we reduced polishing time from 480 hours to 65 hours through the implementation of optimized processes and new tools. The NIF team utilized a conceptual V8 (18 cm) optic to demonstrate this optimization, though it can be applied to the rest of the monolithic optic portfolio. Third, the team developed the first conceptual V20 (50 cm) payload, which is slated to be the next generation of LLNL optical payload systems. A set of structural, dynamic and thermal simulations were performed to identify potential challenges in the future development of this payload. Early-stage simulations suggest the payload is feasible, though thermal management will be the key focus area to maintain optimal performance. Fourth, a non-linear model of Viton was developed, to further enhance the reliability of our structural and dynamic models for future payloads. Viton acts as the primary interface material between the optic and its housing. Lastly, the team focused on successfully displaying the feasibility of using additively manufactured metal composites for optical space payloads. The team successfully demonstrated layer by layer deposition of Al-SiC composites, which have highly tunable structural and coefficient of thermal expansion (CTE) properties. These are crucial for optical payloads because CTE mismatch is one of the causes for degraded optical performance for telescopes in orbit. Overall, the work showed that monolithic telescopes could become a practical, lower-cost path to high-resolution space imaging for both national security and scientific missions.

42 ENGINEERING↗
DOE OSTI2021

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Celestial Mapping System for Lunar Surface Mapping and Analytics

Celestial Mapping System (CMS) is a software platform to generate virtual 3D globe for celestial bodies within our solar system. Various layers are built on top of the virtual globe to provide visualization of high resolution imagery, enable precise measurements, build analytical capabilities and broad range of functionalities to assist planetary scientists and mission planners. CMS is built using OpenJDK 11 and will run on a wide variety of platforms such as Linux, Windows, OSX, etc. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The present focus of CMS is on developing lunar mapping tool kits to provide features such as - 3D first person view with zoom and navigational capabilities, realistic terrain visualization based on LRO data, measurement tools, Apollo landing site annotations, stereoscopic view, elevation profiles, line of sight analysis and many more. The application is developed to provide situational and domain awareness on Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on Lunar surface. CMS utilizes NASA WorldWind Java library and OpenGL to achieve high-performance rendering of data and measurements, and also adheres to OGC standards. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. Nomenclature is pulled from USGS Moon IAU2000 database, and lunar parameters are based of the standardized IAU2000 Moon ellipsoid. GDAL (Geospatial Data Abstraction Library) was used to modify and test the accuracy of datasets before integrating into the application. Our high-resolution global elevation model was compared with the LRO LOLA DEM elevation values and tested to ensure accuracy. Celestial Mapping System has several potential use cases for NASA including subsurface lava tubes visualization and analysis, soil analysis, resource visualization and representation on 3D globe.

GIS system↗
DOE OSTI2023

Control Systems Design for STS Accelerator

The Second Target Station (STS) Project will expand the capabilities of the existing Spallation Neutron Source (SNS), with a suite of neutron instruments optimized for long wavelengths. A new accelerator transport line will be built to deliver one out of four SNS pulses to the new target station. The Integrated Control Systems (ICS) will provide remote control, monitoring, OPI, alarms, and archivers for the accelerator systems, such as magnets power supply, vacuum devices, and beam instrumentation. The ICS will upgrade the existing Linac LLRF controls to allow independent operation of the FTS and STS and support different power levels of the FTS and STS proton beam. The ICS accelerator controls are in the phase of preliminary design for the control systems of magnet power supply, vacuum, LLRF, Timing, Machine protection system (MPS), and computing and machine network. The accelerator control systems build upon the existing SNS Machine Control systems, use the SNS standard hardware and EPICS software, and take full advantage of the performance gains delivered by the PPU Project at SNS.

Yan, Jay↗
DOE OSTI2025

Advanced Transmission Technologies –GETs and HPCs Session 3: HPCs and Building Actions Plans to Digital Assurance Risks

The third session of the Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program, held on November 11, 2025, centered on High Performance Conductors (HPCs) and the formulation of action plans to address digital assurance risks associated with Grid-Enhancing Technologies (GETs). This session convened experts from utilities, vendors, and government agencies to examine the technical, operational, and cybersecurity aspects of HPC deployment. Discussions highlighted the benefits of HPCs, such as their ability to rapidly increase transmission capacity using existing corridors, improve grid resilience, reduce system losses, and align with FERC Orders 2023 and 1920. Participants evaluated supply chain and digital assurance risks, including reliance on imported materials, limited domestic manufacturing capacity, workforce shortages, and traceability issues. The session also emphasized the importance of digital trust, integration-layer cybersecurity, and unified risk frameworks, introducing tools like intrusion detection systems, encryption, zero trust networking, and firmware integrity. Recaps of earlier workshops on Dynamic Line Ratings (DLRs), Advanced Power Flow Control (APFC), and Transmission Topology Optimization (TTO) underscored institutional barriers and integration challenges. Action plans were proposed to mitigate issues such as inconsistent cybersecurity practices, SBOM usage, supply chain visibility, operator trust, and misaligned incentives. Additionally, INL presented its supply chain risk management tools and Cyber-Informed Engineering (CIE) principles to support secure procurement and system design. The session concluded with a commitment to share key takeaways, incorporate cohort feedback into future policy development, and continue collaborative engagement through upcoming pilot activities. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2025

Carbonation reaction of recycled concrete aggregates (RCA): CO 2 mass consumption under various treatment conditions

Concrete is a key building material around the world due to its excellent strength and durability. Recycling demolished concrete for new construction materials may play a significant role in sustainable development. Producing recycled concrete aggregates (RCA) from waste concrete is one approach for such an initiative. However, using RCA may pose challenges, such as reduced density, lower elastic modulus and strength, and increased water absorption. Recently, the carbonation of RCA has emerged as a method to address those concerns. This study explores the carbon sequestration capacity of RCA through carbonation, examining various parametric conditions, including initial CO 2 pressure, relative humidity, temperature, and pre-treatment approach. Both lab-scale and large-scale carbonation tests were conducted. Additionally, a cost analysis and CO 2 footprint assessment were performed. The findings showed that applying higher initial CO 2 pressures (e.g., 40–60 psi) and optimal relative humidity (~55 %) could significantly enhance the carbonation efficiency of RCA. Elevating temperature also led to accelerated CO 2 consumption, being more effective on the lab scale. The economic analysis presented potential cost benefits when substituting natural aggregates with CO 2 -treated RCA. All in all, these results suggest that the carbonation of RCA may provide significant environmental benefits through carbon sequestration, promoting sustainable construction practices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the “glue” that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5 T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Herein our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗