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All sources NASA NTRS DOE OSTI

DOE OSTI2026

mada-tools: MCP servers, configurations, skills, and examples for MADA

MADA-tools (Multi-Agent Design Assistant tools) is a library for defining MCP (Model Context Protocol) servers that can be used by AI agents in the MADA project. Each MCP server provides a focused set of tools that enhances an LLM's knowledge and capabilities for a specific domain, for example, how to launch jobs with Flux versus Slurm. The library makes it easy to configure and start multiple MCP servers using configuration files or command line options. Once running, these servers are intended to be consumed by one or more agents in the MADA ecosystem. The system is designed to be extensible so that future projects can contribute their own MCP servers, skills, and toolsets.

Gunnarson, BrianS [Lawrence Livermore National Lab↗

DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗

DOE OSTI2026

Embedded EPICS server for PowerPMAC motion controllers

An embedded server layer of Experimental Physics and Industrial Control System (EPICS) for PowerPMAC motion controllers has been developed and deployed at two undulator beamlines of the National Institute of General Medical Sciences and the National Cancer Institute (GM/CA) Structural Biology Facility at the Advanced Photon Source (APS). This compact, open source solution makes the power and versatility of PowerPMAC motion controls directly accessible to distributed EPICS clients. At GM/CA the system controls about 200 servo and stepper motors — both encoded and unencoded — and multiple digital and analog I/O accessories. The server stack comprises two sublayers: a lower-level driver and database that communicates directly with PowerPMAC, and a facility-specific soft sublayer built on top. The paper describes installing EPICS on PowerPMAC, the implementation of both layers and client examples, including on-the-fly scanning.

EPICS↗

DOE OSTI2025

EnergyPlus Model Context Protocol Server (EnergyPlus-MCP) v0.1

EnergyPlus-MCP is the first open-source Model Context Protocol server specifically designed for EnergyPlus building energy simulation. This innovative software enables AI assistants and other applications to interact programmatically with EnergyPlus through a standardized, secure interface, eliminating traditional technical barriers in building energy modeling. The software provides specialized tools across five functional domains: server management, model configuration and loading, comprehensive building component inspection, systematic model modification, and simulation execution with results visualization. Key features include automated HVAC system discovery and topology mapping, advanced schedule analysis, intelligent model validation, and interactive visualization capabilities. EnergyPlus-MCP's layered architecture ensures robust separation between protocol communication and domain expertise, enabling scalable deployment across organizations, educational institutions, and research teams. Unlike direct LLM approaches that suffer from inconsistent results and security gaps, EnergyPlus-MCP provides validated, reliable interactions while maintaining scientific rigor. This democratizes sophisticated building energy analysis, making EnergyPlus accessible to broader audiences through conversational interfaces and streamlined workflows.

Li, Han [Lawrence Berkeley National Laboratory (LB↗

DOE OSTI2026

DUNE Rucio Server Scalabiilty Studies

The DUNE collaboration has an ongoing production effort to simulate the full detectors and to analyze the various prototypes that are currently running. Rucio is used to manage the 40PB of files made to date. When 500 or more jobs were sending output to Rucio simultaneously via Rucio upload, we observed timeouts, unhandled exceptions, and Rucio server restarts due to slow performance. In collaboration with the core Rucio team we did a full review of the Rucio upload code and identified several optimizations that can be made. We also have deployed the Ingress load balancer in front of our Rucio servers and added a database connection pooling utility. These changes led to significant improvement both in reliability and scalability, yet we anticipate even better performance will eventually be required. We describe in this paper the initial state of the system, the various debugging processes that were used, and our plans to further improve scalability.

Calcutt, J. [Brookhaven Natl. Lab.]↗

DOE OSTI2025

MOOSE Web Server Interface: A Message-based External Interface for Multiphysics Simulations

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a C++ toolkit designed to streamline the development of finite element and finite volume applications. It offers an interface for input-based coupling of these applications to create multiscale, multiphysics models. We introduce a new capability that enables external applications to integrate with MOOSE-based applications in situ via a web server using HTTP requests. An example of this integration is provided, where a MOOSE thermal-fluids solve has a boundary condition that is driven by an external Python application. Additionally, the coupling of the Python-based OpenMC depletion solver with the Cardinal application is demonstrated. A multiphysics model of a pressurized water reactor, incorporating neutronics, heat conduction, thermal-fluids, and depletion, is presented to showcase this new Cardinal capability that is enabled by the MOOSE web server capability.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

MOOSE Web Server Interface: A Message-based External Interface for Multiphysics Simulations

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

mDNS to support local price server discovery with OpenADR 3 (mDNS for OpenADR 3) v1.0

This software contains a template VEN with local VTN service discovery over mDNS. It provides common starter code for an OpenADR3.0 VEN that advertises itself over mDNS, conducts local VTN service discovery over mDNS, connects to the VTN over HTTP(S), and regularly polls and acts on energy prices and events hosted on the VTN. The software is written to be easily modified to accommodate different VEN appliances, VEN-VTN networking protocols, user interfaces, and default responses, given the wide range of possible use cases for local price server discovery. OpenADR3.0 is an open communications standard from the OpenADR Alliance that is designed to provide two-way information exchange regarding e.g., dynamic price and event signals to utility applications, so that customers can modify their energy usage to save money and reduce their carbon footprint.

Nordman, Bruce [Lawrence Berkeley National Laborat↗

DOE OSTI2025

A Taxonomy and Feature set for Server-Side Identification of Proxies

Malicious actors frequently use proxies and VPNs to evade detection and hide their origin. Current challenges to information security include the use of residential proxies to blend in with normal traffic and Man-in-the-Middle phishing proxies that are used to compromise accounts protected with mult-factor authentication. We advance a taxonomy and feature set for the identification of proxied traffic based on the network layer where proxying occurs. We describe how these features apply to common proxy types and how to use these features in the classification of the proxied traffic. Collection of these additional features is feasible using existing network sensors and web servers, while only adding about 30% volume to commonly deployed network sensor logs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The water use of data center workloads: A review and assessment of key determinants

The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.

Data centers↗

DOE OSTI2025

Shedding light on U.S. small and midsize data centers: Exploring insights from the CBECS survey

As demand for digital services accelerates, the energy and environmental footprint of data centers faces increasing scrutiny. While hyperscale cloud facilities have driven efficiency gains, small and midsize U.S. data centers remain a critical yet underexamined segment with significant untapped potential for energy savings. This study leverages data from the Commercial Buildings Energy Consumption Survey (CBECS) to analyze trends in server stocks, computing customers, cooling system adoption and efficiency, and geospatial distribution from 2012 to 2018. Findings reveal a sharp decline in small and midsize data centers, from 1.764 million to 1.398 million, with server counts dropping from 5.177 million to 4.262 million—aligning with the broader shift toward cloud computing. More than 40 % of servers in small data centers and 55 % in midsize data centers are housed in office buildings, and over half of all servers are concentrated in climate zones 5A (cold), 3A (mixed-humid), and 4A (mixed-humid), with the highest densities in metropolitan hubs. While direct expansion units remain the dominant cooling system, a clear transition toward more energy-efficient solutions, particularly air economizers, is evident. By integrating server and cooling system distributions, we estimate Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) for U.S. data centers by size and year. Results show that midsize data centers are more energy-efficient but more water-intensive due to the widespread use of water-cooled chillers. These findings highlight the trade-offs in cooling system selection and provide a critical foundation for policies aimed at enhancing efficiency in an evolving data center landscape.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Energy–Performance Trade-offs in Privacy-Preserving Federated Learning on SmartNIC-Enabled HPC Systems

Federated learning (FL) is increasingly deployed on accelerator-rich high-performance computing (HPC) systems, yet the system-level energy cost of privacy-aware FL remains poorly understood, particularly across heterogeneous networking and server-placement options. We present a measurement-driven study of energy–performance trade-offs for FL on GH200-class nodes across three deployment configurations: CPU-Ethernet, CPU-InfiniBand (RDMA-capable), and a DPU-hosted FL server over InfiniBand using a BlueField-3 SmartNIC/DPU. Using NVIDIA FLARE (NVFLARE), we align node-level power telemetry with per-round timing extracted from NVFLARE logs to quantify time-to-solution (TTS), energy-to-solution (ETS), energy-delay product (EDP), and synchronization behavior for three transformer models (ALBERT, DistilBERT, BERT), trained with and without differential privacy (DP). We find that interconnect choice is the dominant driver of runtime and energy: host-managed InfiniBand consistently reduces communication overhead versus Ethernet, yielding lower TTS/ETS/EDP. In contrast, in our NVFLARE deployment, placing the FL server on the DPU does not consistently match CPU-InfiniBand performance and can be slower—especially for larger models—highlighting that server placement alone is not sufficient to guarantee end-to-end gains. Finally, under our fixed-round protocol, DP increases per-round cost and runtime variance; ETS increases largely in proportion to TTS because average node power remains relatively stable across configurations.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

DOE OSTI2025

CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training

Federated finetuning is crucial for unlocking the knowledge embedded in pretrained Large Language Models (LLMs) when data are geographically distributed across clients. Unlike finetuning with data from a single institution, federated finetuning allows collaboration across multiple institutions, enabling the utilization of diverse and decentralized datasets while preserving data privacy. Given the high computing costs of LLM training and the emphasis on energy efficiency in Federated Learning (FL), Low-Rank Adaptation (LoRA) has emerged as a widely adopted algorithm due to its significantly reduced number of trainable parameters. However, this assumes that all data silos have the necessary computing resources to compute local updates of LLMs. Nevertheless, in practice, the computing resources across clients are highly heterogeneous: while some may have access to hundreds of GPUs, others might have limited or no GPU access. Recently, federated finetuning using synthetic data has been proposed, allowing clients to participate in a collaborative training run without training LLMs locally. However, our experimental results reveal a performance gap between models trained using synthetic data and those trained using local updates. Motivated by the observed heterogeneity in computing resources and the performance gap, we propose a novel two-stage algorithm that leverages the storage and computing capabilities of a strong server. In the first stage, under the coordination of the strong server, clients with limited computing resources collaborate to generate synthetic data, which is transferred to and stored on the strong server. In the second stage, the strong server uses this synthetic data on behalf of the resource-constrained clients to perform federated LoRA finetuning alongside clients with sufficient computing resources. This approach ensures that all clients can participate in the finetuning process. Experimental results demonstrate that incorporating local updates from even a small fraction of clients improves performance compared to using synthetic data for all clients. Furthermore, we incorporate the Gaussian mechanism in both stages to guarantee client-level differential privacy.

Wang, Jiayi [ORNL]↗

DOE OSTI2026

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗

DOE OSTI2025

CMS Token Transition

Within the LHC community, a momentous transition has been occurring in authorization. For nearly 20 years, services within the Worldwide LHC Computing Grid (WLCG) have authorized based on mapping an identity, derived from an X.509 credential, or a group/role, derived from a VOMS extension issued by the experiment. A fundamental shift is occurring to capabilities: the credential, a bearer token, asserts the authorizations of the bearer, not the identity. By the HL-LHC era, the CMS experiment plans for the transition to tokens, based on the WLCG Common JSON Web Token profile, to be complete. Services in the technology architecture include the INDIGO Identity and Access Management server to issue tokens; a HashiCorp Vault server to store and refresh access tokens for users and jobs; a managed token bastion server to push credentials to the HTCondor CredMon service; and HTCondor to maintain valid tokens in long-running batch jobs. We will describe the transition plans of the experiment, current status, configuration of the central authorization server, lessons learned in commissioning token-based access with sites, and operational experience using tokens for both job submissions and file transfers.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets () according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ -aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by -aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a Privacy Inference Attack that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL -aware aggregation. To address this challenge, we propose IntraShuffler, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving -aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.

Riya, Farhin Farhad [ORNL]↗

DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗

DOE OSTI2025

Demonstration of a Novel Technology to Manage Electricity Demand in Grid-Independent Military Microgrids

This research was conducted by the National Renewable Energy Laboratory (NREL) in collaboration with the S&C Electric Inc. through funding provided by the ESTCP. The project demonstrates use of cybersecure Automated Demand Response (ADR) technology to effectively manage microgrid loads during grid-independent, also known as "islanded," operation. When military microgrids become isolated from the main electrical grid, they are required to balance electricity supply and demand locally. Given that local generation may be constrained, the prevailing strategy involves shedding all but the most critical loads by tripping smart circuit breakers, which then necessitate manual resetting. This approach is generally implemented at the building level, which means that the buildings with mission-critical activities are exempt from load management and remain fully powered, whereas those deemed non-critical can experience a complete loss of service. In this research we developed a method that allows building automation systems to selectively control their assets in response to load shedding request from a microgrid controller, avoiding total loss of service in contrast to the conventional control approach. A commercial OpenADR client server by GridFabric is used for communication between the microgrid controller and the building management system (BMS). The microgrid controller monitors both generation capacity and various assets within the microgrid and issues a demand reduction request when necessary. This request is communicated to the OpenADR server via Modbus. Upon receiving the request, the OpenADR server forwards it to the BMS utilizing the OpenADR protocol. The BMS is pre-configured with various levels of load reduction strategies based on the controllable assets available, allowing for a nuanced approach to demand reduction. Both lab and field tests were performed that considered load shedding needed to achieve closed transition into island mode and to accommodate changing loads and power source availability while islanded. A commercial microgrid controller was used for these tests with normal programming within the expected constraints of the system capabilities. That is, the solution did not require any specialized modification to the code base of the controller. Given the latency of the round-trip communication path between the microgrid controller and the various devices involved with the load shed processes, there are certain scenarios for which the demonstrated solution are appropriate and some which are not. The methods described in this report can be used for load shedding/restoration during transitions between islanded and grid-tied modes of operation, as well as accommodating normal variations in load and the need to remove a power source from operation for maintenance. These methods should not be used for scenarios that require load shedding within a second or two such as sudden and unanticipated significant load increases or loss of power sources through equipment faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗