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This report summarizes radiological monitoring results from groundwater wells associated with the Idaho National Laboratory Site’s Advanced Test Reactor Complex Cold Waste Ponds Reuse Permit (I-161-02). All radiological monitoring is performed to fulfill Department of Energy requirements under the Atomic Energy Act.
This report examines the establishment and operation of Energy Performance Contracting (EPC) Centers of Expertise (COEs) within the federal government. EPCs, including Energy Savings Performance Contracts (ESPCs) and Utility Energy Service Contracts (UESCs), are critical mechanisms for advancing energy efficiency, resilience, and infrastructure modernization without the need for significant upfront appropriations. However, EPCs require specialized knowledge in project development, contracting, financing, legal parameters and technical project oversight. Currently federal agencies have varying levels of expertise and institutionalized policy to effectively and consistently use congressionally authorized EPCs which have decades of proven and impactful use. To address these challenges, several federal agencies have created COEs to centralize expertise, standardize practices, and streamline implementation. This report reviews statutory and policy drivers, highlights the benefits and challenges and presents case studies from the General Services Administration (GSA), the Department of Veterans Affairs (VA) and the U.S. Army Engineering and Support Center Huntsville (HNC). Recommendations are also provided for agencies considering the establishment of EPC COEs, which will bring much needed structure, consistency and lead to implementation of these energy and infrastructure building projects to save costs for U.S. taxpayers.
Deploying an ADMS or looking to optimize its value? NREL offers a low-cost, low-risk evaluation platform for assessing ADMS performance. The National Renewable Energy Laboratory (NREL) has developed a vendor-neutral advanced distribution management system (ADMS) evaluation platform and is expanding its capabilities. The platform uses actual grid-scale hardware, large-scale distribution system models, and advanced visualization to simulate realworld conditions for the most accurate ADMS evaluation and experimentation.
The Advanced Reactor Technologies (ART) Program at Idaho National Laboratory (INL) includes the Advanced Gas Reactor (AGR) fuel development and qualification program that consists of fuel fabrication, experiment irradiations, post-irradiation examination (PIE) and safety testing to assess tristructural isotropic (TRISO) fuel performance during normal irradiation and under potential accident conditions. Advanced microscopy work on selected AGR-1 and AGR-2 unirradiated and irradiated fuel specimens is performed as part of the PIE effort. PIE work on fuel from the first experiment irradiation, AGR-1, began at INL in April 2010, and AGR-2 PIE began at INL in July 2014. This work scope includes University of Florida (UF) performing advanced electron microscopy examination and analysis using facilities at the Center for Advanced Energy Studies (CAES), INL or UF’s electron microscopy facilities. Electron microscopic examination and analysis may include scanning transmission electron microscopy (STEM), transmission electron microscopy (TEM), selected area diffraction (SAD), electron energy loss spectroscopy (EELS), electron dispersive spectroscopy (EDS) and atom probe tomography (APT) on the fuel kernels of TRISO-coated fuel particles. The electron microscopy lamellae will be provided by INL and will be available at CAES or Irradiated Materials Characterization Laboratory (IMCL).
Colliders are built on a foundation of superconducting magnet technology that provides strong dipole magnets to maintain the beam orbit and strong focusing magnets to enable the extraordinary luminosity required to probe physics at the energy frontier. The dipole magnet strength plays a critical role in dictating the energy reach of a collider, and the superconducting magnets are arguably the dominant cost driver for future collider facilities. As the community considers opportunities to explore new energy frontiers, the importance of advanced magnet technology - both in terms of magnet performance and in the magnet technology's potential for cost reduction - is evident, as the technology status is essential for informed decisions on targets for physics reach and facility feasibility.
There is a need in many fields, such as nuclear medicine, non-proliferation, energy exploration, national security, homeland security, nuclear energy, etc, for miniature, thermal neutron detectors. Until recently, thermal neutron detection has required physically large devices to provide sufficient neutron interaction and transduction signal. Miniaturization would allow broader use in the fields just mentioned and open up other applications potentially. Recent research shows promise in creating smaller neutron detectors through the combination of high-neutron-cross-section converter materials and solid-state devices. Yet, till recently it is difficult to measure low neutron fluxes by solidstate means given the need for optimized converter materials (purity, chemical composition and thickness) and a lack of designs capable of efficient transduction of the neutron conversion products (x-rays, electrons, gamma rays). Gadolinium-based semiconductor heterojunctions have detected electrons produced by Gd-neutron reactions but only at high neutron fluxes. One of the main limitations to this type of approach is the use of thin converter layers and the inability to utilize all the conversion products. In this LDRD we have optimized the converter material thickness and chemical composition to improve capture of conversion electrons and have detected thermal neutrons with high fidelity at low flux. We are also examining different semiconductor materials and converter materials to attempt to capture a greater percentage of the conversion electrons, both low and higher energy varieties. We have studied detector size and bias scaling, and cross-sensitivity to xrays and shown that we can detect low fluxes of thermal neutrons in less than 30 minutes with high selectivity by our approach. We are currently studying improvements in performance with direct placement of the Gd converter on the detector. The advancement of sensitive, miniature neutron detectors will have benefits in energy production, nonproliferation and medicine.
Thermoelectric (TE) technology for use in automotive waste heat recovery is being advanced by General Motors with support from the US Department of Energy. Skutterudites are a very promising material for this application of TE technology due to their superior mechanical properties and good TE performance. Double-filled Yb{sub x}Ba{sub y}Co{sub 4}Sb{sub 12} with ZT values around 1.1 at 750 K are the best performing n-type skutterudites produced on a large scale using an economically viable approach of melt spinning (MS) in conjunction with spark plasma sintering (SPS). Another economical production method on the tons scale, the melt quench annealing (MQA) technique, has been recently claimed by Treibacher Industrie AG, further information is available [G. Rogl et al., Acta Mater. 76, 434–448 (2014)]. A possible hurdle to commercial implementation of these materials is the use of rare earths as the fillers to reduce thermal conductivity and improve the electrical transport properties. It will be shown herein that skutterudites double-filled with Ca and Ce, both of which are lower-cost fillers, display markedly different TE properties depending on whether they are produced by MQA or MS + SPS synthesis techniques. Ca and Ce double-filled skutterudites prepared by MS + SPS have TE properties that are superior to the same compositions prepared by MQA and that are comparable to the best performing Yb and Ba filled materials. Furthermore, the results of this study suggest that the unusually poor transport properties of MQA Ca-filled skutterudites can be ascribed to deleterious secondary phases, which is contrary to reports in the literature attempting to explain these irregularities via band structure features.
The High Efficiency and Resolution Microcalorimeter Spectrometer, 400 pixels (HERMES-400) is an ultra-high resolution gamma-ray spectrometer at Idaho National Laboratory’s Materials and Fuels Complex. HERMES-400 is intended to enable precise nondestructive analysis of advanced fuel cycle materials. Key upgrades and repairs of the instrument were performed this year to improve performance. First, the fourth and final microcalorimeter detector module was installed, meeting the milestone for its integration into the system. Operation with all four modules enables higher detector efficiency than any previous microcalorimeter gamma-ray system. Second, a thinner magnetic shield was installed. This shields the sensitive microcalorimeter pixels from stray magnetic fields while allowing transmission of lower-energy gamma rays, which are important to isotopic analysis. Finally, the cryostat, which provides cooling of the modules to their operating temperature, was found to have a helium coolant leak, which was repaired. HERMES-400 is in a stable state to perform repeated, long-term gamma-ray measurements with good detector efficiency and energy resolution. In FY25, irradiated TRISO fuel and salts from advanced electrochemical recycling techniques are candidates for microcalorimeter measurements. A final upgrade of the magnetic shielding and vibration damping of the system is also planned to maximize the system performance.
Thermoelectric materials, which can convert waste heat into electricity or act as solid‐state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high‐throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high‐throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed‐loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low‐cost, and energy‐efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high‐throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid‐state cooling. In the end, this article also discusses the future research prospects and directions.
The Magnetic Materials for Energy Applications IV, held at the 2014, 143rd Annual Meeting of The Minerals, Metals, and Materials Society (TMS), brought together experts, young investigators, and students from this sub-discipline of materials science in order for them to share their latest discoveries and develop collaborations. This symposium, which is organized by The Minerals, Metals, and Materials Society, is an important event for this community of scientists. This year, over 50 high-level technical talks were planned over the course of the event. In addition, the students and young investigators in attendance ensured the maximum benefit to the next generation’s work force in this area of study. Meeting global energy needs in a clean, efficient, secure and sustainable manner is arguably the greatest challenge facing mankind today. Magnetic materials play a critical role in myriad devices for the collection, conversion, transmission and storage of energy. For example, high performance permanent magnets are currently in great demand for the generators in wind turbines and electric motors in hybrid vehicles. Other classes of advanced magnetic materials are essential for efficient inductors/transformers and motors. Energy efficient cooling based on the magnetocaloric effect is another exciting possibility which is rapidly becoming industrially viable. The potential energy savings related to refrigeration and air conditioning based on magnetocaloric materials are highly attractive. This symposium allowed experts in areas relating to the advanced characterization, simulation, and optimization of magnetic materials to convene and present their latest research. The types of interactions afforded by this event are beneficial to society at large primarily because they provide opportunities for the leaders within this field to learn from one another and thus improve the quality and productivity of their investigations. Additionally, the presence of young investigators and students with technical interests in this field provides promise that the United States will continue to be a leader in this area. The support provided by the Department of Energy for this event directly enhanced its impact on the field by helping a number of students, young investigators, and technical experts attend and participate in this event. This symposium brought together research experts and students in the field to present the latest developments on the science surrounding advanced characterization, simulation, and optimization of magnetic materials and their use in energy applications such as generation and magnetic cooling. A strong technical program, containing more than 50 presentations was developed and organized around the following technical sessions. A strong list of invited speakers contributed to the high quality of technical content. Rare Earth Permanent Magnets: Processing, Characterization and Modeling Rare Earth Free Permanent Magnets Fundamentals of the Magnetocaloric Effect and Current Status of Magnetic Cooling Technology Magnetocaloric Materials High Performance Soft Magnets I High Performance Soft Magnets II
We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024
The focus of this interim fleet evaluation is to characterize and evaluate the operating behavior of Foothill Transit's fast charge battery electric buses (BEBs). Future research will compare the BEBs' performance to conventional vehicles. In an effort to better understand the impacts of drive cycle characteristics on advanced vehicle technologies, researchers at the National Renewable Energy Laboratory analyzed over 148,000 km of in-use operational data, including driving and charging events. This analysis provides an unbiased evaluation of advanced vehicle technologies in real-world operation demonstrating the importance of understanding the effects of road grade and heating, ventilating and air conditioning requirements when deploying electric vehicles. The results of this analysis show that the Proterra BE35 demonstrated an operating energy efficiency of 1.34 kWh/km over the data reporting period.
In support of the water conservation goals of the U.S. Department of Energy’s Strategic Sustainability Performance Plan, a number of water conservation projects have been proposed that would reduce the volume of wastewater currently discharged to the Advanced Test Reactor Complex’s Cold Waste Pond at the Idaho National Laboratory. This evaluation provides an approximation of the potential impact to groundwater quality from reducing raw or “clean” water discharges to the Cold Waste Pond and concentrating chemicals (e.g., sulfate) in the effluent.
In the push towards exascale computing, the limiting factors are power requirements and heat dissipation. Using current technology, the power required to drive an exascale supercomputer would near 400-MW. Clearly, future computing hardware will have to be more energy efficient in order to achieve exascale performance. Most of the current research in advanced computing architectures is in the use of standard CPUs with accelerators. However, these accelerators are not generally supported by the traditional scientific programming languages (C, C++, and Fortran) and the scientific software must be tailored specifically to take advantage of the accelerators. Large scale scientific software (hundreds-of-thousand to millions of lines of code) can not be easily ported to the computing platform du jour. The widespread use of low power ARM based processors in the mobile computing market offer a low power alternative to accelerators that are also supported by the traditional scientific programming languages and programming paradigms such as MPI and OpenMP. Computing architectures for scientific computing are often assessed using standard benchmarks such as LINPACK benchmark or using suites of mini-applications that are greatly simplified stand-ins for scientific software. Often the real applications that will deployed on the scientific supercomputers are not ported or tested until delivery of the machines. In order to evaluate the use of ARM processors for scientific computing the Monte Carlo particle transport code, MCNP version 6 was compiled on the ARM Cortex-A8 and a set of benchmark problems were used to assess performance in comparison to an Intel Xeon E5-2670 and the low power Intel Atom N270. MCNP is a continuous energy Monte Carlo particle transport code in development at Los Alamos National Laboratory since 1977. MCNP is used to transport neutrons, photon, electrons, and charged particles using continuous energy interaction data. It is commonly used in fields of nuclear power, nuclear medicine, radiation protection, and high-energy physics through-out the world. MCNP version 6 is approximately 450 thousand lines of code, written primary in Fortran90, with some C and C++. MCNP supports distributed multiprocessing using MPI and threading with OpenMP. The ARM Cortex-A8 executes MCNP6 using about twice as much power as the Intel Xeon E5-2670 and about the same amount of power as the Intel Atom N270. However, these results are encouraging for the use of low-power ARM chips in future supercomputers. The ARM Cortex-A8 is a 32-bit CPU and it's NEON general-purpose SIMD (single-instruction multiple data) engine does not support double-precision floating-point arithmetic. More recent ARM processors (ARM Cortex-A53 and Cortex-A57) support 64-bit addressing and have NEON co-processors that support double-precision floating-point arithmetic. ARM claims that the A50 series delivers a performance increase of 15 times the Cortex-A8 performance for similar power usage. (authors)
As technology advances, progressive building performance goals are met with ease and occupant behavior plays an increasingly significant role in preventing building operators from achieving those goals. However, removing occupants from participating in building control and operation is counterintuitive to the purpose of building operation. Occupant-centric control (OCC) has been suggested as a means of incorporating the occupant while simultaneously reducing the negative impact their behavior can have on building performance. As a result, operators are now tasked with incorporating OCC into their daily operation. The relationship between building occupants and operators is a critical component to OCC, and balancing occupant comfort and building performance. In this paper, we present findings from an international qualitative study of building operators with a focus on the operator and occupant relationship. This paper identifies the role operators play in OCC, how these relationships develop, and how these relationships impact building operation through two key research questions: What factors influence the quality of relationships between occupants and operators? How can the relationships between operators and occupants be improved? Subsequently, these questions revealed this relationship becomes strained when building performance is prioritized over comfort, occupants are ignored or uneducated, and feedback is negative or sparse. In short: there is a disconnection between operators and occupants. Here we propose several solutions including modifying job requirements to prioritize occupant comfort, educating occupants to make autonomous decisions that are not detrimental to operation, and creating effective communication channels for instances where operator intervention is required.
Hybrid energy system research has the potential to expand the application for nuclear reactor technology beyond electricity. The purpose of this research is to reduce both technical and economic risks associated with energy systems of the future. Nuclear hybrid energy systems (NHES) mitigate the variability of renewable energy sources, provide opportunities to produce revenue from different product streams, and avoid capital inefficiencies by matching electrical output to demand by using excess generation capacity for other purposes when it is available. An essential step in the commercialization and deployment of this advanced technology is scaled testing to demonstrate integrated dynamic performance of advanced systems and components when risks cannot be mitigated adequately by analysis or simulation. Further testing in a prototypical environment is needed for validation and higher confidence. This research supports the development of advanced nuclear reactor technology and NHES, and their adaptation to commercial industrial applications that will potentially advance U.S. energy security, economy, and reliability and further reduce carbon emissions. Experimental infrastructure development for testing and feasibility studies of coupled systems can similarly support other projects having similar developmental needs and can generate data required for validation of models in thermal energy storage and transport, energy, and conversion process development. Experiments performed in the Systems Integration Laboratory will acquire performance data, identify scalability issues, and quantify technology gaps and needs for various hybrid or other energy systems. This report discusses detailed scaling (component and integrated system) and heat transfer figures of merit that will establish the experimental infrastructure for component, subsystem, and integrated system testing to advance the technology readiness of components and systems to the level required for commercial application and demonstration under NHES.
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Idaho National Laboratory (INL) is the national and global leader of the Department of Energy (DOE) ’s nuclear energy research, development, and demonstration programs. Developing critical design criteria of new advanced reactor systems, components and materials requires an understanding of fabrication and the irradiation environment during normal operating and accident conditions. Next generation researchers and designers, are, therefore challenged not only by demands for ever improved performance but must also work to shorten the development and commercialization lifecycle for new nuclear reactors and systems to remain competitive. This provides unique and exciting opportunities for all contributors to this field of study. This presentation will offer a strategic overview of the competitively funded R&D awards by the Advanced Methods for Manufacturing (AMM) program of the DOE’s office of Nuclear Energy (NE). This program conducts research and development (R&D) to accelerate innovations by reducing the cost and schedule of constructing new nuclear plants and to make fabrication of nuclear power plant components faster, cheaper, and more reliable. By evaluating state-of-the-art practices found in other large manufacturing industries, the nuclear community has identified six major areas of innovation to reach the objectives of this DOE-NE program. Additionally, typical research case studies performed at INL, will be presented, showing the vast variety of opportunities for which, the next generation of researchers can seek solutions.