Next Generation Energy Training
Report of Activities developing and deploying training designed for the next generation work force for energy efficient and high performance construction.
30 DIRECT ENERGY CONVERSION↗
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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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Maximized Information Gain of Next Generation Pulsed Power Using Optimized Design of Z-Machine Experiments
This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.
97 MATHEMATICS AND COMPUTING↗
A Multi-Model, Multi-Scale Research Program in Stressors, Responses, and Coupled Systems Dynamics at the Energy-Water-Land Nexus and for Concentrated, Interdependent Infrastructures: Toward Next Generation Capabilities in Integrated Impacts, Adaptation, and Vulnerability (I-IAV) Modeling and a Community of Practice
The goal of this research program was to build a next generation integrated suite of science-driven modeling and analytic capabilities, and a more expanded and connected community of practice, for analyses of the stressors, impacts, adaptations and vulnerabilities of global and regional change. The emphasis was on understanding energy-water-land interactions and feedbacks and interdependent infrastructures at appropriate regional and temporal scales. Although the scope spans many complex facets of data, modeling, and analysis, as well as scales appropriate for integrated impacts and adaptation research, the focus of this effort was the development of multi-model, multi-scale capabilities spanning the domains of Multi-Sector Dynamics (MSD) models; Impact, Adaptation, and Vulnerability (IAV) models; and Earth System Models (ESMs).
54 ENVIRONMENTAL SCIENCES↗
High performance heat exchangers for next generation hybrid cooler (CRADA NFE-19-07891 Final Report)
The evaporative cooling process has been successfully deployed in multiple energy conversion processes such as power generation, process cooling, heating, ventilation & cooling (HVAC), and commercial and industrial refrigeration. The heat exchanger is a very common yet extremely critical component of such systems. The device is important since the performance of the overall cooling system heavily relies on the thermal-hydraulic efficiency of the heat exchanger. The possible wet operation as evaporative cooling heat exchanger complicates the issue. Indeed, based on the hybrid nature of operation (fully dry, partially wet, fully wet) the efficiency can vary significantly and can cause performance variation for the whole system. Understanding the importance of the issue, Oak Ridge National Laboratory (Contractor) and Baltimore Aircoil Company (Participant) propose an activity focused on the analysis of the existing heat exchanger technology used in air and evaporative cooling processes and design, development and demonstration of a novel heat exchanger design based on porous materials. The potential candidates include metallic wire meshes and metal foams carefully designed for the proposed application. Prior research has indicated that these materials have great potential for deployment in thermal systems, particularly heat sinks and heat exchangers. It is expected that the successful completion of the project will lead to novel heat exchanger technology for hybrid systems (capable of dry and wet operation) by exploring newly emerging materials and their potential for heat transfer application. The overall goal is to develop a next generation heat exchanger technology which can be deployed in evaporative cooling systems. The impact of the such development is significant since, it will not only make the overall system highly efficient but will also, reduce the total refrigerant charge in the heat exchanger, a critical aspect for the deployment of refrigerants with lower Global Warming Potential (GWP).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Development of Engine Valve Materials for Next Generation Higher Efficiency Engines
The growing demand to increase the performance and efficiency of light-, medium-, and heavy-duty engines continues to drive increases in combustion intensities and cylinder pressures, which result in higher exhaust gas temperatures. Thus, there is a critical need for new materials that can meet the performance and cost targets for components such as exhaust valves which are exposed to these higher exhaust gas temperatures. Oak Ridge National Laboratory (ORNL) has developed several lower-cost, high-strength alloys that have the potential to be adopted into intake and/or exhaust valves in the next generation, high-efficiency engines and other high temperature applications. These alloys are covered by two issued patents.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)
Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.
36 MATERIALS SCIENCE↗
Next-Generation Parametrically Induced QUantum Engineering (PIQUE 2.0) (Final Technical Report)
This final technical report summarizes the key accomplishments on the project Next-Generation Parametrically-Induced QUantum Engineering (PIQUE) , a BES-funded award received by PI Archana Kamal at University of Massachusetts Lowell. The focus of this award was to enable new quantum information processing functionalities for multi-qubit entanglement and control, using parametric interactions in superconducting quantum circuits.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Next Generation Heat Transfer Fluids for Two-Phase Immersion Cooling of Data Centers
The purpose of this study is to evaluate the performance of next generation dielectric fluids in a Two-Phase Immersion Cooling (2PIC) system, which was designed for use in data centers. Hence, this report contains the performance evaluations of a new developmental dielectric fluid, Opteon™ 2P50, in a commercially available small-scale 2PIC system under typical and off-design range of operating conditions. Accordingly, ambient temperature and thermal loads were varied to simulate different ambient conditions. Additionally, this research report describes the development of a semi-empirical lumped model to predict the energy efficiency of the 2PIC system using Opteon™ 2P50 across a wide range of conditions. The model aims to offer a comprehensive understanding of the system’s efficiency and potential improvements. The outcomes of this study are expected to contribute to the adoption of sustainable 2PIC cooling technologies in data centers.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Project "Right Way" Next Generation Melt Project: Phase 1 Performance Report Narrative (PRN)
In November 2024, AMERICAN entered into a Cooperative Agreement with the Office of Clean Energy Demonstrations (OCED) regarding the Industrial Decarbonization and Emissions Reduction Demonstration-To-Deployment Funding Opportunity for the “Right Way” Next Generation Melt Project (“Project”). This report encompasses initial planning and analysis activities to ensure that the project is viable.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Tailoring composition and deformation modes at the microstructural level for next generation low-cost high-strength austenitic stainless steels
The objective of this project is to enable deliberate development of cost-effective, hydrogen resistant alloys by establishing detailed relationships specific to the effects of alloy composition, short-range order (SRO), and microsegregation in the presence of hydrogen on the transition between homogeneous deformation and localized plasticity in shear bands. In collaboration with the International Institute for Carbon-Neutral Energy Research, I2CNER, at Kyushu University in Japan, we conceptualized, designed, and manufactured four austenitic alloys that maintain corrosion resistance and ensure lower cost relative to baseline commercial alloys. The mechanical properties and deformation modes of the novel alloys (KU alloys) were assessed in the presence of hydrogen (H). Correlations between composition and performance revealed that two of the KU alloys are suitable replacements for 316 steel, while another is a viable replacement for 304 steel at room temperature. We found that, in the presence of other austenite stabilizing elements namely Mn and N, replacing Ni with Cu does not lead to martensite formation as has been previously reported.1–3 Furthermore, we found that the addition of Cu leads to an earlier onset of multiple slip resulting in an relative earlier onset of a higher work hardening rate (WHR). Greater understanding of the relationships between alloy composition and SRO required the development of a novel advanced electron diffraction methodology to characterize SRO in complex FCC alloys. This innovative approach, which combines fluctuation and correlation analyses of diffuse-scattering signals, successfully differentiated between SRO and long-range ordering (LRO). Further investigations into annealed austenitic stainless steels could provide insights into manipulating SRO and its effects on material properties. Atomistic simulations provided understanding of SRO behavior that was difficult to capture experimentally. This project created the first spin cluster expansion model that is able to capture and describe SRO effects in Fe-Ni-Cr FCC alloys, accounting for the non-negligible effects of magnetism. An automated computational workflow was established to provide reliable predictions of SRO in Fe-Ni-Cr austenitic alloys, both with and without the presence of H atoms. Analysis of the propensity for SRO in Fe-Ni-Cr alloys revealed that H tends to cluster with specific, well-defined SRO domains. The computational framework is general purpose and can be extended to realistic stainless steels across diverse composition ranges. With confidence that SRO is possible in austenitic stainless steels, we developed a discrete dislocation finite element code to understand the interaction of dislocations with SRO in the presence of H. By incorporating H effects on the dislocation emission and SRO stress field we show that the critical stress for the dislocation pileup to breakthrough the SRO domain decreases in the presence of H, which directly contributes localized deformation at the macroscale. Through the simulation of a uniaxial tension test, we demonstrated that H-induced weakening of SRO stress field and H-enhanced dislocation emission can lead to the onset of shear localization at lower macroscopic strains. As a whole, this project identified three novel alloys that show improvements in performance and cost efficiency for H-facing applications by studying correlations between alloy chemistry and deformation behavior. We also made significant advancements to experimental and computational methodologies necessary to study the chemistry and distribution of SRO across a range of alloys, which in turn allowed us to demonstrate how deformation mechanisms change due to the contributions of SRO in austenitic alloys in the presence of H. The combined advancements in fundamental understanding with novel alloy development in this project has increased the viability of next generation H-technologies for the broader public through accessible low-cost alloys and accelerated development towards future H-infrastructure.
08 HYDROGEN↗
Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows
We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.
97 MATHEMATICS AND COMPUTING↗
Evaluating pulse-shaping capabilities of next-generation pulsed power architectures
This project evaluated the pulse shaping capabilities of next-generation pulsed power (NGPP) architectures. NGPP architectures share several common attributes including multiple independent pulse-generation lines, a radial water-insulated impedance transformer, and a central vacuum insulated load region. A multi-module circuit model was developed, incorporating independent pulse-generation lines and a 2-D transmission line mesh of the radial impedance transformer to assess the effects of azimuthal asymmetry in pulse-shaped experiments. Circuit model simulations demonstrated that NGPP architectures are able to produce the the desired current pulse shapes for exemplar NGPP experiments. Additionally, the project explored automated methods for experiment design, including derivative -ree optimization and machine learning. Pulse-shaped experiments require designers to determine machine parameters that reliably produce the desired current pulse at the load, a process that typically relies on expert knowledge and iterative adjustments using the Z circuit model. Given the increased complexity of NGPP systems, this manual approach may be impractical. While the evaluated methods do not eliminate the need for manual iteration, they can reduce the time required for experiment design. Derivative-free optimization automates much of the trial-and-error process, providing a close starting point for manual adjustments or making small modifications to near-final designs. Meanwhile, deep neural network methods can generate a good qualitative match to the desired current pulse in under one second without requiring circuit model simulations.
42 ENGINEERING↗
Evaluating Variable-Impedance Magnetically-Insulated Transmission Lines as a Risk-Mitigation Measure for Next-Generation Pulsed Power
This project has produced the first detailed characterizations of power flow resulting from applying the “variable-impedance MITL” concept to real-life systems in Sandia’s pulsed power program (Z and next-generation pulsed power (NGPP)). We present simulation results and analyses for constant-impedance versions of both Z and NGPP and survey the operational viability of several variable-impedance re-designs in the parameter space of linear tapers. Circuit modeling (SCREAMER/Bertha) was used to pinpoint promising candidate designs, and EM-PIC (Empire) simulations were used to evaluate these candidates more rigorously. This approach was particularly successful in the Z regime which resulted in the identification of several viable variable-impedance MITL designs for each level. The approach was more challenged in the operating space NGPP occupies, producing data points that speak to a more restrictive design space due to anode plasma turn-on. In the end, we were able to converge on one viable variable-impedance design for the highest inductance line (level “F”) and one for the highest current line (level “A”). Altogether, the body of simulation evidence presented in this report suggest there does exist flexibility in operating space for magnetically-insulated transmission lines (MITLs) having variable geometric impedance to be a potential enabling technology for safely increasing current delivery (and potentially lowering stack voltage) in pulsed-power drivers by manipulating electron losses; however, operating points for a particular design must be carefully screened. Circuit and EM-PIC modeling provided consistent verdicts in safe operating regimes for operational viability, but additional physics such as anode plasma turn-on which is included in Empire but not in SCREAMER/Bertha was found to be a critical factor affecting power flow that lead to different assessments between the codes. It is not always the case that the occurrence of anode plasma caused a design to fail (some designs turned on anode plasma yet still delivered load currents meeting design targets); the details matter such as how early in the pulse anode surfaces break down (and how large a region). However, in every case that it did fail it was found that the feedback from anode plasma was the cause (i.e., turning off the anode plasma model in Empire restored agreement with the circuit model prediction). As circuit simulations represent an efficient and practical means of surveying design space compared to more computationally-expensive approaches such as EM-PIC, it could be prudent to invest in the research and development of models to include the effects of anode plasma such as ion emission in circuit codes. The variable-impedance MITL design is a new concept that enables controlled manipulation of the initial electron losses in the outer MITL and can be tested on Z today. We encourage follow-on work to explore further optimization (including alternative variable-impedance profiles, e.g., having constant dZ/dR), and to confirm the major findings presented in this report by fielding test hardware on actual Z shots.
24 POWER TRANSMISSION AND DISTRIBUTION↗
High performance heat exchangers for next generation hybrid cooler (CRADA Final Report)
The evaporative cooling process has been successfully deployed in multiple energy conversion processes such as power generation, process cooling, heating, ventilation & cooling (HVAC), and commercial and industrial refrigeration. The heat exchanger is a very common yet extremely critical component of such systems. The device is important since the performance of the overall cooling system heavily relies on the thermal-hydraulic efficiency of the heat exchanger. The possible wet operation as evaporative cooling heat exchanger complicates the issue. Indeed, based on the hybrid nature of operation (fully dry, partially wet, fully wet) the efficiency can vary significantly and can cause performance variation for the whole system. Understanding the importance of the issue, Oak Ridge National Laboratory (Contractor) and Baltimore Aircoil Company (Participant) propose an activity focused on the analysis of the existing heat exchanger technology used in air and evaporative cooling processes and design, development and demonstration of a novel heat exchanger design based on porous materials. The potential candidates include metallic wire meshes and metal foams carefully designed for the proposed application. Prior research has indicated that these materials have great potential for deployment in thermal systems, particularly heat sinks and heat exchangers. It is expected that the successful completion of the project will lead to novel heat exchanger technology for hybrid systems (capable of dry and wet operation) by exploring newly emerging materials and their potential for heat transfer application. The overall goal is to develop a next generation heat exchanger technology which can be deployed in evaporative cooling systems. The impact of the such development is significant since, it will not only make the overall system highly efficient but will also, reduce the total refrigerant charge in the heat exchanger, a critical aspect for the deployment of refrigerants with lower Global Warming Potential (GWP).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A method for modeling battery-temperature-aware EV power profiles utilizing Next-Gen Profile data
With the expected increase in the number of electric vehicles (EVs) on the road in the coming years, it is important that analysis tools are capable of modeling and predicting the expected load on the power grid due to both individual EV charging sessions as well as large populations of vehicles. To do this accurately, the power profile of an EV charge session must be accurately modeled, including for scenarios where the temperature is above or below the ideal, and also take into account the nuances of manufacturer charging preferences. This paper introduces a method that utilizes the data in the Next-Gen Profile (NGP) data collection project to build a model of EV charging that takes into account the variations in charging power that occur due to off-nominal battery temperature and manufacturer preferences that limit power due to cold temperatures or high battery state-of-charge.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
EV Profile Capture 2025: Next-Gen Profiles Project Report
As part of the Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high-power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of starting battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), EVSE limitations and charging adapter usage. Nominal conditions are defined as ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against their nominal condition profile counterparts to highlight the variance across less desirable starting conditions within a single EV.
33 ADVANCED PROPULSION SYSTEMS↗
Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials
Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.
36 MATERIALS SCIENCE↗
Next-Generation Electric Motors and Electromagnetic Devices (CRADA Final Report)
As part of the Cyclotron Road program, the Foli Research team investigated electric motors and other electromagnetic devices additively manufactured by depositing fine conducting wire onto a substrate. By leveraging modern motion control and efficient upstream manufacturing processes, the team explored using geometric control and turnkey production comparable to printed circuit board manufacturing, aiming at ten-fold improvements in signal density, while eliminating the material use, cost, and complexity overheads of subtractive processes at large scales. Owing to the achievable density and additive scaling, disparate components became monolithic, and disparate processes were combined with the aim of reducing development costs, uncertainties, and timelines. When applied to electric motors, this technology may enable agile manufacturing of next-generation designs, with dramatic increases in achievable power density and material utilization. Electric motors represent the single largest user of electricity in the U.S. and a critical component of decarbonized infrastructure. Despite this significance, the manufacturing of motors and other electromagnetic devices remains capital-intensive, labor-intensive, and performance-limiting. Advanced manufacturing processes were required to produce high-performance, low-cost, application-specific electric motors and electromagnetic devices to support an electrified future.
42 ENGINEERING↗