Results for “Analytical Model” — Page 43

DOE OSTI2024

A Self Consistent 2D Simulation of Coherent Synchrotron Radiation Effects on Beam Dynamics

An increasing interest in high quality and high current electron beams necessitates a thorough understanding and prediction of coherent synchrotron radiation effects. The self-interaction of charged particles in a beam undergoing synchrotron motion is a physically significant process that is all too often computationally intensive with very little analytical results to rely on for the general case. The coherent spectrum of this interaction is of utmost importance to the design of free electron lasers (FELs) and an accurate assessment is imperative for their design. This work presents a novel implementation to the numerical simulation of charged particle beams. The simulation is a self-consistent approach including the self-fields generated by the beam of which coherent synchrotron radiation effects are of primary interest. A particle-in-cell model is used where a planar beam sampled by point particles is deposited on an encompassing grid at each timestep. The electromagnetic fields are calculated on the grid using the retarded potentials according to causality. The electromagnetic forces from the fields are interpolated on each particle which in turn advance in time. The simulation is benchmarked against well-established results for coherent synchrotron radiation effects. In addition, studies are provided that show the convergence of simulation results for increasing resolution. A study into the transverse beam size effects on beam dynamics is performed as well as a proof of concept where the simulation is used by a genetic algorithm to optimize the design parameters of a beam lattice. The results of these studies in tandem verify the efficacy of the simulation for its practical use in accelerator design or the study of synchrotron radiation effects

Duffin, Dallan [Old Dominion Univ., Norfolk, VA (U↗

DOE OSTI2025

Mechanistic and Mitigation-Strategy Insights into NaCl and CaCl 2 Contamination of Proton-Exchange-Membrane Water Electrolysis Using Continuum Modeling

Cationic contaminants are detrimental to proton-exchange-membrane water electrolyzers (PEMWEs). To obtain insight, a 1-D, nonisothermal, multiphase continuum cell model including cationic contamination is developed. Simulations of steady-state cell performance predict decreased performance due to an increase in kinetic overpotential associated with the hydrogen-evolution reaction, which was attributed to decreased protonic-activity within the cathode catalyst layer from proton supplantation with contaminant cations. The accumulation and extent of cation exchange in the cathode catalyst layer depends on the operating current density due to migration. Simulations of cell recovery of potential suggest that a contaminated cell can recover approximately 78% (450 mV) with 24 h of constant current density operation at 2 A cm –2 , with higher current densities accelerating reduced recovery times. Parametric studies show that anode-side acidification at lower current densities inhibit cation contaminant adsorption, and cathode-side acidification at larger current densities facilitate the expulsion of adsorbed cations; for a cathode-side pH of 6 and 5, the cell can recover an additional 10% and 100% performance, respectively. Overall, the model serves as a framework for modeling other aspects of PEMWE systems to address durability and performance aspects, which can assist in improving the viability of the technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗

DOE OSTI2025

Polarization-shape alignment of IllustrisTNG star-forming galaxies

In star-forming disk galaxies, the radio continuum emission (1–10GHz) powered by star formation has an integrated polarization direction imperfectly aligned with the apparent disk minor axis. This polarization-shape alignment effect was previously observed in a small sample of local spirals. If this is prevalent for disk galaxies out to cosmological redshifts, novel measurements of cosmic birefringence and cosmic shear will be enabled by leveraging radio continuum surveys such as the Square Kilometre Array synergized with galaxy shape measurements. We calculate the polarization-shape misalignment angle for star-forming galaxies in the IllustrisTNG50 simulation at 0 < z < 2, assuming that additional polarized radio emission from an active galactic nucleus is negligible in at least a sizable fraction of the star-forming galaxies. The alignment found for z = 0 is consistent with local spiral data, but significantly deteriorates as redshift increases. Moreover, it degrades toward lower frequencies due to internal Faraday depolarization. Thanks to cosmic redshifting, observing higher-z galaxies at a fixed frequency greatly mitigates degradation due to reduced Faraday depolarization at the source-frame frequency. We present analytic fits to the non-Gaussian misalignment angle distribution, and evaluate Fisher information per galaxy for measuring a polarization rotation angle induced by cosmic birefringence. For observation at 4.8 GHz, the effective root-mean-square misalignment angle σα,eff is 18°, 23° and 33° at z = 0, 1 and 2, respectively. Analyzing N independent galaxies reduces the uncertainty on an isotropic cosmic birefringence signal to σ α,eff /√(N), providing competitive sensitivity once large samples are available. As accurate observation-driven models are not yet available for cosmological galaxy samples, our results motivate pilot observations to empirically characterize polarization-shape alignment, and can facilitate forecasts of cosmology and fundamental physics applications that exploit this effect.

axions↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2026

In-Situ Magnetic Field Reconstruction in the MAGIS-100 Experiment

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.; Ferm↗

DOE OSTI2026

Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)

Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.

Advanced Manufacturing↗

DOE OSTI2025

A Multiscale Approach to Simulate Non‐Isothermal Multiphase Flow in Deformable Porous Materials

Coupled thermal, hydraulic, and mechanical processes in porous materials play important roles in several energy and environmental technologies. The Darcy-Brinkman-Biot (DBB) framework has proven effective in modeling multiphase fluid flow in deformable porous solids across both pore and Darcy scales, including in systems where fractures coexist with a porous matrix. In this study, we extend the DBB framework, originally designed for isothermal conditions, to address non-isothermal problems by incorporating an energy conservation equation. The resulting solver, hybridBiotThermalInterFoam, enables simulations of coupled multiphase fluid flow, heat transfer, and solid deformation in hybrid-scale systems containing both solid-free regions and ductile porous domains. The new solver is validated through comparisons with analytical solutions and, also, against established heat transfer solvers chtMultiRegionFoam and compressibleInterFoam. Further, a series of 2D and 3D case studies, including two-phase heat transfer in solid-free, static, or deformable porous media, highlights the solver's capacity to simulate complex flow dynamics and heat transport in systems involving high mobility ratios, viscous fingering, and fracture propagation. Our results establish the feasibility of incorporating thermal effects in simulations of a wide variety of energy geotechnics and environmental applications, including enhanced hydrocarbon recovery, soil remediation, and enhanced geothermal energy systems.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗

DOE OSTI2025

Summary of Analytical Services for the Hanford Site Radionuclide NESHAP Program

This document is a summary of the point source analytical requirements used to demonstrate compliance for the Department of Energy (DOE) Hanford Site operations with 40 Code of Federal Regulations (CFR) Part 61, “National Emission Standards for Hazardous Air Pollutants,” (NESHAP) Subpart H, “National Emission Standards for Emissions of Radionuclides Other Than Radon From Department of Energy Facilities,” and the Washington Administrative Code (WAC) 246-247, “Radiation Protection – Air Emissions.” This reference collects information from multiple source documents and is not intended to create, supersede, replace or over-ride any existing contractual, DOE, federal or state statutes, regulations, compliance agreements, orders, permits, licenses or other requirements. The requirement source document governs where any difference may exist. The Hanford Mission Integration Solutions (HMIS) Environmental organization has been contracted by DOE to manage and report data collected from the sampling and monitoring of radioactive air emissions point sources, colloquially called stacks. The Environmental organization coordinates the analyses and reporting of samples collected at various facilities across the Hanford Site. These facilities operate approximately 52 stacks that require sampling, monitoring or estimating radioactive air emissions. The stacks are operated by Bechtel National, Inc. (BNI), Central Plateau Cleanup Company (CPCCo), Hanford Tank Waste Operations & Closure (H2C), Hanford Laboratory Management and Integration (HLMI), and Pacific Northwest National Laboratory (PNNL). Stack samples from CPCCo, HLMI and H2C facilities are collected by the operating contractor staff, delivered to HMIS, and then shipped to an offsite contracted laboratory for analyses. The field and laboratory sample data uploaded into the Sample Management and Analytical Results Tracking (SMART) database are used to calculate sample volumes and concentrations. Sample concentrations are evaluated for compliance with federal and state regulations, permits, and license requirements. The SMART database also calculates total curies released for sampled point sources and stacks. Point source effluent concentrations and releases are published annually in publicly available reports. The BNI and PNNL operate several DOE-Hanford Field Office (HFO) stacks subject to the requirements of 40 CFR 61, Subpart H and WAC 246-247. The concentrations, curies released and dose modeling evaluation for these stacks are included in the DOE-HFO annual radionuclide NESHAP report. The sample collection, analyses and emissions estimates for these stacks are outside the scope of HMIS contracted responsibilities and not addressed further in this document.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Particle-in-Cell Method for Plasmas with a Generalized Momentum Formulation, Part I: Model Formulation

Here, this paper formulates a new particle-in-cell method for the Vlasov–Maxwell system. Under the Lorenz gauge condition, Maxwell’s equations for the electromagnetic fields can be written as a collection of scalar and vector wave equations. The use of potentials for the fields motivates the adoption of a Hamiltonian formulation for particles that employs the generalized (conjugate) momentum. A notable advantage offered by the Hamiltonian formulation is the elimination of time derivatives in the Lorenz gauge formulation that are required by the standard Newton–Lorentz treatment of the particles. This allows the fields to retain the full time-accuracy guaranteed by the field solver. The resulting updates for particles require only knowledge of the fields and their spatial derivatives. An analytical method for constructing these spatial derivatives is presented that exploits the underlying integral solution used in the field solver for the wave equations. Moreover, these derivatives are demonstrated to converge at the same rate as the fields in both time and space. The Method of Lines Transpose field solver we consider in this work is globally first-order accurate in time and high-order accurate in space (e.g., fourth- and fifth-order) and belongs to a larger class of methods which are unconditionally stable, can address geometry, and leverage $\mathcal {O}(N)$ fast summation methods for efficiency. We demonstrate the method on several well-established benchmark problems on bounded domains, including a plasma sheath as well as a relativistic particle beam. The efficacy of the proposed formulation is established by comparing with a second-order accurate finite-difference time-domain method that employs a leapfrog time advance for particles and a charge conserving map suitable for bounded domains. The new method shows mesh-independent numerical heating properties even in cases where the plasma Debye length is smaller than the grid spacing. This is an important feature of the new method for problems defined on bounded domains, because it permits the use of coarser grids in space in the representation of the fields. Such a capability has significant implications for the simulation of plasmas in bounded domains with complex geometry, where the ratio between the largest and smallest cells can vary significantly. The use of high-order spatial approximations in the new method also means that fewer grid points are required in order to achieve a fixed accuracy. Our results also suggest that the new method can be used with fewer simulation particles per cell compared to the benchmark explicit method, which permits further computational savings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

Knapp, Gerald L. [Oak Ridge National Laboratory (O↗

DOE OSTI2025

A subgrid model for electron-scale turbulent transport in global ion-scale gyrokinetic simulations of tokamak plasmas

A subgrid electron–temperature–gradient (ETG) model is demonstrated here which averages local electron-scale turbulence over intermediate scales in space and time to include in global ion–temperature–gradient (ITG) simulations. This approach results in ion-scale equations, which incorporate the electron heat transport from ETG turbulence and the effects of electron-scale turbulence on the ion scale. Flux-tube ETG Cyclone Base Case simulations are performed at different radial locations and a kinetic form of the flux is added to global ion-scale (IS) simulations as a source term. Analytic radial profiles of ETG heat flux are constructed and compared to flux-tube results at multiple radial locations. Different ratios of ITG to ETG heat flux levels are considered and the results of capturing ETG heat transport in global ion-scale simulations are discussed. The potential coupling of the ETG streamer potential and intermediate-scale zonal flows to the ion scale is further addressed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A Knowledge Graph Approach to Analyze Systems and Assets Health

Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

Conductivity spectroscopy is an extremely powerful set of methods for probing the properties of optoelectronic materials, especially photovoltaics, where photoconductivity is one of the best spectroscopic proxies for performance. Despite this power, they are substantially less commonly used than time-resolved photoluminescence (for instance) because they tend to be more expensive to implement (THz) and/or require specialized knowledge (GHz) to construct instruments, which are not widely available. The goal of this review is to illustrate the utility of these experiments in the discovery and study of photovoltaic absorber materials and simultaneously make them more accessible to the community by providing a central tutorial resource. We provide a comprehensive review of how conductivity spectroscopy has developed over the past decade and been applied in the discovery and development of photovoltaic materials, with a primary focus on emerging solution-processable technologies. Along the way we aim to demystify conductivity spectroscopy with focused tutorial sections that explain the physical models used to fit the data and illustrate how to think about “high-frequency conductivity”.

14 SOLAR ENERGY↗

DOE OSTI2025

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗