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DOE OSTI2023

Complete Evaluation of ION Cost Reduction Opportunities for LWRS Pathways

The Light Water Reactor Sustainability (LWRS) Program, sponsored by the U.S. Department of Energy (DOE), plays a pivotal role in enhancing the sustainability, safety, and economic viability of the nation's fleet of nuclear power plants. This program collaborates extensively with industry, vendors, suppliers, regulatory agencies, and research and development organizations within the nuclear energy sector. The overarching goals of the LWRS program are twofold: to provide innovative science and technology-based solutions to the nuclear industry, surpassing current performance models, and to manage the aging of systems, structures, and components (SSCs) to extend the operational lifetimes of nuclear power plants safely, efficiently, and economically. The program focuses on five research and development pathways: Plant Modernization: This pathway seeks to enhance the economic viability of nuclear power plants in evolving energy markets through innovation, efficiency improvements, and digital transformation. The integration of digital technologies into plant operations is a central theme, aiming to create a seamless digital environment that improves plant economics and safety. Flexible Plant Operation and Generation: Research efforts in this pathway explore opportunities for light-water reactors (LWRs) to directly supply energy to industrial processes, diversifying revenue generation approaches and improving economic sustainability.

42 ENGINEERING↗

DOE OSTI2020

H2Fills™ [SWR-19-67]

H2FillS: Hydrogen Filling Simulation The Hydrogen Filling Simulation (H2FillS) software is a thermodynamic model designed to track and report on the transient change in hydrogen temperature, pressure, and mass flow when filling a fuel cell electric vehicle (FCEV). H2FillS simulates gas flow from the hydrogen station to the FCEV storage system. Using empirical fueling data sets, the model has been validated over a range of fueling conditions to match common light-duty FCEV fill profiles. Overall, it provides significant benefits to the light-duty fueling market and fill knowledge gaps of the interaction between a hydrogen station and an FCEV. Capabilities Use the comprehensive H2FillS model to: Safely design and operate a hydrogen fueling station Support code refinement with readily available data Develop system and operational improvements to reduce capital or operating costs at hydrogen stations or on-board FCEVs. How It Works H2FillS uses a "drag-and-drop" graphical user interface to simulate station and vehicle systems with preset parameters for common hydrogen station components as default values. When needed, users can define their own parameters to create their own station or vehicle components. Download H2FillS in Two Steps Read the terms of the end user software license agreement. Register to download the software. There are two versions of the model: A full-station model starts the simulation at high-pressure ground storage, runs through a dispenser, and ends at a vehicle storage system. A partial-station model starts at the dispenser breakaway and consists solely of the dispenser components and the vehicle storage system. H2FillS will automatically output fill performance data from the vehicle by tracking pressure and temperature throughout the fill. Users can input their own fill profiles into the model to run a variety of simulations.

Peters, Michael↗

DOE OSTI2025

Status and Performance of the GlueX DIRC

The GlueX experiment is located in experimental Hall D at Jefferson Lab (JLab) and provides a unique capability to search for hybrid mesons in high-energy photoproduction, utilizing a $\sim$9 GeV linearly polarized photon beam. Since 2020 a Detector of Internally Reflected Cherenkov light (DIRC) has been in use at GlueX, utilizing components from the decommissioned BaBar DIRC. In this contribution we will discuss the status and performance of the GlueX DIRC with the data collected to date, including recent developments in the use of machine learning for particle identification.

Stevens, Justin [College of William and Mary, Will↗

DOE OSTI2025

Self-consistent modeling of tokamak edge plasma transport with lithium sources

Magnetic confinement fusion devices require effective heat and particle exhaust solutions on the divertor plates to operate sustainably, especially under reactor-relevant conditions. Liquid lithium divertors have been proposed to address two major challenges: control of excessive heat flux to plasma-facing components through vapor shielding and minimization of core plasma contamination from impurities. The National Spherical Torus Experiment-Upgrade (NSTX-U) will explore lithium as a divertor material due to its potential to meet both objectives. We present a self-consistent coupling framework between the plasma boundary transport code UEDGE and the lithium wall transport code Wall–Li to evaluate the feasibility and operational limits of lithium-based divertors. The model aims to optimize lithium sourcing levels to prevent core plasma contamination via fuel dilution while ensuring divertor protection through vapor shielding. This integrated framework, applicable to any tokamak with lithium sources, dynamically adjusts lithium sourcing based on local plasma conditions and surface temperature. The coupled model is tested using NSTX-like geometry and plasma conditions to assess its performance and reliability. Wall–Li calculates lithium fluxes from plasma-facing components, incorporating physical sputtering, thermally enhanced sputtering, and evaporation driven by surface temperature and ion flux. These fluxes are reintroduced into UEDGE as neutral lithium atoms, enabling simulation of their transport and distribution within the plasma. UEDGE computes plasma and neutral transport, surface heat flux, and iteratively feeds this information back to Wall–Li. A small time step is employed to ensure numerical stability and convergence, enabling accurate simulations over typical tokamak discharge durations. This integrated modeling approach provides a robust tool for identifying operational regimes that balance effective lithium sourcing with minimal core plasma contamination, offering critical insights for optimizing lithium-based divertor systems in current and future fusion devices.

Magnetic confinement fusion↗

DOE OSTI2025

Enhancing Modeling and Simulation for Effective Protection Strategies

This report was created by Sandia National Laboratories (SNL) to document the principles and methodology of performance data collection and integration with modeling and simulation tools to better facilitate the performance evaluation of physical protection systems (PPS). Results and conclusions from the use of modeling and simulation tools are only as good as the data employed by the tools when conducting analysis. Acquiring the performance testing data necessary to ensure effective evaluation can be a complex and sometimes daunting process. It is the desire of the organization to provide guidance that eases the burdens associated with pursuit of these objectives. This document draws heavily upon longstanding principles of systems engineering that have been developed and employed by SNL in the discipline of security since the 1970s. The scope of this document is constrained to the testing of system components and integration of data that is applicable within the context of PPS performance analysis using two tools that have been developed by SNL, PathTrace© and Scribe3D©. For guidance related to testing and evaluation more broadly, the manuals and reports referenced by this document can be consulted.

42 ENGINEERING↗

DOE OSTI2021

Effect of Pressurization on Solid Oxide Cell Oxygen Electrodes: the Role of PrO x Nanoparticle Infiltration

Pressurization of solid oxide cells improves performance by reducing electrode polarization resistance (R P ) and facilitates system integration with balance of plant components such as pressurized storage tanks. However, there are few reports on pressurization effects for electrodes designed for low-temperature operation and utilizing infiltrated catalysts. Here we report an electrochemical impedance spectroscopy study of high performing oxygen electrode materials, SrTi 0.3 Fe 0.63 Co 0.07 O 3-∂ (STFC) and PrO x infiltrated STFC, for oxygen partial pressures ($p$ $o$ 2 ) from 0.1 to 8 atm and temperatures from 550 to 650 °C. decreases more with pressurization for STFC:PrO x , fitting well to with an exponent n~0.3, compared to n~0.25 for STFC. The combination of PrO x infiltration and pressurization yields a substantial R P decrease, e.g., at 600 °C by ~7 times from 0.36 Ω cm 2 at $p$ $o$ 2 = 0.2 atm for STFC to 0.055 Ω cm 2 at $p$ $o$ 2 = 4 atm for STFC:PrO x . Here, a transmission-line-based circuit model impedance fit reveals that the significant oxygen surface reaction (R surf ) resistance contribution decreases substantially with PrO x infiltration; and its $p$ $o$ 2 dependence become more pronounced, with n increasing from ~0.25 to ~0.5. R surf for STFC:PrO x decreases so much at elevated $p$ $o$ 2 that the electrode/electrolyte interface resistance dominates.

25 ENERGY STORAGE↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

DOE OSTI2026

Photon Detection System Calibration for DUNE

Photon Detection System Calibration for DUNE Not scheduled 20m Conference Center (University of California, Irvine) Poster New Technologies for Neutrino Physics Poster session Speaker Denis Torres (South Dakota School of Mines and Technology) Description The Deep Underground Neutrino Experiment (DUNE) is a long baseline neutrino oscillation experiment that relies on a precise Photon Detection System (PDS) to provide accurate timing information, enhance sensitivity to low-energy and non-beam events, and support detector performance studies in liquid argon time projection chambers. Achieving these goals requires a well-understood and stable optical calibration strategy that operates reliably under cryogenic conditions. In this poster, I will present PDS calibration studies performed in ProtoDUNE, focusing on the characterization of the ultraviolet (UV) light calibration system and key optical components in the light-delivery chain. I will discuss measurements of optical fiber transmission, SMA-to-SMA connector and feedthrough interface losses, and diffuser assemblies, emphasizing wavelength dependence, attenuation, and performance under cryogenic thermal cycling and stability tests. These studies provide quantitative inputs for understanding light transport, uniformity, and long-term reliability of the PDS in large-scale liquid argon detectors, and they directly inform calibration strategies for the DUNE Far Detector.

Torres Muñoz, Denis [South Dakota Sch. Mines Tech.↗

DOE OSTI2025

High-current-density electrosynthesis of formate from captured CO 2 solution by MOF-derived bismuth nanosheets

Greenhouse gas emissions present a significant challenge to humanity, and utilizing renewable electricity to convert emitted CO 2 into value-added products offers a promising solution; however, traditional CO 2 capture and regeneration processes remain energy-intensive, restricting the overall system efficiency and decarbonization efficacy. In this study, an advanced direct reduction of captured CO 2 with large current densities for formate electrosynthesis was demonstrated without the need for CO 2 regeneration or compression. The bismuth nanosheet (DRM-BiNS) was synthesized by direct reduction of a Bi-based MOF, representing a new class of catalytic materials with a large surface area and interconnected pores, suitable for the direct reduction of captured CO 2 . By seamlessly combining experimentation and simulation, insights into the structure-parameter-performance relation were acquired in a flow cell setting, including critical membrane-electrode distance, cell orientation, and pumping flow rate. Important flow-cell components, such as catholyte volume, electrode substrate, membrane choice, and ionomer type, were also carefully examined to enhance the cell performance. In sharp contrast to prior studies limited to current densities below 20 mA/cm 2 in bicarbonate-based captured CO 2 solutions, this work demonstrates a remarkable current density of 300 mA/cm 2 with an FE to formate comparable to the case with gas-fed CO 2 reduction. Moreover, the process sustained an FE above 50% at a high current density of 500 mA/cm 2 . The DRM-BiNS catalyst exhibited outstanding selectivity, activity, and stability, significantly outperforming oxide-derived bismuth nanosheets (OD-BiNS) in captured CO 2 reduction. Furthermore, these findings offer critical insights into the development of sustainable and scalable CO 2 utilization technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Multimode turbulent flow measurements using magnetic resonance imaging- and laser-based techniques and computational fluid dynamics simulations

We studied the flow field characteristics of a turbulent flow over a regularized cube array with a perpendicular injection flow through the floor between the second and third cubical elements, representing the complex flow interactions of a 3D jet and the wake flows behind cubical obstacles. Four different experimental measurements were performed: two magnetic resonance imaging-based measurements for three-dimensional three-component velocity (MRV) and concentration (MRC) and two laser-based techniques, particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF), for two-dimensional two-component velocity and concentration measurement, respectively. The mainstream Reynolds number is Re = 15 000⁠, based on the primary inlet velocity U m and channel height D H ⁠, whereas the injector Reynolds number is Re j = 3400⁠, based on the injector velocity U j and the injector's exit width D j ⁠. Numerical simulations were performed for the studied flow configuration of turbulent flow over a regularized cube array using Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) approaches. Results obtained from experimental measurements—including MRV, MRC, PIV, and PLIF—as well as RANS and LES simulations are discussed and compared along several horizontal and vertical planes of the studied configuration. In addition, 3D turbulent flow structures, such as leading-edge vortex, horseshoe vortex, and jet shear-layer vortex, and the isosurfaces of scalar concentration successfully revealed by MRV and MRC techniques were found to be in very good agreement with those 3D features extracted from RANS and LES simulations. In conclusion, the high-resolution experimental and numerical database obtained from this study could be useful for validation and verification of numerical codes.

Computational fluid dynamics↗

DOE OSTI2020

Development and Integration of Predictive Models for Manufacturing and Structural Performance of Carbon Fiber Composites in Automotive Applications (Final Report)

The goal of this project is to develop integrated, state-of-the-art, computational modeling tools based on an integrated computational materials engineering (ICME) methodology that are critically needed to enable structural carbon fiber (CF) applications in automobiles. These tools are designed to predict the manufacturing and structural performance of CF composites, including stochastic effects. During the first phase of the project, the manufacturing and structural performance tools, including a stochastic driver, were developed, calibrated, and validated against coupon and component level test results. For this project, from the manufacturing side, the development efforts were focused on high-pressure resin transfer molding (HP-RTM), a potentially game-changing manufacturing technology that is capable of reaching the 3-5-minute cycle time that is necessary for volume manufacturing of composites. On the structural performance side, the crashworthiness of CF structures was studied. Accordingly, computational tools were developed and validated. The resulting differences between the numerical predictions and the experimental results in the first phase of the project were required to be less than 15%. During the second phase of the project, the manufacturing and performance tools were integrated by mapping the manufacturing outcomes (e.g., fiber angles, residual stresses, degree of cure, and defects) into the structural models. Also, using the tools developed in this project, an automotive assembly currently manufactured in steel (2016 GM Malibu) was redesigned to be manufactured using advanced CF composites with the objective of comparing the performance of the CF assembly with the developed ICME model predictions. The weight, performance and cost of the CF assembly were compared with the baseline steel assembly as well. The CF underbody design was shown to be 30% lighter than the corresponding steel design. The CF underbody assembly withstood the side pole impact with less than half the intrusion for the steel assembly. Excellent correlations within 10% for both peak impact load and intrusion were observed between the numerical predictions and experimental results. Based on the cost models developed in the project, the cost increase per Kg saved was determined to be $22. The project team has prepared a total of 28 publications in various journals and conferences and broadly communicated the project results for the benefit of the composite industry. The project team has also prepared a total of 10 patent applications that were submitted to the US Patent and Trademark office.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Method for Coupled Electromagnetic and Circuit Simulations to Evaluate Surge Arrester Performance in Protecting Equipment Against E1 HEMP

Surge arrester behavioral modeling for realistic systems embedded in an E1 high-altitude electromagnetic pulse environment inherently encompasses three interconnected complications: (1) the need to account for signal propagation across two domains, electromagnetics and electrical; (2) the need to include both linear and nonlinear circuit components in the analysis; and (3) the need to understand that the over-current and over-voltage mitigation performance is dependent not only on the properties of the surge arrester and protected load but also on the topology of the overall electrical network. This study presents a framework to address these challenges in a systematic manner to consider the effectiveness of protective measures for a common class of equipment in power generation facilities. Full-wave simulations were carried out to derive circuit-domain (i.e., lumped element–based) equivalent models for the excitation waveform and the physical components of the system. Then, these equivalent models were imported to a circuit solver and combined with a high-frequency surge arrester model to evaluate mitigation performance. The methodology outlined is general enough such that it can be applied for other electromagnetic interference problems that involve E2/E3 HEMP or microwave emissions.

42 ENGINEERING↗

DOE OSTI2025

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Pellet cladding mechanical interaction as a potential failure mechanism during a control rod drop accident in a boiling water reactor

Boiling water reactors (BWRs) represent approximately one-third of the operating fleet in the United States, contributing significantly towards the global effort in reducing carbon emissions. Even though most of the operating fleet has been in operation for quite some time, continued advancements in new nuclear fuel (such as accident tolerant fuel) or operating regimes (such as power up-rates and higher burnup operation) necessitates similar advancements in modeling and simulation capabilities. Bison, a component of the Virtual Environment for Reactor Applications (VERA), is a high-fidelity fuel performance code able to explore the fuel performance of a wide variety of fuel types in one-, two-, and three-dimensions. Until recently, the code had not been used for analyses of BWRs. Modeling capabilities have been added for Gd-bearing UO{sub 2} and pure zirconium liners. New models have been added based upon the U.S. Nuclear Regulatory Commission (NRC) guide-lines for hydrogen pickup in Zircaloy-2 claddings and pellet-clad mechanical interaction (PCMI) failure during a reactivity insertion accident (RIA), known as a rod drop accident (CRDA) in BWRs. Implementing and/or improving Bison modeling capabilities extended its analytical reach to areas beyond its original intended purpose. This paper demonstrates one of these capabilities as a proof of concept. Recently developed models enable Bison to provide an alternate approach for cladding integrity determination in CRDA evaluations, which currently use bounding conservative estimates. As part of this demonstration, NRC guidance on hydrogen-pickup and PCMI failure models were utilized in this research. Even though more research is needed in establishing right inputs and process in this area, this paper demonstrates Bison's ability to determine cladding integrity in a CRDA evaluation. This first of a kind demonstration is a proof of concept in this area, which could potentially be extended to a number of other areas where a more accurate cladding integrity determination would be needed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor’s algorithm, weighted sum optimization problems in data processing and machine learning and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM’s superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands’ superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. Here, we analyze performance trends in our results and speculate on possible future development within this computational paradigm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A heterogeneously integrated silicon photonic/lithium niobate travelling wave electro-optic modulator

Silicon photonics is a platform that enables densely integrated photonic components and systems and integration with electronic circuits. Depletion mode modulators designed on this platform suffer from a fundamental frequency response limit due to the mobility of carriers in silicon. Lithium niobate-based modulators have demonstrated high performance, but the material is difficult to process and cannot be easily integrated with other photonic components and electronics. In this manuscript, we simultaneously take advantage of the benefits of silicon photonics and the Pockels effect in lithium niobate by heterogeneously integrating silicon photonic-integrated circuits with thin-film lithium niobate samples. We demonstrate the most CMOS-compatible thin-film lithium niobate modulator to date, which has electro-optic 3 dB bandwidths of 30.6 GHz and half-wave voltages of 6.7 V×cm. These modulators are fabricated entirely in CMOS facilities, with the exception of the bonding of a thin-film lithium niobate sample post fabrication, and require no etching of lithium niobate.

42 ENGINEERING↗

DOE OSTI2020

Thermally Anisotropic Composites for Heat Redirection and Thermal Management in Building Envelopes

In building envelope systems, thermal management is important from energy conservation and thermal comfort perspectives, and is typically done via insulation, thermal mass, solar control and shading, etc. Thermal anisotropy is commonly utilized in electronics for heat dissipation. This article investigates the feasibility of utilizing thermally anisotropic composites (TACs) to reduce unwanted heat gains and losses through the building envelope by redirecting the heat transfer to or from appropriate heat sinks and sources.Numerical simulations of building envelope components using COMSOL Multiphysics and whole building simulations using EnergyPlus (E+) were performed to estimate the energy savings potential of TACs used in external walls of residential buildings. Application of TACs was compared with baseline walls with cavity insulation as well as with and without exterior continuous insulation (CI). The COMSOL simulations indicated 75% or higher reductions in heat gains when using a TAC coupled with a heat sink compared to an exterior CI case. In terms of energy savings in a hot climate, E+ models estimated cooling energy savings of 19% with a "TAC + heat sink" compared to a baseline wall with only cavity insulation. Further, a small-scale test sample with a TAC-heat sink system was built and tested in a multi-transducer heat flow meter (HFM). The heat sink was a copper tube circulating cold water. The experiments verified the re-direction of heat to a sink and significant reduction in heat transfer through the test specimen. The experiments showed that the heat redirection only occurred in the presence of both thermal anisotropy and water circulation; if either element was missing, the system behaved like a traditional envelope component.

Biswas, Kaushik↗